sample research paper on robotics

Artificial Intelligence

Data Science
Hardware & Sensors

Machine Learning

Agriculture.

Defense & Cyber Security
Healthcare & Sports
Hospitality & Retail
Logistics & Industrial
Office & Household
Write for Us

Top AI meeting assistants for effortless recording, transcription, and summarization

Best ai tools for executive and personal assistants, limitations of ai detectors in education and better alternatives, 13 teacher-specific ai tools to enhance the teaching experience, top ai tools revolutionizing research: from idea generation to dissemination, acceptable and unacceptable uses of generative ai in academic research, common mistakes to avoid when using a demat account, warehouse robotics companies in europe transforming order fulfillment [updated], why businesses should invest in decentralized apps, a close watch: how uk businesses benefit from advanced cctv systems, empowering small businesses: the role of it support in growth and….

Technologies

500 research papers and projects in robotics – Free Download

The recent history of robotics is full of fascinating moments that accelerated the rapid technological advances in artificial intelligence , automation , engineering, energy storage, and machine learning. The result transformed the capabilities of robots and their ability to take over tasks once carried out by humans at factories, hospitals, farms, etc.

These technological advances don’t occur overnight; they require several years of research and development in solving some of the biggest engineering challenges in navigation, autonomy, AI and machine learning to build robots that are much safer and efficient in a real-world situation. A lot of universities, institutes, and companies across the world are working tirelessly in various research areas to make this reality.

In this post, we have listed 500+ recent research papers and projects for those who are interested in robotics. These free, downloadable research papers can shed lights into the some of the complex areas in robotics such as navigation, motion planning, robotic interactions, obstacle avoidance, actuators, machine learning, computer vision, artificial intelligence, collaborative robotics, nano robotics, social robotics, cloud, swan robotics, sensors, mobile robotics, humanoid, service robots, automation, autonomous, etc. Feel free to download. Share your own research papers with us to be added into this list. Also, you can ask a professional academic writer from CustomWritings – research paper writing service to assist you online on any related topic.

Navigation and Motion Planning

Robotics Navigation Using MPEG CDVS
Design, Manufacturing and Test of a High-Precision MEMS Inclination Sensor for Navigation Systems in Robot-assisted Surgery
Motion Control of a Three Active Wheeled Mobile Robot and Collision-Free Human Following Navigation in Outdoor Environment
One Point Perspective Vanishing Point Estimation for Mobile Robot Vision Based Navigation System
Application of Ant Colony Optimization for finding the Navigational path of Mobile Robot-A Review
Robot Navigation Using a Brain-Computer Interface
Path Generation for Robot Navigation using a Single Ceiling Mounted Camera
Exact Robot Navigation Using Power Diagrams
Learning Socially Normative Robot Navigation Behaviors with Bayesian Inverse Reinforcement Learning
Pipelined, High Speed, Low Power Neural Network Controller for Autonomous Mobile Robot Navigation Using FPGA
Proxemics models for human-aware navigation in robotics: Grounding interaction and personal space models in experimental data from psychology
Optimality and limit behavior of the ML estimator for Multi-Robot Localization via GPS and Relative Measurements
Aerial Robotics: Compact groups of cooperating micro aerial vehicles in clustered GPS denied environment
Disordered and Multiple Destinations Path Planning Methods for Mobile Robot in Dynamic Environment
Integrating Modeling and Knowledge Representation for Combined Task, Resource and Path Planning in Robotics
Path Planning With Kinematic Constraints For Robot Groups
Robot motion planning for pouring liquids
Implan: Scalable Incremental Motion Planning for Multi-Robot Systems
Equilibrium Motion Planning of Humanoid Climbing Robot under Constraints
POMDP-lite for Robust Robot Planning under Uncertainty
The RoboCup Logistics League as a Benchmark for Planning in Robotics
Planning-aware communication for decentralised multi- robot coordination
Combined Force and Position Controller Based on Inverse Dynamics: Application to Cooperative Robotics
A Four Degree of Freedom Robot for Positioning Ultrasound Imaging Catheters
The Role of Robotics in Ovarian Transposition
An Implementation on 3D Positioning Aquatic Robot

Robotic Interactions

On Indexicality, Direction of Arrival of Sound Sources and Human-Robot Interaction
OpenWoZ: A Runtime-Configurable Wizard-of-Oz Framework for Human-Robot Interaction
Privacy in Human-Robot Interaction: Survey and Future Work
An Analysis Of Teacher-Student Interaction Patterns In A Robotics Course For Kindergarten Children: A Pilot Study
Human Robotics Interaction (HRI) based Analysis–using DMT
A Cautionary Note on Personality (Extroversion) Assessments in Child-Robot Interaction Studies
Interaction as a bridge between cognition and robotics
State Representation Learning in Robotics: Using Prior Knowledge about Physical Interaction
Eliciting Conversation in Robot Vehicle Interactions
A Comparison of Avatar, Video, and Robot-Mediated Interaction on Users’ Trust in Expertise
Exercising with Baxter: Design and Evaluation of Assistive Social-Physical Human- Robot Interaction
Using Narrative to Enable Longitudinal Human- Robot Interactions
Computational Analysis of Affect, Personality, and Engagement in HumanRobot Interactions
Human-robot interactions: A psychological perspective
Gait of Quadruped Robot and Interaction Based on Gesture Recognition
Graphically representing child- robot interaction proxemics
Interactive Demo of the SOPHIA Project: Combining Soft Robotics and Brain-Machine Interfaces for Stroke Rehabilitation
Interactive Robotics Workshop
Activating Robotics Manipulator using Eye Movements
Wireless Controlled Robot Movement System Desgined using Microcontroller
Gesture Controlled Robot using LabVIEW
RoGuE: Robot Gesture Engine

Obstacle Avoidance

Low Cost Obstacle Avoidance Robot with Logic Gates and Gate Delay Calculations
Advanced Fuzzy Potential Field Method for Mobile Robot Obstacle Avoidance
Controlling Obstacle Avoiding And Live Streaming Robot Using Chronos Watch
Movement Of The Space Robot Manipulator In Environment With Obstacles
Assis-Cicerone Robot With Visual Obstacle Avoidance Using a Stack of Odometric Data.
Obstacle detection and avoidance methods for autonomous mobile robot
Moving Domestic Robotics Control Method Based on Creating and Sharing Maps with Shortest Path Findings and Obstacle Avoidance
Control of the Differentially-driven Mobile Robot in the Environment with a Non-Convex Star-Shape Obstacle: Simulation and Experiments
A survey of typical machine learning based motion planning algorithms for robotics
Linear Algebra for Computer Vision, Robotics , and Machine Learning
Applying Radical Constructivism to Machine Learning: A Pilot Study in Assistive Robotics
Machine Learning for Robotics and Computer Vision: Sampling methods and Variational Inference
Rule-Based Supervisor and Checker of Deep Learning Perception Modules in Cognitive Robotics
The Limits and Potentials of Deep Learning for Robotics
Autonomous Robotics and Deep Learning
A Unified Knowledge Representation System for Robot Learning and Dialogue

Computer Vision

Computer Vision Based Chess Playing Capabilities for the Baxter Humanoid Robot
Non-Euclidean manifolds in robotics and computer vision: why should we care?
Topology of singular surfaces, applications to visualization and robotics
On the Impact of Learning Hierarchical Representations for Visual Recognition in Robotics
Focused Online Visual-Motor Coordination for a Dual-Arm Robot Manipulator
Towards Practical Visual Servoing in Robotics
Visual Pattern Recognition In Robotics
Automated Visual Inspection: Position Identification of Object for Industrial Robot Application based on Color and Shape
Automated Creation of Augmented Reality Visualizations for Autonomous Robot Systems
Implementation of Efficient Night Vision Robot on Arduino and FPGA Board
On the Relationship between Robotics and Artificial Intelligence
Artificial Spatial Cognition for Robotics and Mobile Systems: Brief Survey and Current Open Challenges
Artificial Intelligence, Robotics and Its Impact on Society
The Effects of Artificial Intelligence and Robotics on Business and Employment: Evidence from a survey on Japanese firms
Artificially Intelligent Maze Solver Robot
Artificial intelligence, Cognitive Robotics and Human Psychology
Minecraft as an Experimental World for AI in Robotics
Impact of Robotics, RPA and AI on the insurance industry: challenges and opportunities

Probabilistic Programming

On the use of probabilistic relational affordance models for sequential manipulation tasks inrobotics
Exploration strategies in developmental robotics: a unified probabilistic framework
Probabilistic Programming for Robotics
New design of a soft-robotics wearable elbow exoskeleton based on Shape Memory Alloy wires actuators
Design of a Modular Series Elastic Upgrade to a Robotics Actuator
Applications of Compliant Actuators to Wearing Robotics for Lower Extremity
Review of Development Stages in the Conceptual Design of an Electro-Hydrostatic Actuator for Robotics
Fluid electrodes for submersible robotics based on dielectric elastomer actuators
Cascaded Control Of Compliant Actuators In Friendly Robotics

Collaborative Robotics

Interpretable Models for Fast Activity Recognition and Anomaly Explanation During Collaborative Robotics Tasks
Collaborative Work Management Using SWARM Robotics
Collaborative Robotics : Assessment of Safety Functions and Feedback from Workers, Users and Integrators in Quebec
Accessibility, Making and Tactile Robotics : Facilitating Collaborative Learning and Computational Thinking for Learners with Visual Impairments
Trajectory Adaptation of Robot Arms for Head-pose Dependent Assistive Tasks

Mobile Robotics

Experimental research of proximity sensors for application in mobile robotics in greenhouse environment.
Multispectral Texture Mapping for Telepresence and Autonomous Mobile Robotics
A Smart Mobile Robot to Detect Abnormalities in Hazardous Zones
Simulation of nonlinear filter based localization for indoor mobile robot
Integrating control science in a practical mobile robotics course
Experimental Study of the Performance of the Kinect Range Camera for Mobile Robotics
Planification of an Optimal Path for a Mobile Robot Using Neural Networks
Security of Networking Control System in Mobile Robotics (NCSMR)
Vector Maps in Mobile Robotics
An Embedded System for a Bluetooth Controlled Mobile Robot Based on the ATmega8535 Microcontroller
Experiments of NDT-Based Localization for a Mobile Robot Moving Near Buildings
Hardware and Software Co-design for the EKF Applied to the Mobile Robotics Localization Problem
Design of a SESLogo Program for Mobile Robot Control
An Improved Ekf-Slam Algorithm For Mobile Robot
Intelligent Vehicles at the Mobile Robotics Laboratory, University of Sao Paolo, Brazil [ITS Research Lab]
Introduction to Mobile Robotics
Miniature Piezoelectric Mobile Robot driven by Standing Wave
Mobile Robot Floor Classification using Motor Current and Accelerometer Measurements
Sensors for Robotics 2015
An Automated Sensing System for Steel Bridge Inspection Using GMR Sensor Array and Magnetic Wheels of Climbing Robot
Sensors for Next-Generation Robotics
Multi-Robot Sensor Relocation To Enhance Connectivity In A WSN
Automated Irrigation System Using Robotics and Sensors
Design Of Control System For Articulated Robot Using Leap Motion Sensor
Automated configuration of vision sensor systems for industrial robotics

Nano robotics

Light Robotics: an all-optical nano-and micro-toolbox
Light-driven Nano- robotics
Light-driven Nano-robotics
Light Robotics: a new tech–nology and its applications
Light Robotics: Aiming towards all-optical nano-robotics
NanoBiophotonics Appli–cations of Light Robotics
System Level Analysis for a Locomotive Inspection Robot with Integrated Microsystems
High-Dimensional Robotics at the Nanoscale Kino-Geometric Modeling of Proteins and Molecular Mechanisms
A Study Of Insect Brain Using Robotics And Neural Networks

Social Robotics

Integrative Social Robotics Hands-On
ProCRob Architecture for Personalized Social Robotics
Definitions and Metrics for Social Robotics, along with some Experience Gained in this Domain
Transmedia Choreography: Integrating Multimodal Video Annotation in the Creative Process of a Social Robotics Performance Piece
Co-designing with children: An approach to social robot design
Toward Social Cognition in Robotics: Extracting and Internalizing Meaning from Perception
Human Centered Robotics : Designing Valuable Experiences for Social Robots
Preliminary system and hardware design for Quori, a low-cost, modular, socially interactive robot
Socially assistive robotics: Human augmentation versus automation
Tega: A Social Robot

Humanoid robot

Compliance Control and Human-Robot Interaction – International Journal of Humanoid Robotics
The Design of Humanoid Robot Using C# Interface on Bluetooth Communication
An Integrated System to approach the Programming of Humanoid Robotics
Humanoid Robot Slope Gait Planning Based on Zero Moment Point Principle
Literature Review Real-Time Vision-Based Learning for Human-Robot Interaction in Social Humanoid Robotics
The Roasted Tomato Challenge for a Humanoid Robot
Remotely teleoperating a humanoid robot to perform fine motor tasks with virtual reality

Cloud Robotics

CR3A: Cloud Robotics Algorithms Allocation Analysis
Cloud Computing and Robotics for Disaster Management
ABHIKAHA: Aerial Collision Avoidance in Quadcopter using Cloud Robotics
The Evolution Of Cloud Robotics: A Survey
Sliding Autonomy in Cloud Robotics Services for Smart City Applications
CORE: A Cloud-based Object Recognition Engine for Robotics
A Software Product Line Approach for Configuring Cloud Robotics Applications
Cloud robotics and automation: A survey of related work
ROCHAS: Robotics and Cloud-assisted Healthcare System for Empty Nester

Swarm Robotics

Evolution of Task Partitioning in Swarm Robotics
GESwarm: Grammatical Evolution for the Automatic Synthesis of Collective Behaviors in Swarm Robotics
A Concise Chronological Reassess Of Different Swarm Intelligence Methods With Multi Robotics Approach
The Swarm/Potential Model: Modeling Robotics Swarms with Measure-valued Recursions Associated to Random Finite Sets
The TAM: ABSTRACTing complex tasks in swarm robotics research
Task Allocation in Foraging Robot Swarms: The Role of Information Sharing
Robotics on the Battlefield Part II
Implementation Of Load Sharing Using Swarm Robotics
An Investigation of Environmental Influence on the Benefits of Adaptation Mechanisms in Evolutionary Swarm Robotics

Soft Robotics

Soft Robotics: The Next Generation of Intelligent Machines
Soft Robotics: Transferring Theory to Application,” Soft Components for Soft Robots”
Advances in Soft Computing, Intelligent Robotics and Control
The BRICS Component Model: A Model-Based Development Paradigm For ComplexRobotics Software Systems
Soft Mechatronics for Human-Friendly Robotics
Seminar Soft-Robotics
Special Issue on Open Source Software-Supported Robotics Research.
Soft Brain-Machine Interfaces for Assistive Robotics: A Novel Control Approach
Towards A Robot Hardware ABSTRACT ion Layer (R-HAL) Leveraging the XBot Software Framework

Service Robotics

Fundamental Theories and Practice in Service Robotics
Natural Language Processing in Domestic Service Robotics
Localization and Mapping for Service Robotics Applications
Designing of Service Robot for Home Automation-Implementation
Benchmarking Speech Understanding in Service Robotics
The Cognitive Service Robotics Apartment
Planning with Task-oriented Knowledge Acquisition for A Service Robot
Cognitive Robotics
Meta-Morphogenesis theory as background to Cognitive Robotics and Developmental Cognitive Science
Experience-based Learning for Bayesian Cognitive Robotics
Weakly supervised strategies for natural object recognition in robotics
Robotics-Derived Requirements for the Internet of Things in the 5G Context
A Comparison of Modern Synthetic Character Design and Cognitive Robotics Architecture with the Human Nervous System
PREGO: An Action Language for Belief-Based Cognitive Robotics in Continuous Domains
The Role of Intention in Cognitive Robotics
On Cognitive Learning Methodologies for Cognitive Robotics
Relational Enhancement: A Framework for Evaluating and Designing Human-RobotRelationships
A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering
Spatial Cognition in Robotics
IOT Based Gesture Movement Recognize Robot
Deliberative Systems for Autonomous Robotics: A Brief Comparison Between Action-oriented and Timelines-based Approaches
Formal Modeling and Verification of Dynamic Reconfiguration of Autonomous RoboticsSystems
Robotics on its feet: Autonomous Climbing Robots
Implementation of Autonomous Metal Detection Robot with Image and Message Transmission using Cell Phone
Toward autonomous architecture: The convergence of digital design, robotics, and the built environment
Advances in Robotics Automation
Data-centered Dependencies and Opportunities for Robotics Process Automation in Banking
On the Combination of Gamification and Crowd Computation in Industrial Automation and Robotics Applications
Advances in RoboticsAutomation
Meshworm With Segment-Bending Anchoring for Colonoscopy. IEEE ROBOTICS AND AUTOMATION LETTERS. 2 (3) pp: 1718-1724.
Recent Advances in Robotics and Automation
Key Elements Towards Automation and Robotics in Industrialised Building System (IBS)
Knowledge Building, Innovation Networks, and Robotics in Math Education
The potential of a robotics summer course On Engineering Education
Robotics as an Educational Tool: Impact of Lego Mindstorms
Effective Planning Strategy in Robotics Education: An Embodied Approach
An innovative approach to School-Work turnover programme with Educational Robotics
The importance of educational robotics as a precursor of Computational Thinking in early childhood education
Pedagogical Robotics A way to Experiment and Innovate in Educational Teaching in Morocco
Learning by Making and Early School Leaving: an Experience with Educational Robotics
Robotics and Coding: Fostering Student Engagement
Computational Thinking with Educational Robotics
New Trends In Education Of Robotics
Educational robotics as an instrument of formation: a public elementary school case study
Developmental Situation and Strategy for Engineering Robot Education in China University
Towards the Humanoid Robot Butler
YAGI-An Easy and Light-Weighted Action-Programming Language for Education and Research in Artificial Intelligence and Robotics
Simultaneous Tracking and Reconstruction (STAR) of Objects and its Application in Educational Robotics Laboratories
The importance and purpose of simulation in robotics
An Educational Tool to Support Introductory Robotics Courses
Lollybot: Where Candy, Gaming, and Educational Robotics Collide
Assessing the Impact of an Autonomous Robotics Competition for STEM Education
Educational robotics for promoting 21st century skills
New Era for Educational Robotics: Replacing Teachers with a Robotic System to Teach Alphabet Writing
Robotics as a Learning Tool for Educational Transformation
The Herd of Educational Robotic Devices (HERD): Promoting Cooperation in RoboticsEducation
Robotics in physics education: fostering graphing abilities in kinematics
Enabling Rapid Prototyping in K-12 Engineering Education with BotSpeak, a UniversalRobotics Programming Language
Innovating in robotics education with Gazebo simulator and JdeRobot framework
How to Support Students’ Computational Thinking Skills in Educational Robotics Activities
Educational Robotics At Lower Secondary School
Evaluating the impact of robotics in education on pupils’ skills and attitudes
Imagining, Playing, and Coding with KIBO: Using Robotics to Foster Computational Thinking in Young Children
How Does a First LEGO League Robotics Program Provide Opportunities for Teaching Children 21st Century Skills
A Software-Based Robotic Vision Simulator For Use In Teaching Introductory Robotics Courses
Robotics Practical
A project-based strategy for teaching robotics using NI’s embedded-FPGA platform
Teaching a Core CS Concept through Robotics
Ms. Robot Will Be Teaching You: Robot Lecturers in Four Modes of Automated Remote Instruction
Robotic Competitions: Teaching Robotics and Real-Time Programming with LEGO Mindstorms
Visegrad Robotics Workshop-different ideas to teach and popularize robotics
LEGO® Mindstorms® EV3 Robotics Instructor Guide
DRAFT: for Automaatiop iv t22 MOKASIT: Multi Camera System for Robotics Monitoring and Teaching
MOKASIT: Multi Camera System for Robotics Monitoring and Teaching
Autonomous Robot Design and Build: Novel Hands-on Experience for Undergraduate Students
Semi-Autonomous Inspection Robot
Sumo Robot Competition
Engagement of students with Robotics-Competitions-like projects in a PBL Bsc Engineering course
Robo Camp K12 Inclusive Outreach Program: A three-step model of Effective Introducing Middle School Students to Computer Programming and Robotics
The Effectiveness of Robotics Competitions on Students’ Learning of Computer Science
Engaging with Mathematics: How mathematical art, robotics and other activities are used to engage students with university mathematics and promote
Design Elements of a Mobile Robotics Course Based on Student Feedback
Sixth-Grade Students’ Motivation and Development of Proportional Reasoning Skills While Completing Robotics Challenges
Student Learning of Computational Thinking in A Robotics Curriculum: Transferrable Skills and Relevant Factors
A Robotics-Focused Instructional Framework for Design-Based Research in Middle School Classrooms
Transforming a Middle and High School Robotics Curriculum
Geometric Algebra for Applications in Cybernetics: Image Processing, Neural Networks, Robotics and Integral Transforms
Experimenting and validating didactical activities in the third year of primary school enhanced by robotics technology

Construction

Bibliometric analysis on the status quo of robotics in construction
AtomMap: A Probabilistic Amorphous 3D Map Representation for Robotics and Surface Reconstruction
Robotic Design and Construction Culture: Ethnography in Osaka University’s Miyazaki Robotics Lab
Infrastructure Robotics: A Technology Enabler for Lunar In-Situ Resource Utilization, Habitat Construction and Maintenance
A Planar Robot Design And Construction With Maple
Robotics and Automations in Construction: Advanced Construction and FutureTechnology
Why robotics in mining
Examining Influences on the Evolution of Design Ideas in a First-Year Robotics Project
Mining Robotics
TIRAMISU: Technical survey, close-in-detection and disposal mine actions in Humanitarian Demining: challenges for Robotics Systems
Robotics for Sustainable Agriculture in Aquaponics
Design and Fabrication of Crop Analysis Agriculture Robot
Enhance Multi-Disciplinary Experience for Agriculture and Engineering Students with Agriculture Robotics Project
Work in progress: Robotics mapping of landmine and UXO contaminated areas
Robot Based Wireless Monitoring and Safety System for Underground Coal Mines using Zigbee Protocol: A Review
Minesweepers uses robotics’ awesomeness to raise awareness about landminesexplosive remnants of war
Intelligent Autonomous Farming Robot with Plant Disease Detection using Image Processing
Auotomatic Pick And Place Robot
Video Prompting to Teach Robotics and Coding to Students with Autism Spectrum Disorder
Bilateral Anesthesia Mumps After RobotAssisted Hysterectomy Under General Anesthesia: Two Case Reports
Future Prospects of Artificial Intelligence in Robotics Software, A healthcare Perspective
Designing new mechanism in surgical robotics
Open-Source Research Platforms and System Integration in Modern Surgical Robotics
Soft Tissue Robotics–The Next Generation
CORVUS Full-Body Surgical Robotics Research Platform
OP: Sense, a rapid prototyping research platform for surgical robotics
Preoperative Planning Simulator with Haptic Feedback for Raven-II Surgical Robotics Platform
Origins of Surgical Robotics: From Space to the Operating Room
Accelerometer Based Wireless Gesture Controlled Robot for Medical Assistance using Arduino Lilypad
The preliminary results of a force feedback control for Sensorized Medical Robotics
Medical robotics Regulatory, ethical, and legal considerations for increasing levels of autonomy
Robotics in General Surgery
Evolution Of Minimally Invasive Surgery: Conventional Laparoscopy Torobotics
Robust trocar detection and localization during robot-assisted endoscopic surgery
How can we improve the Training of Laparoscopic Surgery thanks to the Knowledge in Robotics
Discussion on robot-assisted laparoscopic cystectomy and Ileal neobladder surgery preoperative care
Robotics in Neurosurgery: Evolution, Current Challenges, and Compromises
Hybrid Rendering Architecture for Realtime and Photorealistic Simulation of Robot-Assisted Surgery
Robotics, Image Guidance, and Computer-Assisted Surgery in Otology/Neurotology
Neuro-robotics model of visual delusions
Neuro-Robotics
Robotics in the Rehabilitation of Neurological Conditions
What if a Robot Could Help Me Care for My Parents
A Robot to Provide Support in Stigmatizing Patient-Caregiver Relationships
A New Skeleton Model and the Motion Rhythm Analysis for Human Shoulder Complex Oriented to Rehabilitation Robotics
Towards Rehabilitation Robotics: Off-The-Shelf BCI Control of Anthropomorphic Robotic Arms
Rehabilitation Robotics 2013
Combined Estimation of Friction and Patient Activity in Rehabilitation Robotics
Brain, Mind and Body: Motion Behaviour Planning, Learning and Control in view of Rehabilitation and Robotics
Reliable Robotics – Diagnostics
Robotics for Successful Ageing
Upper Extremity Robotics Exoskeleton: Application, Structure And Actuation

Defence and Military

Voice Guided Military Robot for Defence Application
Design and Control of Defense Robot Based On Virtual Reality
AI, Robotics and Cyber: How Much will They Change Warfare
BORDER SECURITY ROBOT
Brain Controlled Robot for Indian Armed Force
Autonomous Military Robotics
Wireless Restrained Military Discoursed Robot
Bomb Detection And Defusion In Planes By Application Of Robotics
Impacts Of The Robotics Age On Naval Force Design, Effectiveness, And Acquisition

Space Robotics

Lego robotics teacher professional learning
New Planar Air-bearing Microgravity Simulator for Verification of Space Robotics Numerical Simulations and Control Algorithms
The Artemis Rover as an Example for Model Based Engineering in Space Robotics
Rearrangement planning using object-centric and robot-centric action spaces
Model-based Apprenticeship Learning for Robotics in High-dimensional Spaces
Emergent Roles, Collaboration and Computational Thinking in the Multi-Dimensional Problem Space of Robotics
Reaction Null Space of a multibody system with applications in robotics

Other Industries

Robotics in clothes manufacture
Recent Trends in Robotics and Computer Integrated Manufacturing: An Overview
Application Of Robotics In Dairy And Food Industries: A Review
Architecture for theatre robotics
Human-multi-robot team collaboration for efficent warehouse operation
A Robot-based Application for Physical Exercise Training
Application Of Robotics In Oil And Gas Refineries
Implementation of Robotics in Transmission Line Monitoring
Intelligent Wireless Fire Extinguishing Robot
Monitoring and Controlling of Fire Fighthing Robot using IOT
Robotics An Emerging Technology in Dairy Industry
Robotics and Law: A Survey
Increasing ECE Student Excitement through an International Marine Robotics Competition
Application of Swarm Robotics Systems to Marine Environmental Monitoring

Future of Robotics / Trends

The future of Robotics Technology
RoboticsAutomation Are Killing Jobs A Roadmap for the Future is Needed
The next big thing (s) in robotics
Robotics in Indian Industry-Future Trends
The Future of Robot Rescue Simulation Workshop
PreprintQuantum Robotics: Primer on Current Science and Future Perspectives
Emergent Trends in Robotics and Intelligent Systems

Recent Publications

Some of our teams.

We're always looking for more talented, passionate people.

IEEE Account

Change Username/Password
Update Address

Purchase Details

Payment Options
Order History
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical Interests
US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support
About IEEE Xplore
Accessibility
Terms of Use
Nondiscrimination Policy
Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Research article
Open access
Published: 18 January 2021

Exploring the impact of Artificial Intelligence and robots on higher education through literature-based design fictions

A. M. Cox ORCID: orcid.org/0000-0002-2587-245X 1

International Journal of Educational Technology in Higher Education volume 18 , Article number: 3 ( 2021 ) Cite this article

80k Accesses

62 Citations

30 Altmetric

Metrics details

Artificial Intelligence (AI) and robotics are likely to have a significant long-term impact on higher education (HE). The scope of this impact is hard to grasp partly because the literature is siloed, as well as the changing meaning of the concepts themselves. But developments are surrounded by controversies in terms of what is technically possible, what is practical to implement and what is desirable, pedagogically or for the good of society. Design fictions that vividly imagine future scenarios of AI or robotics in use offer a means both to explain and query the technological possibilities. The paper describes the use of a wide-ranging narrative literature review to develop eight such design fictions that capture the range of potential use of AI and robots in learning, administration and research. They prompt wider discussion by instantiating such issues as how they might enable teaching of high order skills or change staff roles, as well as exploring the impact on human agency and the nature of datafication.

Introduction

The potential of Artificial Intelligence (AI) and robots to reshape our future has attracted vast interest among the public, government and academia in the last few years. As in every other sector of life, higher education (HE) will be affected, perhaps in a profound way (Bates et al., 2020 ; DeMartini and Benussi, 2017 ). HE will have to adapt to educate people to operate in a new economy and potentially for a different way of life. AI and robotics are also likely to change how education itself works, altering what learning is like, the role of teachers and researchers, and how universities work as institutions.

However, the potential changes in HE are hard to grasp for a number of reasons. One reason is that impact is, as Clay ( 2018 ) puts it, “wide and deep” yet the research literature discussing it is siloed. AI and robotics for education are separate literatures, for example. AI for education, learning analytics (LA) and educational data mining also remain somewhat separate fields. Applications to HE research as opposed to learning, such as the robot scientist concept or text and data mining (TDM), are also usually discussed separately. Thus if we wish to grasp the potential impact of AI and robots on HE holistically we need to extend our vision across the breadth of these diverse literatures.

A further reason why the potential implications of AI and robots for HE are quite hard to grasp is because rather than a single technology, something like AI is an idea or aspiration for how computers could participate in human decision making. Faith in how to do this has shifted across different technologies over time; as have concepts of learning (Roll and Wylie, 2016 ). Also, because AI and robotics are ideas that have been pursued over many decades there are some quite mature applications: impacts have already happened. Equally there are potential applications that are being developed and many only just beginning to be imagined. So, confusingly from a temporal perspective, uses of AI and robots in HE are past, present and future.

Although hard to fully grasp, it is important that a wider understanding and debate is achieved, because AI and robotics pose a range of pedagogic, practical, ethical and social justice challenges. A large body of educational literature explores the challenges of implementing new technologies in the classroom as a change management issue (e.g. as synthesised by Reid, 2014 ). Introducing AI and robots will not be a smooth process without its challenges and ironies. There is also a strong tradition in the educational literature of critical responses to technology in HE. These typically focus on issues such as the potential of technology to dehumanise the learning experience. They are often driven by fear of commercialisation or neo-liberal ideologies wrapped up in technology. Similar arguments are developing around AI and robotics. There is a particularly strong concentration of critique around the datafication of HE. Thus the questions around the use of AI and robots are as much about what we should do as what is possible (Selwyn, 2019a ). Yet according to a recent literature review most current research about AI in learning is from computer science and seems to neglect both pedagogy and ethics (Zawacki-Richter et al., 2019 ). Research on AIEd has also been recognised to have a WEIRD (western, educated, industrialized, rich and democratic) bias for some time (Blanchard, 2015 ).

One device to make the use of AI and robots more graspable is fiction, with its ability to help us imagine alternative worlds. Science fiction has already had a powerful influence on creating collective imaginaries of technology and so in shaping the future (Dourish and Bell, 2014 ). Science fiction has had a fascination with AI and robots, presumably because they enhance or replace defining human attributes: the mind and the body. To harness the power of fiction for the critical imagination, a growing body of work within Human Computer Interaction (HCI) studies adopts the use of speculative or critical narratives to destabilise assumptions through “design fictions” (Blythe 2017 ): “a conflation of design, science fact, and science fiction” (Bleecker, 2009 : 6). They can be used to pose critical questions about the impact of technology on society and to actively engage wider publics in how technology is designed. This is a promising route for making the impact of AI and robotics on HE easier to grasp. In this context, the purpose of this paper is to describe the development of a collection of design fictions to widen the debate about the potential impact of AI and robots on HE, based on a wide-ranging narrative literature review. First, the paper will explain more fully the design fiction method.

Method: design fictions

There are many types of fictions that are used for our thinking about the future. In strategic planning and in future studies, scenarios—essentially fictional narratives—are used to encapsulate contrasting possible futures (Amer et al., 2013 ; Inayatullah, 2008 ). These are then used collaboratively by stakeholders to make choices about preferred directions. On a more practical level, in designing information systems traditional design scenarios are short narratives that picture use of a planned system and that are employed to explain how it could be used to solve existing problems. As Carroll ( 1999 ) argues, such scenarios are also essentially stories or fictions and this reflects the fact that system design is inherently a creative process (Blythe, 2017 ). They are often used to involve stakeholders in systems design. The benefit is that the fictional scenario prompts reflection outside the constraints of trying to produce something that simply works (Carroll, 1999 ). But they tend to represent a system being used entirely as intended (Nathan et al., 2007 ). They typically only include immediate stakeholders and immediate contexts of use, rather than thinking about the wider societal impacts of pervasive use of the technology. A growing body of work in the study of HCI refashions these narratives:

Design fiction is about creative provocation, raising questions, innovation, and exploration. (Bleecker, 2009 : 7).

Design fictions create a speculative space in which to raise questions about whether a particular technology is desirable, the socio-cultural assumptions built into technologies, the potential for different technologies to make different worlds, our relation to technology in general, and indeed our role in making the future happen.

Design fictions exist on a spectrum between speculative and critical. Speculative fictions are exploratory. More radical, critical fictions ask fundamental questions about the organisation of society and are rooted in traditions of critical design (Dunne and Raby, 2001 ). By definition they challenge technical solutionism: the way that technologies seem to be built to solve a problem that does not necessarily exist or ignore the contextual issues that might impact its success (Blythe et al., 2016 ).

Design fictions can be used in research in a number of ways, where:

Fictions are the output themselves, as in this paper.

Fictions (or an artefact such as a video based on them) are used to elicit research data, e.g. through interviews or focus groups Lyckvi et al. ( 2018 ).

Fictions are co-created with the public as part of a process of raising awareness (e.g. Tsekleves et al. 2017 ).

For a study of the potential impact of AI and robots on HE, design fictions are a particularly suitable method. They are already used by some authors working in the field such as Pinkwart ( 2016 ), Luckin and Holmes ( 2017 ) and Selwyn et al. ( 2020 ). As a research tool, design fictions can encapsulate key issues in a short, accessible form. Critically, they have the potential to change the scope of the debate, by shifting attention away from the existing literature and its focus on developing and testing specific AI applications (Zawacki-Richter et al., 2019 ) to weighing up more or less desirable directions of travel for society. They can be used to pose critical questions that are not being asked by developers because of the WEIRD bias in the research community itself (Blanchard, 2015 ), to shift focus onto ethical and social justice issues, and also raise doubts based on practical obstacles to their widespread adoption. Fictions engage readers imaginatively and on an affective level. Furthermore, because they are explicitly fictions readers can challenge their assumptions, even get involved in actively rewriting them.

Design fictions are often individual texts. But collections of fictions create potential for reading against each other, further prompting thoughts about alternative futures. In a similar way, in future studies, scenarios are often generated around four or more alternatives, each premised on different assumptions (Inayatullah, 2008 ). This avoids the tendency towards a utopian/ dystopian dualism found in some use of fiction (Rummel et al., 2016 ; Pinkwart 2016 ). Thus in this study the aim was to produce a collection of contrasting fictions that surface the range of debates revolving around the application of AI and robotics to HE.

The process of producing fictions is not easy to render transparent.

In this study the foundation for the fictions was a wide-ranging narrative review of the literature (Templier and Paré, 2015 ). The purpose of the review was to generate a picture of the pedagogic, social, ethical and implementation issues raised by the latest trends in the application of AI and robots to teaching, research and administrative functions in HE, as a foundation for narratives which could instantiate the issues in a fictional form. We know from previous systematic reviews that these type of issue are neglected at least in the literature on AIEds (Zawacki-Richter et al., 2019 ). So the chief novelty of the review lay in (a) focusing on social, ethical, pedagogic and management implications (b) encompassing both AI and robotics as related aspects of automation and (c) seeking to be inclusive across the full range of functions of HE, including impacts on learning, but also on research and scholarly communications, as well as administrative functions, and estates management (smart campus).

In order to gather references for the review, systematic searches on the ERIC database for relevant terms such as “AI or Artificial Intelligence”; “conversational agent”, “AIED” were conducted. Selection was made for items which either primarily addressed non-technical issues or which themselves contained substantial literature reviews that could be used to gain a picture of the most recent applications. This systematic search was combined with snowballing (also known as pearl growing techniques) using references by and to highly relevant matches to find other relevant material. While typically underreported in systematic reviews this method has been shown to be highly effective in retrieving more relevant items (Badampudi et al. 2015 ). Some grey literature was included because there are a large number of reports by governmental organisations summarizing the social implications of AI and robots. Because many issues relating to datafication are foreshadowed in the literature on learning analytics, this topic was also included. In addition, some general literature on AI and robots, while not directly referencing education, was deemed to be relevant, particularly as it was recognised that education might be a late adopter and so impacts would be felt through wider social changes rather than directly through educational applications. Literature reviews which suggested trends in current technologies were included but items which were detailed reports of the development of technologies were excluded. Items prior to 2016 tended also to be excluded, because the concern was with the latest wave of AI and robots. As a result of these searches in the order of 500 items were consulted, with around 200 items deemed to be of high relevance. As such there is no claim that this was an “exhaustive” review, rather it should be seen as complimenting existing systematic reviews by serving a different purpose. The review also successfully identified a number of existing fictions in the literature that could then be rewritten to fit the needs of the study, such as to apply to HE, to make them more concise or add new elements (fictions 1, 3, 4).

As an imaginative act, writing fictions is not reducible to a completely transparent method, although some aspects can be described (Lyckvi et al., 2018 ). Some techniques to create effective critical designs are suggested by Auger ( 2013 ) such as placing something uncanny or unexpected against the backdrop of mundane normality and a sense of verisimilitude (perhaps achieved through mixing fact and fiction). Fiction 6, for example, exploits the mundane feel of committee meeting minutes to help us imagine the debates that would occur among university leaders implementing AI. A common strategy is to take the implications of a central counterfactual premise to its logical conclusion: asking: “what if?” For example, fiction 7 extends existing strategies of gathering data and using chatbots to act on them to its logical extension as a comprehensive system of data surveillance. Another technique used here was to exploit certain genres of writing such as in fiction 6 where using a style of writing from marketing and PR remind us of the role of EdTech companies in producing AI and robots.

Table 1 offers a summary of the eight fictions produced through this process. The fictions explore the potential of AI and robots in different areas of university activity, in learning, administration and research (Table 1 column 5). They seek to represent some different types of technology (column 2). Some are rather futuristic, most seem feasible today, or in the very near future (column 3). The full text of the fictions and supporting material can be downloaded from the University of Sheffield data repository, ORDA, and used under a cc-by-sa licence ( https://doi.org/10.35542/osf.io/s2jc8 ). The following sections describe each fiction in turn, showing how it relates to the literature and surfaces relevant issues. Table 2 below will summarise the issues raised.

In the following sections each of the eight fictions is described, set in the context of the literature review material that shaped their construction.

AI and robots in learning: Fiction 1, “AIDan, the teaching assistant”

Much of the literature around AI in learning focuses on tools that directly teach students (Baker and Smith, 2019 ; Holmes et al., 2019 ; Zawacki-Richter et al., 2019 ). This includes classes of systems such as:

Intelligent tutoring systems (ITS) which teach course content step by step, taking an approach personalised to the individual. Holmes et al. ( 2019 ) differentiate different types of Intelligent Tutoring Systems, based on whether they adopt a linear, dialogic or more exploratory model.

One emerging area of adaptivity is using sensors to detect the emotional and physical state of the learner, recognising the embodied and affective aspects of learning (Luckin, et al., 2016 ); a further link is being made to how virtual and augmented reality can be used to make the experience more engaging and authentic (Holmes et al., 2019 ).

Automatic writing evaluation (AWE) which are tools to assess and offer feedback on writing style (rather than content) such as learnandwrite, Grammarly and Turnitin’s Revision Assistant (Strobl, et al. 2019 ; Hussein et al., 2019 ; Hockly, 2019 ).

Conversational agents (also known as Chatbots or virtual assistants) which are AI tools designed to converse with humans (Winkler and Sӧllner, 2018 ).

The adaptive pedagogical agent, which is an “anthropomorphic virtual character used in an online learning environment to serve instructional purposes” (Martha and Santoso, 2017 ).

Many of these technologies are rather mature, such as AWE and ITS. However, there are also a wide range of different type of systems within each category, e.g. conversational agents can be designed for short or long term interaction, and could act as tutors, engage in language practice, answer questions, promote reflection or act as co-learners. They could be based on text or verbal interaction (Følstad et al., 2019 ; Wellnhammer et al., 2020 ).

Much of such literature reflects the development of AI technologies and their evaluation compared to other forms of teaching. However, according to a recent review it is primarily written by computer scientists mostly from a technical point of view with relatively little connection to pedagogy or ethics (Zawacki-Richter et al., 2019 ). In contrast some authors such as Luckin and Holmes, seek to move beyond the rather narrow development of tools and their evaluation, to envisioning how AI can address the grand challenges of learning in the twenty-first century (Luckin, et al. 2016 ; Holmes et al., 2019 ; Woolf et al., 2013 ). According to this vision many of the inefficiencies and injustices of the current global education system can be addressed by applying AI.

To surface such discussion around what is possible fiction 1 is based loosely on a narrative published by Luckin and Holmes ( 2017 ) themselves. In their paper, they imagine a school classroom ten years into the future from the time of writing, where a teacher is working with an AI teaching assistant. Built into their fiction are the key features of their vision of AI (Luckin et al. 2016 ), thus emphasis is given to:

AI designed to support teachers rather than replacing them;

Personalisation of learning experiences through adaptivity;

Replacement of one-off assessment by continuous monitoring of performance (Luckin, 2017 );

The monitoring of haptic data to adjust learning material to students’ emotional and physical state in real time;

The potential of AI to support learning twenty-first century skills, such as collaborative skills;

Teachers developing skills in data analysis as part of their role;

Students (and parents) as well as teachers having access to data about their learning.

While Luckin and Holmes ( 2017 ) acknowledge that the vision of AI sounds a “bit big brother” it is, as one would expect, essentially an optimistic piece in which all the key technologies they envisage are brought together to improve learning in a broad sense. The fiction developed here retains most of these elements, but reimagined for an HE context, and with a number of other changes:

Reference is also made to rooting teaching in learning science, one of the arguments for AI Luckin makes in a number of places (e.g. Luckin et al. 2016 ).

Students developing a long term relationship with the AI. It is often seen as a desirable aspect of providing AI as a lifelong learning partner (Woolf, et al. 2013 ).

Of course, the more sceptical reader may be troubled by some aspects of this vision, including the potential effects of continuously monitoring performance as a form of surveillance. The emphasis on personalization of learning through AI has been increasingly questioned (Selwyn, 2019a ).

The following excerpt gives a flavour of the fiction:

Actually, I partly picked this Uni because I knew they had AI like AIDan which teach you on principles based in learning science. And exams are a thing of the past! AIDan continuously updates my profile and uses this to measure what I have learned. I have set tutorials with AIDan to analyse data on my performance. Jane often talks me through my learning data as well. I work with him planning things like my module choices too. Some of my data goes to people in the department (like my personal tutor) to student and campus services and the library to help personalise their services.

Social robots in learning: Fiction 2, “Footbotball”

Luckin and Holmes ( 2017 ) see AI as instantiated by sensors and cameras built into the classroom furniture. Their AI does not seem to have a physical form, though it does have a human name. But there is also a literature around educational robots: a type of social robot for learning.

a physical robot, in the same space as the student. It has an intelligence that can support learning tasks and students learn by interacting with it through suitable semiotic systems (Catlin et al., 2018 ).

There is some evidence that learning is better when the learner interacts with a physical entity rather than purely virtual agent and certainly there might be beneficial where what is learned involves embodiment (Belpaeme et al., 2018 ). Fiction 2 offers an imaginative account of what learning alongside robots might be like, in the context of university sport rather than within the curriculum. The protagonist describes how he is benefiting from using university facilities to participate in an imaginary sport, footbotball.

Maybe it’s a bit weird to say, but it’s about developing mutual understanding and… respect. Like the bots can sense your feelings too and chip in with a word just to pick you up if you make a mistake. And you have to develop an awareness of their needs too. Know when is the right time to say something to them to influence them in the right direction. When you watch the best teams they are always like talking to each other. But also just moving together, keeping eyes on and moving as a unit.

The protagonist in fiction 2 describes the high level and employability skills he is learning from a sporting application of robotics. This also reminds us of how the widespread use of AI and robots in wider social contexts may be a key driver for adoption in HE.

Fiction 3: “Criticalbot in conversation”

Both fictions 1 and 2 are glossy science fictions, with a strongly futuristic feel and, as in traditional design scenarios the technology seems to be used as intended by the designer. In contrast, the third fiction is inspired by Bayne’s ( 2015 ) description of Teacherbot, a chatbot developed to support a MOOC on elearning and digital cultures. Teacherbot does not masquerade as human. Students on the course are told what it is and its interactions are clumsy enough to reveal this anyway. Yet Bayne’s ( 2015 ) argument is that it offers positive non-human affordances. Students seem to learn from it, partly by reflecting on its place in their learning. Thus fiction 3, echoing the sample transcripts between Teacherbot and real students quoted in Bayne ( 2015 ), is a fictional dialogue between a conversational agent and a group of students working on an assignment (itself on the topic of bias in AI). Criticalbot, as its name suggests, is being imagined used to teach the students to be more critical, e.g. by prompting them to think harder about how to read an academic text, raising questions about the authority of authors, and prompting discussion around quotes from a key text.

Student 1: @CriticalBot we have been set the task to research bias in AIED. What do you know about bias in AIED? CriticalBot: Blanchard is recommended in the reading list. Student 2: I think he means rtfm. Student 1: Blanchard? CriticalBot: Sorry. I should have typed: Blanchard, E. G. (2015). Socio-cultural imbalances in AIED research: Investigations, implications and opportunities. International Journal of Artificial Intelligence in Education, 25(2), 204–228. No one’s perfect. Student 1: @CriticalBot What is the main point he makes? CriticalBot: Nice try. Try reading the abstract. Abstracts summarise key points. It may also be useful to read the conclusion section to grasp the author’s main argument before deciding on whether to undertake a closer reading.

As the quotation from the fiction illustrates, echoing Bayne ( 2015 ), the conversation in Fiction 2 is not necessarily smooth; misunderstandings and conflicts occur. The fiction brings into view the less compliant vision of the student who might wish to game the system, a potential problem with AI which is apparent in the literature of AWE (Hussein et al. 2019 ). This fiction encapsulates an important alternative potential imaginary of AI, as a simple, low-tech intervention. At the same time in being designed to promote critical thinking it can also be seen as teaching a key, high-level skill. This challenges us to ask if an AI can truly do that and how.

The intelligent campus: Fiction 4, “The intelligent campus app”

The AIED literature with its emphasis on the direct application of AI to learning accounts for a big block of the literature about AI in Higher Education, but not all of it. Another rather separate literature exists around the smart or intelligent campus (e.g. JISC 2018; Min-Allah and Alrashed, 2020 ; Dong et al., 2020 ). This is the application of Internet of Things and increasingly AI to the management of the campus environment. This is often oriented towards estates management, such as monitoring room usage and controlling lighting and heating. But it does also encompass support of wayfinding, attendance monitoring, and ultimately of student experience, so presents an interesting contrast to the AIEd literature.

The fourth fiction is adapted from a report each section of which is introduced by quotes from an imaginary day in the life of a student, Leda, who reflects on the benefits of the intelligent/smart campus technologies to her learning experience (JISC, 2018). The emphasis in the report is on:

Data driven support of wayfinding and time management;

Integration of smart campus with smart city features (e.g. bus and traffic news);

Attendance monitoring and delivery of learning resources;

The student also muses about the ethics of the AI. She is presented as a little ambivalent about the monitoring technologies, and as in Luckin and Holmes ( 2017 ), it is referred to in her own words as potentially “a bit big brother” (JISC 2018: 9). But ultimately she concludes that the smart campus improves her experience as a student. In this narrative, unlike in the Luckin and Holmes ( 2017 ) fiction, the AI is much more in the background and lacks a strong personality. It is a different sort of optimistic vision geared towards convenience rather than excellence. There is much less of a futuristic feel, indeed one could say that not only does the technology exist to deliver many of the services described, they are already available and in use—though perhaps not integrated within one application.

Sitting on the bus I look at the plan for the day suggested in the University app. A couple of timetabled classes; a group work meeting; and there is a reminder about that R205 essay I have been putting off. There is quite a big slot this morning when the App suggests I could be in the library planning the essay – as well as doing the prep work for one of the classes it has reminded me about. It is predicting that the library is going to be very busy after 11AM anyway, so I decide to go straight there.

The fiction seeks to bring out more about the idea of “nudging” to change behaviours a concept often linked to AI and the ethics of which are queried by Selwyn ( 2019a ). The issue of how AI and robots might impact the agency of the learner recurs across the first four fictions.

AI and robotics in research: Fiction 5, “The Research Management Suite TM”

So far in this paper most of the focus has been on the application of AI and robotics to learning. AI also has applications in university research, but it is an area far less commonly considered than learning and teaching. Only 1% of CIOs responding to a survey of HEIs by Gartner had deployed AI for research, compared to 27% for institutional analytics and 10% for adaptive learning (Lowendahl and Williams, 2018 ). Some AI could be used directly in research, not just to perform analytical tasks, but to generate hypotheses to be tested (Jones et al., 2019 ). The “robot scientist” being tireless and able to work in a precise way could carry through many experiments and increase reproducibility (King, et al., 2009 ; Sparkes et al., 2010 ). It might have the potential to make significant discoveries independently, perhaps by simply exploiting its tirelessness to test every possible hypothesis rather than use intuition to select promising ones (Kitano, 2016 ).

Another direct application of AI to research is text and data mining (TDM). Given the vast rate of academic publishing there is growing need to mine published literature to offer summaries to researchers or even to develop and test hypotheses (McDonald and Kelly, 2012 ). Advances in translation also offer potential to make the literature in other languages more accessible, with important benefits.

Developments in publishing give us a further insight into how AI might be applied in the research domain. Publishers are investing heavily in AI (Gabriel, 2019 ). One probable landmark was that in 2019, Springer published the first “machine generated research book” (Schoenenberger, 2019 : v): a literature review of research on Lithium-Ion batteries, written entirely automatically. This does not suggest the end of the academic author, Springer suggest, but does imply changing roles (Schoenenberger, 2019 ). AI is being applied to many aspects of the publication process: to identify peer reviewers (Price and Flach, 2017 ), to assist review by checking statistics, to summarise open peer reviews, to check for plagiarism or for the fabrication of data (Heaven, 2018 ), to assist copy editing, to suggest keywords and to summarise and translate text. Other tools claim to predict the future citation of articles (Thelwall, 2019 ). Data about academics, their patterns of collaboration and citation through scientometrics are currently based primarily on structured bibliographic data. The cutting edge is the application of text mining techniques to further analyse research methods, collaboration patterns, and so forth (Atanassova et al., 2019 ). This implies a potential revolution in the management and evaluation of research. It will be relevant to ask what responsible research metrics are in this context (Wilsdon, 2015 ).

Instantiating these developments, the sixth fiction revolves around a university licensing “Research Management Suite TM “a set of imaginary proprietary tools to offer institutional level support to its researchers to increase and perhaps measure their productivity. A flavour of the fiction can be gleaned from this except:

Academic Mentor ™ is our premium meta analysis service. Drawing on historic career data from across the disciplines, it identifies potential career pathways to inform your choices in your research strategy. By identifying structural holes in research fields it enables you to position your own research within emerging research activity, so maximising your visibility and contribution. Mining data from funder strategy, the latest publications, preprints and news sources it identifies emergent interdisciplinary fields, matching your research skills and interests to the complex dynamics of the changing research landscape.

This fiction prompts questions about the nature of the researcher’s role and ultimately about what research is. At what point does the AI become a co-author, because it is making a substantive intellectual contribution to writing a research output, making a creative leap or even securing funding? Given the centrality of research to academic identity this indeed may feel even more challenging than the teaching related scenarios. This fiction also recognised the important role of EdTech companies in how AI reaches HE, partly because of the high cost of AI development. The reader is also prompted to wonder how the technology might disrupt the HE landscape if those investing in these technologies were ambitious newer institutions keen to rise in university league tables.

Tackling pragmatic barriers: Fiction 6, “Verbatim minutes of University AI project steering committee: AI implementation phase 3”

A very large literature around technologies in HE in general focuses on the challenges of implementing them as a change management problem. Reid ( 2014 ), for example, seeks to develop a model of the differing factors that block the smooth implementation of learning technologies in the classroom, such as problems with access to the technology, project management challenges, as well as issues around teacher identity. Echoing these arguments, Tsai et al.’s ( 2017 , 2019 ) work captures why for all the hype around it, Learning Analytics have not yet found extensive practical application in HE. Given that AI requires intensive use of data, by extension we can argue that the same barriers will probably apply to AI. Specifically Tsai et al. ( 2017 , 2019 ) identify barriers in terms of technical, financial and other resource demands, ethics and privacy issues, failures of leadership, a failure to involve all stakeholders (students in particular) in development, a focus on technical issues and neglect of pedagogy, insufficient staff training and a lack of evidence demonstrating the impact on learning. There are hints of similar types of challenge around the implementation of administration focussed applications (Nurshatayeva, et al., 2020 ) and TDM (FutureTDM, 2016 ).

Reflecting these thoughts, the fifth fiction is an extract from an imaginary committee meeting, in which senior university managers discuss the challenges they are facing in implementing AI. It seeks to surface issues around teacher identity, disciplinary differences and resource pressures that might shape the extensive implementation of AI in practice.

Faculty of Humanities Director: But I think there is a pedagogic issue here. With the greatest of respect to Engineering, this approach to teaching, simply does not fit our subject. You cannot debate a poem or a philosophical treatise with a machine. Faculty of Engineering Director: The pilot project also showed improved student satisfaction. Data also showed better student performance. Less drop outs. Faculty of Humanities Director: Maybe that’s because… Vice Chancellor: All areas where Faculty of Humanities has historically had a strategic issue. Faculty of Engineering Director: The impact on employability has also been fantastic, in terms of employers starting to recognise the value of our degrees now fluency with automation is part of our graduate attributes statement. Faculty of Humanities Director: I see the benefits, I really do. But you have to remember you are taking on deep seated assumptions within the disciplinary culture of Humanities at this university. Staff are already under pressure with student numbers not to mention in terms of producing world class research! I am not sure how far this can be pushed. I wouldn’t want to see more industrial action.

Learning analytics and datafication: Fiction 7, “Dashboards”

Given the strong relation between “big data” and AI, the claimed benefits and the controversies that already exist around LA are relevant to AI too (Selwyn, 2019a ). The main argument for LA is that they give teachers and learners themselves information to improve learning processes. Advocates talk of an obligation to act. LA can also be used for the administration of admissions decisions and ensuring retention. Chatbots are now being used to assist applicants through complex admissions processes or to maintain contact to ensure retention and appear to offer a cheap and effective alternative (Page and Gehlbach, 2017 ; Nurshatayeva et al., 2020 ). Gathering more data about HE also promotes public accountability.

However, data use in AI does raise many issues. The greater the dependence on data or data driven AI the greater the security issues associated with the technology. Another inevitable concern is with legality and the need to abide by appropriate privacy legislation, such as GDPR in Europe. Linked to this are clearly privacy issues, implying consent, the right to control over the use of one’s data and the right to withdraw (Fjeld et al., 2020 ). Yet a recent study by Jones ( 2020 ) found students knew little of how LA were being used in their institution or remembered consenting to allowing their data to be used. These would all be recognised as issues by most AI projects.

However, increasingly critiques of AI in learning centre around the datafication of education (Jarke and Breiter, 2019 ; Williamson and Eynon, 2020 ; Selwyn, 2019 a; Kwet and Prinsloo, 2020 ). A data driven educational system has the potential to be used or experienced as a surveillance system. “What can be accomplished with data is usually a euphemism for what can be accomplished with surveillance” (Kwet and Prinsloo, 2020 : 512). Not only might individual freedoms be threatened by institutions or commercial providers undertaking surveillance of student and teaching staff behaviour, there is also a chilling effect just through the fear of being watched (Kwet and Prinsloo, 2020 ). Students become mere data points, as surveillance becomes intensified and normalised (Manolev et al. 2019 ). While access to their own learning data could be empowering for students, techniques such as nudging intended to influence people without their knowledge undermine human agency (Selwyn, 2019b ). Loss of human agency is one of the fears revolving around AI and robots.

Further, a key issue with AI is that although predictions can be accurate or useful it is quite unclear how these were produced. Because AI “learns” from data, even the designers do not fully understand how the results were arrived at so they are certainly hard to explain to the public. The result is a lack of transparency, and so of accountability, leading to deresponsibilisation.

Much of the current debate around big data and AI revolves around bias, created by using training data that does not represent the whole population, reinforced by the lack of diversity among designers of the systems. If data is based on existing behaviour, this is likely to reproduce existing patterns of disadvantage in society, unless AI design takes into account social context—but datafication is driven by standardisation. Focussing on technology diverts attention from the real causes of achievement gaps in social structures, it could be argued (Macgilchrist, 2019 ). While often promoted as a means of empowering learners and their teachers, mass personalisation of education redistributes power away from local decision making (Jarke and Breiter, 2019 ; Zeide, 2017 ). In the context of AIEd there is potential for assumptions about what should be taught to show very strong cultural bias, in the same way that critics have already argued that plagiarism detection systems impose culturally specific notions of authorship and are marketed in a way to reinforce crude ethnic stereotypes (Canzonetta and Kannan, 2016 ).

Datafication also produces performativity: the tendency of institutions (and teachers and students) to shift their behaviour towards doing what scores well against the metric, in a league table mentality. Yet what is measured is often a proxy of learning or reductive of what learning in its full sense is, critics argue (Selwyn, 2019b ). The potential impact is to turn HE further into a marketplace (Williamson, 2019 ). It is evident that AI developments are often partly a marketing exercise (Lacity, 2017 ). Edtech companies play a dominant role in developing AI (Williamson and Eynon, 2020 ). Selwyn ( 2019a ) worries that those running education will be seduced by glittering promises of techno-solutionism, when the technology does not really work. The UK government has invested heavily in gathering more data about HE in order to promote the reform of HE in the direction of marketisation and student choice (Williamson and Eynon, 2020 ). Learning data could also increasingly itself become a commodity, further reinforcing the commercialisation of HE.

Thus fiction 6 explores the potential to gather data about learning on a huge scale, make predictions based on it and take actions via conveying information to humans or through chatbots. In the fiction the protagonist explains an imaginary institutional level system that is making data driven decisions about applicants and current students.

Then here we monitor live progress of current students within their courses. We can dip down into attendance, learning environment use, library use, and of course module level performance and satisfaction plus the extra-curricula data. Really low-level stuff some of it. It’s pretty much all there, monitored in real time. We are really hot on transition detection and monitoring. The chatbots are used just to check in on students, see they are ok, nudge things along, gather more data. Sometimes you just stop and look at it ticking away and think “wow!”. That all gets crunched by the system. All the time we feed the predictives down into departmental dashboards, where they pick up the intervention work. Individual teaching staff have access via smart speaker. Meanwhile, we monitor the trend lines up here.

In the fiction the benefits in terms of being able to monitor and address attainment gaps is emphasised. The protagonist’s description of projects that are being worked on suggests competing drivers behind such developments including meeting government targets, cost saving and the potential to make money by reselling educational data.

Infrastructure: Fiction 8, “Minnie—the AI admin assistant”

A further dimension to the controversy around AI is to consider its environmental cost and the societal impact of the wider infrastructures needed to support AI. Brevini ( 2020 ) points out that a common AI training model in linguistics can create the equivalent of five times the lifetime emissions of an average US car. This foregrounds the often unremarked environmental impact of big data and AI. It also prompts us to ask questions about the infrastructure required for AI. Crawford and Joler’s ( 2018 ) brilliant Anatomy of an AI system reveals that making possible the functioning of a physically rather unassuming AI like Amazon echo, is a vast global infrastructure based on mass human labour, complex logistic chains and polluting industry.

The first part of fiction 8 describes a personal assistant based on voice recognition, like Siri, which answers all sorts of administrative questions.The protagonist expresses some unease with how the system works, reflecting the points made by Rummel et al. ( 2016 ) about the failure of systems if despite their potential sophistication they lack nuance and flexibility in their application. There is also a sense of alienation (Griffiths, 2015 ). The second part of the fiction extends this sense of unease to a wider perspective on the usually invisible, but very material infrastructure which AI requires, as captured in Crawford and Joler ( 2018 ). In addition, imagery is drawn from Maughan’s ( 2016 ) work where he travels backwards up the supply chain for consumer electronics from the surreal landscape of hi-tech docks then visiting different types of factories and ending up visiting a huge polluted lake created by mining operations for rare earth elements in China. This perspective queries all the other fictions with their focus on using technologies or even campus infrastructure by widening the vision to encompass the global infrastructures that are required to make AI possible.

The vast effort of global logistics to bring together countless components to build the devices through which we interact with AI. Lorries queuing at the container port as another ship comes in to dock. Workers making computer components in hi-tech factories in East Asia. All dressed in the same blue overalls and facemasks, two hundred workers queue patiently waiting to be scan searched as they leave work at the end of the shift. Exploitative mining extracting non-renewable, scarce minerals for computer components, polluting the environment and (it is suspected) reducing the life expectancy of local people. Pipes churn out a clayey sludge into a vast lake.

Conclusion: using the fictions together

As we have seen each of the fictions seeks to open up different positive visions or dimensions of debate around AI (summarised in Table 2 below). All implicitly ask questions about the nature of human agency in relationship to AI systems and robots, be that through empowerment through access to learning data (fiction 1), their power to play against the system (Fiction 3) or the hidden effects of nudging (Fiction 4) and the reinforcements of social inequalities. Many raise questions about the changing role of staff or the skills required to operate in this environment. They are written in a way seeking to avoid taking sides, e.g. not to always undercut a utopian view or simply present a dark dystopia. Each contains elements that might be inspirational or a cause of controversy. Specifically, they can be read together to suggest tensions between different possible futures. In particular fictions 7 and 8 and the commercial aspects implied by the presentation of fiction 5, reveal aspects of AI largely invisible in the glossy strongly positive images in fictions 1 and 2, or the deceptive mundanity of fiction 3. It is also anticipated that the fictions will be read “against the grain” by readers wishing to question what the future is likely to be or should be like. This is one of the affordances of them being fictions.

The most important contribution of the paper was the wide-ranging narrative literature review emphasising the social, ethical, pedagogic and management issues of automation through AI and robots on HE as a whole. On the basis of the understanding gained from the literature review a secondary contribution was the development of a collection of eight accessible, repurposable design fictions that prompt debate about the potential role of AI and robots in HE. This prompts us to notice common challenges, such as around commodification and the changing role of data. It encompasses work written by developers, by those with more visionary views, those who see the challenges as primarily pragmatic and those coming from much more critical perspectives.

The fictions are intended to be used to explore staff and student responses through data collection using the fictions to elicit views. The fictions could also be used in teaching to prompt debate among students, perhaps setting them the task to write new fictions (Rapp, 2020 ). Students of education could use them to explore the potential impact of AI on educational institutions and to discuss the role of technologies in educational change more generally. The fictions could be used in teaching students of computer science, data science, HCI and information systems in courses about computer ethics, social responsibility and sustainable computing—as well as those directly dealing with AI. They could also be used in Media Studies and Communications, e.g. to compare them with other future imaginaries in science fiction or to design multimedia creations inspired by such fictions. They might also be used for management studies as a case study of strategizing around AI in a particular industry.

While there is an advantage in seeking to encompass the issues within a small collection of engaging fictions that in total run to less than 5000 words, it must be acknowledged that not every issue is reflected. For example, what is not included is the different ways that AI and robots might be used in teaching different disciplines, such as languages, computer science or history. The many ways that robots might be used in background functions or to play the role themselves of learner also requires further exploration. Most of the fictions were located in a fairly near future, but there is also potential to develop much more futuristic fictions. These gaps leave room for the development of more fictions.

The paper has explained the rationale and process of writing design fictions. To the growing literature around design fictions, the paper seeks to make a contribution by emphasising the use of design fictions as collections, exploiting different narratives and styles and genre of writing to set up intertextual reflections that help us ask questions about technologies in the widest sense.

Availability of data and materials

Data from the project is available from the University of Sheffield repository, ORDA. https://doi.org/10.35542/osf.io/s2jc8 .

Amer, M., Daim, T., & Jetter, A. (2013). A review of scenario planning. Futures, 46, 23–40.

Article Google Scholar

Atanassova, I., Bertin, M., & Mayr, P. (2019). Editorial: mining scientific papers: NLP-enhanced bibliometrics. Frontiers in Research Metrics and Analytics . https://doi.org/10.3389/frma.2019.00002 .

Auger, J. (2013). Speculative design: Crafting the speculation. Digital Creativity, 24 (1), 11–35.

Badampudi, D., Wohlin, C., & Petersen, K. (2015). Experiences from using snowballing and database searches in systematic literature studies. In Proceedings of the 19th International Conference on Evaluation and Assessment in Software Engineering (pp. 1–10).

Baker, T., Smith, L. and Anissa, N. (2019). Educ-AI-tion Rebooted? Exploring the future of artificial intelligence in schools and colleges. NESTA. https://www.nesta.org.uk/report/education-rebooted/ .

Bates, T., Cobo, C., Mariño, O., & Wheeler, S. (2020). Can artificial intelligence transform higher education? International Journal of Educational Technology in Higher Education . https://doi.org/10.1186/s41239-020-00218-x .

Bayne, S. (2015). Teacherbot: interventions in automated teaching. Teaching in Higher Education, 20 (4), 455–467.

Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., & Tanaka, F. (2018). Social robots for education: A review. https://doi.org/10.1126/scirobotics.aat5954 .

Blanchard, E. G. (2015). Socio-cultural imbalances in AIED research: Investigations, implications and opportunities. International Journal of Artificial Intelligence in Education, 25 (2), 204–228.

Bleecker, J. (2009). Design fiction: A short essay on design, science, fact and fiction. Near Future Lab.

Blythe, M. (2017). Research fiction: storytelling, plot and design. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 5400–5411).

Blythe, M., Andersen, K., Clarke, R., & Wright, P. (2016). Anti-solutionist strategies: Seriously silly design fiction. Conference on Human Factors in Computing Systems - Proceedings (pp. 4968–4978). Association for Computing Machinery.

Brevini, B. (2020). Black boxes, not green: Mythologizing artificial intelligence and omitting the environment. Big Data & Society, 7 (2), 2053951720935141.

Canzonetta, J., & Kannan, V. (2016). Globalizing plagiarism & writing assessment: a case study of Turnitin. The Journal of Writing Assessment , 9(2). http://journalofwritingassessment.org/article.php?article=104 .

Carroll, J. M. (1999) Five reasons for scenario-based design. In Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences . HICSS-32. Abstracts and CD-ROM of Full Papers, Maui, HI, USA, 1999, pp. 11. https://doi.org/10.1109/HICSS.1999.772890 .

Catlin, D., Kandlhofer, M., & Holmquist, S. (2018). EduRobot Taxonomy a provisional schema for classifying educational robots. 9th International Conference on Robotics in Education 2018, Qwara, Malta.

Clay, J. (2018). The challenge of the intelligent library. Keynote at What does your eResources data really tell you? 27th February, CILIP.

Crawford, K., & Joler, V. (2018) Anatomy of an AI system , https://anatomyof.ai/ .

Darby, E., Whicher, A., & Swiatek, A. (2017). Co-designing design fictions: a new approach for debating and priming future healthcare technologies and services. Archives of design research. Health Services Research, 30 (2), 2.

Google Scholar

Demartini, C., & Benussi, L. (2017). Do Web 4.0 and Industry 4.0 Imply Education X.0? IT Pro , 4–7.

Dong, Z. Y., Zhang, Y., Yip, C., Swift, S., & Beswick, K. (2020). Smart campus: Definition, framework, technologies, and services. IET Smart Cities, 2 (1), 43–54.

Dourish, P., & Bell, G. (2014). “resistance is futile”: Reading science fiction alongside ubiquitous computing. Personal and Ubiquitous Computing, 18 (4), 769–778.

Dunne, A., & Raby, F. (2001). Design noir: The secret life of electronic objects . New York: Springer Science & Business Media.

Fjeld, J., Achten, N., Hilligoss, H., Nagy, A., & Srikumar, M. (2020). Principled artificial intelligence: Mapping consensus in ethical and rights-based approaches to principles for AI. SSRN Electronic Journal . https://doi.org/10.2139/ssrn.3518482 .

Følstad, A., Skjuve, M., & Brandtzaeg, P. (2019). Different chatbots for different purposes: Towards a typology of chatbots to understand interaction design. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11551 LNCS , pp. 145–156. Springer Verlag.

Future TDM. (2016). Baseline report of policies and barriers of TDM in Europe. https://project.futuretdm.eu/wp-content/uploads/2017/05/FutureTDM_D3.3-Baseline-Report-of-Policies-and-Barriers-of-TDM-in-Europe.pdf .

Gabriel, A. (2019). Artificial intelligence in scholarly communications: An elsevier case study. Information Services & Use, 39 (4), 319–333.

Griffiths, D. (2015). Visions of the future, horizon report . LACE project. http://www.laceproject.eu/visions-of-the-future-of-learning-analytics/ .

Heaven, D. (2018). The age of AI peer reviews. Nature, 563, 609–610.

Hockly, N. (2019). Automated writing evaluation. ELT Journal, 73 (1), 82–88.

Holmes, W., Bialik, M. and Fadel, C. (2019). Artificial Intelligence in Education . The center for curriculum redesign. Boston, MA.

Hussein, M., Hassan, H., & Nassef, M. (2019). Automated language essay scoring systems: A literature review. PeerJ Computer Science . https://doi.org/10.7717/peerj-cs.208 .

Inayatullah, S. (2008). Six pillars: Futures thinking for transforming. foresight, 10 (1), 4–21.

Jarke, J., & Breiter, A. (2019). Editorial: the datafication of education. Learning, Media and Technology, 44 (1), 1–6.

JISC. (2019). The intelligent campus guide. Using data to make smarter use of your university or college estate . https://www.jisc.ac.uk/rd/projects/intelligent-campus .

Jones, E., Kalantery, N., & Glover, B. (2019). Research 4.0 Interim Report. Demos.

Jones, K. (2019). “Just because you can doesn’t mean you should”: Practitioner perceptions of learning analytics ethics. Portal, 19 (3), 407–428.

Jones, K., Asher, A., Goben, A., Perry, M., Salo, D., Briney, K., & Robertshaw, M. (2020). “We’re being tracked at all times”: Student perspectives of their privacy in relation to learning analytics in higher education. Journal of the Association for Information Science and Technology . https://doi.org/10.1002/asi.24358 .

King, R. D., Rowland, J., Oliver, S. G., Young, M., Aubrey, W., Byrne, E., et al. (2009). The automation of science. Science, 324 (5923), 85–89.

Kitano, H. (2016). Artificial intelligence to win the nobel prize and beyond: Creating the engine for scientific discovery. AI Magazine, 37 (1), 39–49.

Kwet, M., & Prinsloo, P. (2020). The ‘smart’ classroom: a new frontier in the age of the smart university. Teaching in Higher Education, 25 (4), 510–526.

Lacity, M., Scheepers, R., Willcocks, L. & Craig, A. (2017). Reimagining the University at Deakin: An IBM Watson Automation Journey . The Outsourcing Unit Working Research Paper Series.

Lowendahl, J.-M., & Williams, K. (2018). 5 Best Practices for Artificial Intelligence in Higher Education. Gartner. Research note.

Luckin, R. (2017). Towards artificial intelligence-based assessment systems. Nature Human Behaviour, 1 (3), 1–3.

Luckin, R., & Holmes, W. (2017). A.I. is the new T.A. in the classroom. https://howwegettonext.com/a-i-is-the-new-t-a-in-the-classroom-dedbe5b99e9e .

Luckin, R., Holmes, W., Griffiths, M., & Pearson, L. (2016). Intelligence unleashed an argument for AI in Education. Pearson. https://www.pearson.com/content/dam/one-dot-com/one-dot-com/global/Files/about-pearson/innovation/open-ideas/Intelligence-Unleashed-v15-Web.pdf .

Lyckvi, S., Wu, Y., Huusko, M., & Roto, V. (2018). Eagons, exoskeletons and ecologies: On expressing and embodying fictions as workshop tasks. ACM International Conference Proceeding Series (pp. 754–770). Association for Computing Machinery.

Macgilchrist, F. (2019). Cruel optimism in edtech: When the digital data practices of educational technology providers inadvertently hinder educational equity. Learning, Media and Technology, 44 (1), 77–86.

Manolev, J., Sullivan, A., & Slee, R. (2019). The datafication of discipline: ClassDojo, surveillance and a performative classroom culture. Learning, Media and Technology, 44 (1), 36–51.

Martha, A. S. D., & Santoso, H. B. (2019). The design and impact of the pedagogical agent: A systematic literature review. Journal of Educators Online, 16 (1), n1.

Maughan, T. (2016). The hidden network that keeps the world running. https://datasociety.net/library/the-hidden-network-that-keeps-the-world-running/ .

McDonald, D., & Kelly, U. (2012). The value and benefits of text mining . England: HEFCE.

Min-Allah, N., & Alrashed, S. (2020). Smart campus—A sketch. Sustainable Cities and Society . https://doi.org/10.1016/j.scs.2020.102231 .

Nathan, L. P., Klasnja, P. V., & Friedman, B. (2007). Value scenarios: a technique for envisioning systemic effects of new technologies. In CHI'07 extended abstracts on human factors in computing systems (pp. 2585–2590).

Nurshatayeva, A., Page, L. C., White, C. C., & Gehlbach, H. (2020). Proactive student support using artificially intelligent conversational chatbots: The importance of targeting the technology. EdWorking paper, Annenberg University https://www.edworkingpapers.com/sites/default/files/ai20-208.pdf .

Page, L., & Gehlbach, H. (2017). How an artificially intelligent virtual assistant helps students navigate the road to college. AERA Open . https://doi.org/10.1177/2332858417749220 .

Pinkwart, N. (2016). Another 25 years of AIED? Challenges and opportunities for intelligent educational technologies of the future. International journal of artificial intelligence in education, 26 (2), 771–783.

Price, S., & Flach, P. (2017). Computational support for academic peer review: A perspective from artificial intelligence. Communications of the ACM, 60 (3), 70–79.

Rapp, A. (2020). Design fictions for learning: A method for supporting students in reflecting on technology in human–computer interaction courses. Computers & Education, 145, 103725.

Reid, P. (2014). Categories for barriers to adoption of instructional technologies. Education and Information Technologies, 19 (2), 383–407.

Renz, A., & Hilbig, R. (2020). Prerequisites for artificial intelligence in further education: Identification of drivers, barriers, and business models of educational technology companies. International Journal of Educational Technology in Higher Education . https://doi.org/10.1186/s41239-020-00193-3 .

Roll, I., & Wylie, R. (2016). Evolution and Revolution in Artificial Intelligence in Education. International Journal of Artificial Intelligence in Education, 26 (2), 582–599.

Rummel, N., Walker, E., & Aleven, V. (2016). Different futures of adaptive collaborative learning support. International Journal of Artificial Intelligence in Education, 26 (2), 784–795.

Schoenenberger, H. (2019). Preface. In H. Schoenenberger (Ed.), Lithium-ion batteries a machine-generated summary of current research (v–xxiii) . Berlin: Springer.

Selwyn, N. (2019a). Should robots replace teachers? AI and the future of education . New Jersey: Wiley.

Selwyn, N. (2019b). What’s the problem with learning analytics? Journal of Learning Analytics, 6 (3), 11–19.

Selwyn, N., Pangrazio, L., Nemorin, S., & Perrotta, C. (2020). What might the school of 2030 be like? An exercise in social science fiction. Learning, Media and Technology, 45 (1), 90–106.

Sparkes, A., Aubrey, W., Byrne, E., Clare, A., Khan, M. N., Liakata, M., et al. (2010). Towards robot scientists for autonomous scientific discovery. Automated Experimentation, 2 (1), 1.

Strobl, C., Ailhaud, E., Benetos, K., Devitt, A., Kruse, O., Proske, A., & Rapp, C. (2019). Digital support for academic writing: A review of technologies and pedagogies. Computers and Education, 131, 33–48.

Templier, M., & Paré, G. (2015). A framework for guiding and evaluating literature reviews. Communications of the Association for Information Systems, 37 (1), 6.

Thelwall, M. (2019). Artificial intelligence, automation and peer review . Bristol: JISC.

Tsai, Y., & Gasevic, D. (2017). Learning analytics in higher education—Challenges and policies: A review of eight learning analytics policies. ACM International Conference Proceeding Series (pp. 233–242). Association for Computing Machinery.

Tsai, Y. S., Poquet, O., Gašević, D., Dawson, S., & Pardo, A. (2019). Complexity leadership in learning analytics: Drivers, challenges and opportunities. British Journal of Educational Technology, 50 (6), 2839–2854.

Tsekleves, E., Darby, A., Whicher, A., & Swiatek, P. (2017). Co-designing design fictions: A new approach for debating and priming future healthcare technologies and services. Archives of Design Research, 30 (2), 5–21.

Wellnhammer, N., Dolata, M., Steigler, S., & Schwabe, G. (2020). Studying with the help of digital tutors: Design aspects of conversational agents that influence the learning process. Proceedings of the 53rd Hawaii International Conference on System Sciences , (pp. 146–155).

Williamson, B. (2019). Policy networks, performance metrics and platform markets: Charting the expanding data infrastructure of higher education. British Journal of Educational Technology, 50 (6), 2794–2809.

Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology. https://doi.org/10.1080/17439884.2020.1798995 .

Wilsdon, J. (2015). The metric tide: Independent review of the role of metrics in research assessment and management . Sage.

Winkler, R. & Söllner, M. (2018). Unleashing the potential of chatbots in education: A state-of-the-art analysis. In: Academy of Management Annual Meeting (AOM). Chicago, USA.

Woolf, B. P., Lane, H. C., Chaudhri, V. K., & Kolodner, J. L. (2013). AI grand challenges for education. AI Magazine, 34 (4), 66–84.

Zawacki-Richter, O., Marín, V., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—where are the educators? International Journal of Educational Technology in Higher Education . https://doi.org/10.1186/s41239-019-0171-0 .

Zeide, E. (2017). The structural consequences of big data-driven education. Big Data, 5 (2), 164–172.

Download references

Acknowledgements

Not applicable.

The project was funded by Society of Research into Higher Education—Research Scoping Award—SA1906.

Author information

Authors and affiliations.

Information School, The University of Sheffield, Level 2, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK

You can also search for this author in PubMed Google Scholar

Contributions

AC conceived and wrote the entire article. All authors read and approved the final manuscript.

Corresponding author

Correspondence to A. M. Cox .

Ethics declarations

Competing interests.

The author declares that he has no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Cox, A.M. Exploring the impact of Artificial Intelligence and robots on higher education through literature-based design fictions. Int J Educ Technol High Educ 18 , 3 (2021). https://doi.org/10.1186/s41239-020-00237-8

Download citation

Received : 04 September 2020

Accepted : 24 November 2020

Published : 18 January 2021

DOI : https://doi.org/10.1186/s41239-020-00237-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Artificial Intelligence
Social robots
Learning analytics
Design fiction

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser .

We're Hiring!
Help Center
Most Cited Papers
Most Downloaded Papers
Newest Papers
Last »
Autonomous Robotics Follow Following
Mobile Robotics Follow Following
Human-Robot Interaction Follow Following
Mechatronics & Robotics Follow Following
Mechatronics Follow Following
Artificial Intelligence Follow Following
Multiagent Robotics Follow Following
Humanoid Robotics Follow Following
Robotics (Computer Science) Follow Following
Computer Vision Follow Following

Enter the email address you signed up with and we'll email you a reset link.

Academia.edu Journals
We're Hiring!
Help Center
Find new research papers in:
Health Sciences
Earth Sciences
Cognitive Science
Mathematics
Computer Science
Academia ©2024

Robotics and Intelligent Systems Certificate Program

Topics for research in robotics and intelligent systems.

General areas for study and research:

Chemical and Biological Engineering

Control of chemical and biological dynamic processes
Optimal design of systems for material processing
Manipulation of matter at atomic and molecular scale

Civil and Environmental Engineering

Structural health monitoring and adaptive structures
Water resources
Earthquake detection and protective design
Remote sensing of natural resources
Urban planning and engineering

Computer Science

Theory and practice of computation for physical systems
Game playing, photo identification, and semantic identification
Real-time algorithms for measurement, prediction, and control
Artificial intelligence and machine learning
Databases, Internet security, and privacy

Electrical Engineering

Information theory
Electricity, microelectronics, and electromagnetism
Digital circuits and computation
Image processing, face, and character recognition
Video analysis and manipulation
Telecommunications networks
Autonomous vehicles

Mechanical and Aerospace Engineering

Robotic devices and systems
Autonomous air, sea, undersea, and land vehicles
Space exploration and development
Intelligent control systems
Biomimetic modeling, dynamics, and control
Cooperating robots for manufacturing and assembly
Cooperative control of natural and engineered groups
Identification of dynamic system models
Optimal state estimation and control

Operations Research and Financial Engineering

Intelligent transportation systems
Financial management and risk analysis
Dynamic resource management
Decision science
Optimal design
Knowledge, reasoning, and language
Logic and metaphysics
Politics and art of robotics and intelligent systems
Inference, reasoning, problem solving
Human factors and human-machine interaction
Human motor control
Modeling perception
Neural network (connectionist) modeling of cognitive functions
Reinforcement learning
Study of brain function using functional magnetic response imaging, electrical, and optical methods

200+ Robotics Research Topics: Discovering Tomorrow’s Tech

Post author By admin
September 15, 2023

Explore cutting-edge robotics research topics and stay ahead of the curve with our comprehensive guide. Discover the latest advancements in the field today.

Robotics research topics are not like any other research topics. In these topics science fiction meets reality and innovation knows no bounds.

In this blog post we are going to explore some of the best robotics research topics that will help you to explore machine learning, artificial intelligence and many more.

Apart from that you will also explore the industries and the future of robotics. Whether you are an experienced engineering or a student of robotics, these project ideas will definitely help you to explore a lot more the dynamic and ever evolving world of robotics. So be ready to explore these topics:-

Table of Contents

Robotics Research Topics

Have a close look at robotics research topics:-

Autonomous Robots

Development of an Autonomous Delivery Robot for Urban Environments
Swarm Robotics for Agricultural Crop Monitoring and Maintenance
Simultaneous Localization and Mapping (SLAM) for Indoor Navigation of Service Robots
Human-Robot Interaction Study for Improved Robot Assistance in Healthcare
Self-Driving Car Prototype with Advanced Perception and Decision-Making Algorithms
Autonomous Aerial Surveillance Drones for Security Applications
Autonomous Underwater Vehicles (AUVs) for Ocean Exploration
Robotic Drones for Disaster Response and Search-and-Rescue Missions
Autonomous Exploration Rover for Planetary Surfaces
Unmanned Aerial Vehicles (UAVs) for Precision Agriculture and Crop Analysis

Robot Manipulation and Grasping

Object Recognition and Grasping Planning System for Warehouse Automation
Cooperative Multi-Robot Manipulation for Assembly Line Tasks
Tactile Sensing Integration for Precise Robotic Grasping
Surgical Robot with Enhanced Precision and Control for Minimally Invasive Surgery
Robotic System for Automated 3D Printing and Fabrication
Robot-Assisted Cooking System with Object Recognition and Manipulation
Robotic Arm for Hazardous Materials Handling and Disposal
Biomechanically Inspired Robotic Finger Design for Grasping
Multi-Arm Robotic System for Collaborative Manufacturing
Development of a Dexterous Robotic Hand for Complex Object

Robot Vision and Perception:

Object Detection and Recognition Framework for Robotic Inspection
Deep Learning-Based Vision System for Real-time Object Recognition
Human Activity Recognition Algorithm for Assistive Robots
Vision-Based Localization and Navigation for Autonomous Vehicles
Image Processing and Computer Vision for Robotic Surveillance
Visual Odometry for Precise Mobile Robot Positioning
Facial Recognition System for Human-Robot Interaction
3D Object Reconstruction from 2D Images for Robotic Mapping
Autonomous Drone with Advanced Vision-Based Obstacle Avoidance
Development of a Visual SLAM System for Autonomous Indoor navigation.

Human-Robot Collaboration

Development of Robot Assistants for Elderly Care and Companionship
Implementation of Collaborative Robots (Cobots) in Manufacturing Processes
Shared Control Interfaces for Teleoperation of Remote Robots
Ethics and Social Impact Assessment of Human-Robot Interaction
Evaluation of User Interfaces for Robotic Assistants in Healthcare
Human-Centric Design of Robotic Exoskeletons for Enhanced Mobility
Enhancing Worker Safety in Industrial Settings through Human-Robot Collaboration
Haptic Feedback Systems for Improved Teleoperation of Remote Robots
Investigating Human Trust and Acceptance of Autonomous Robots in Daily Life
Design and Testing of Safe and Efficient Human-Robot Collaborative Workstations

Bio-Inspired Robotics

Biohybrid Robots Combining Biological and Artificial Components for Unique Functions
Evolutionary Robotics Algorithms for Robot Behavior Optimization
Swarm Robotics Inspired by Insect Behavior for Collective Tasks
Design and Fabrication of Soft Robotics for Flexible and Adaptive Movement
Biomimetic Robotic Fish for Underwater Exploration
Biorobotics Research for Prosthetic Limb Design and Control
Development of a Robotic Pollination System Inspired by Bees
Bio-Inspired Flying Robots for Agile and Efficient Aerial Navigation
Bio-Inspired Sensing and Localization Techniques for Robotic Applications
Development of a Legged Robot with Biomimetic Locomotion Inspired by Animals

Robot Learning and AI

Transfer Learning Strategies for Robotic Applications in Varied Environments
Explainable AI Models for Transparent Robot Decision-Making
Robot Learning from Demonstration (LfD) for Complex Tasks
Machine Learning Algorithms for Predictive Maintenance of Industrial Robots
Neural Network-Based Vision System for Autonomous Robot Learning
Reinforcement Learning for UAV Swarms and Cooperative Flight
Human-Robot Interaction Studies to Improve Robot Learning
Natural Language Processing for Human-Robot Communication
Robotic Systems with Advanced AI for Autonomous Exploration
Implementation of Reinforcement Learning Algorithms for Robotic Control

Robotics in Healthcare

Design and Testing of Robotic Prosthetics and Exoskeletons for Enhanced Mobility
Telemedicine Platform for Remote Robotic Medical Consultations
Robot-Assisted Rehabilitation System for Physical Therapy
Simulation-Based Training Environment for Robotic Surgical Skill Assessment
Humanoid Robot for Social and Emotional Support in Healthcare Settings
Autonomous Medication Dispensing Robot for Hospitals and Pharmacies
Wearable Health Monitoring Device with AI Analysis
Robotic Systems for Elderly Care and Fall Detection
Surgical Training Simulator with Realistic Haptic Feedback
Development of a Robotic Surgical Assistant for Minimally Invasive Procedures

Robots in Industry

Quality Control and Inspection Automation with Robotic Systems
Risk Assessment and Safety Measures for Industrial Robot Environments
Human-Robot Collaboration Solutions for Manufacturing and Assembly
Warehouse Automation with Robotic Pick-and-Place Systems
Industrial Robot Vision Systems for Quality Assurance
Integration of Cobots in Flexible Manufacturing Cells
Robot Grippers and End-Effector Design for Specific Industrial Tasks
Predictive Maintenance Strategies for Industrial Robot Fleet
Robotics for Lean Manufacturing and Continuous Improvement
Robotic Automation in Manufacturing: Process Optimization and Integration

Robots in Space Exploration

Precise Autonomous Spacecraft Navigation for Deep Space Missions
Robotics for Satellite Servicing and Space Debris Removal
Lunar and Martian Surface Exploration with Autonomous Robots
Resource Utilization and Mining on Extraterrestrial Bodies with Robots
Design and Testing of Autonomous Space Probes for Interstellar Missions
Autonomous Space Telescopes for Astronomical Observations
Robot-Assisted Lunar Base Construction and Maintenance
Planetary Sample Collection and Return Missions with Robotic Systems
Biomechanics and Human Factors Research for Astronaut-Robot Collaboration
Autonomous Planetary Rovers: Mobility and Scientific Exploration

Environmental Robotics

Environmental Monitoring and Data Collection Using Aerial Drones
Robotics in Wildlife Conservation: Tracking and Protection of Endangered Species
Disaster Response Robots: Search, Rescue, and Relief Operations
Autonomous Agricultural Robots for Sustainable Farming Practices
Autonomous Forest Fire Detection and Firefighting Robot Systems
Monitoring and Rehabilitation of Coral Reefs with Robotic Technology
Air Quality Monitoring and Pollution Detection with Mobile Robot Swarms
Autonomous River and Marine Cleanup Robots
Ecological Studies and Environmental Protection with Robotic Instruments
Development of Underwater Robotic Systems for Ocean Exploration and Monitoring

These project ideas span a wide range of topics within robotics research, offering opportunities for innovation, exploration, and contribution to the field. Researchers, students, and enthusiasts can choose projects that align with their interests and expertise to advance robotics technology and its applications.

Robotics Research Topics for high school students

Home Robots: Explore how robots can assist in daily tasks at home.
Medical Robotics: Investigate robots used in surgery and patient care.
Robotics in Education: Learn about robots teaching coding and science.
Agricultural Robots: Study robots in farming for planting and monitoring.
Space Exploration: Discover robots exploring planets and space.
Environmental Robots: Explore robots in conservation and pollution monitoring.
Ethical Questions: Discuss the ethical dilemmas in robotics.
DIY Robot Projects: Build and program robots from scratch.
Robot Competitions: Participate in exciting robotics competitions.
Cutting-Edge Innovations: Stay updated on the latest in robotics.

These topics offer exciting opportunities for high school students to delve into robotics research, learning, and creativity.

Easy Robotics Research Topics

Introduction to robotics.

Explore the basics of robotics, including robot components and their functions.

History of Robotics

Investigate the evolution of robotics from its beginnings to modern applications.

Robotic Sensors

Learn about various sensors used in robots for detecting and measuring data.

Simple Robot Building

Build a basic robot using kits or everyday materials and learn about its components.

Programming a Robot

Experiment with programming languages like Scratch or Blockly to control a robot’s movements.

Robots in Entertainment

Explore how robots are used in the entertainment industry, such as animatronics and robot performers.

Robotics in Toys

Investigate robotic toys and their mechanisms, such as remote-controlled cars and drones.

Robotic Pets

Learn about robotic pets like robot dogs and cats and their interactive features.

Robotics in Science Fiction

Analyze how robots are portrayed in science fiction movies and literature.

Robotic Safety

Explore safety measures and protocols when working with robots to prevent accidents.

These topics provide a gentle introduction to robotics research and are ideal for beginners looking to learn more about this exciting field.

Latest Research Topics in Robotics

The field of robotics is ever-evolving, with a plethora of exciting research topics at the forefront of exploration. Here are some of the latest and most intriguing areas of research in robotics:

Soft Robotics

Soft robots, crafted from flexible materials, can adapt to their surroundings, making them safer for human interaction and ideal for unstructured environments.

Robotic Swarms

Groups of robots working collectively toward a common objective, such as search and rescue missions, disaster relief efforts, and environmental monitoring.

Robotic Exoskeletons

Wearable devices designed to enhance human strength and mobility, offering potential benefits for individuals with disabilities, boosting worker productivity, and aiding soldiers in carrying heavier loads.

Medical Robotics

Robots play a vital role in various medical applications, including surgery, rehabilitation, and drug delivery, enhancing precision, reducing human error, and advancing healthcare practices.

Intelligent Robots

Intelligent robots have the ability to learn and adapt to their surroundings, enabling them to tackle complex tasks and interact naturally with humans.

These are just a glimpse of the thrilling research avenues within robotics. As the field continues to progress, we anticipate witnessing even more groundbreaking advancements and innovations in the years ahead.

What topics are in robotics?

Robotics basics.

Understanding the fundamental concepts of robotics, including robot components, kinematics, and control systems.

Robotics History

Exploring the historical development of robotics and its evolution into a multidisciplinary field.

Robot Sensors

Studying the various sensors used in robots for perception, navigation, and interaction with the environment.

Robot Actuators

Learning about the mechanisms and motors that enable robot movement and manipulation.

Robot Control

Understanding how robots are programmed and controlled, including topics like motion planning and trajectory generation.

Robot Mobility

Examining the different types of robot mobility, such as wheeled, legged, aerial, and underwater robots.

Artificial Intelligence in Robotics

Exploring the role of AI and machine learning in enhancing robot autonomy, decision-making, and adaptability.

Human-Robot Interaction

Investigating how robots can effectively interact with humans, including social and ethical considerations.

Robot Perception

Studying computer vision and other technologies that enable robots to perceive and interpret their surroundings.

Robotic Manipulation

Delving into robot arms, grippers, and manipulation techniques for tasks like object grasping and assembly.

Robot Localization and Mapping

Understanding methods for robot localization (knowing their position) and mapping (creating maps of their environment).

Robotics in Medicine

Exploring the use of robots in surgery, rehabilitation, and medical applications.

Analyzing the role of robots in manufacturing and automation, including industrial robot arms and cobots.

Learning about robots capable of making decisions and navigating autonomously in complex environments.

Robot Ethics

Examining ethical considerations related to robotics, including issues of privacy, safety, and AI ethics.

Exploring robots inspired by nature, such as those mimicking animal locomotion or behavior.

Robotic Applications

Investigating specific applications of robots in fields like agriculture, space exploration, entertainment, and more.

Robotics Research Trends

Staying updated on the latest trends and innovations in the field, such as soft robotics, swarm robotics, and intelligent agents.

These topics represent a broad spectrum of areas within robotics, each offering unique opportunities for research, development, and exploration.

What are your 10 robotics ideas?

Home assistant robot.

Build a robot that can assist with everyday tasks at home, like fetching objects, turning lights on and off, or even helping with cleaning.

Robotics in Agriculture

Create a robot for farming tasks, such as planting seeds, monitoring crop health, or even autonomous weed removal.

Autonomous Delivery Robot

Design a robot capable of delivering packages or groceries autonomously within a neighborhood or urban environment.

Search and Rescue Robot

Develop a robot that can navigate disaster-stricken areas to locate and assist survivors or relay important information to rescuers.

Robot Artist

Build a robot that can create art, whether it’s through painting, drawing, or even sculpture.

Underwater Exploration Robot

Construct a remotely operated vehicle (ROV) for exploring the depths of the ocean and gathering data on marine life and conditions.

Robot for the Elderly

Create a companion robot for the elderly that can provide companionship, reminders for medication, and emergency assistance.

Educational Robot

Design a robot that can teach coding and STEM concepts to children in an engaging and interactive way.

Robotics in Space

Develop a robot designed for space exploration, such as a planetary rover or a robot for asteroid mining.

Design a lifelike robotic pet that can offer companionship and emotional support, especially for those unable to care for a real pet.

These project ideas span various domains within robotics, from practical applications to creative endeavors, offering opportunities for innovation and exploration.

What are the 7 biggest challenges in robotics?

Robot autonomy.

Imagine robots that can think for themselves, make decisions, and navigate complex, ever-changing environments like a seasoned explorer.

Robotic Senses

Picture robots with superhuman perception, able to see, hear, and understand the world around them as well as or even better than humans.

Human-Robot Harmony

Think of robots seamlessly working alongside us, understanding our needs, and being safe, friendly, and helpful companions.

Robotic Hands and Fingers

Envision robots with the dexterity of a skilled surgeon, capable of handling delicate and complex tasks with precision.

Robots on the Move

Imagine robots that can gracefully traverse all kinds of terrain, from busy city streets to rugged mountain paths.

Consider the ethical questions surrounding robots, like privacy, fairness, and the impact on employment, as we strive for responsible and beneficial AI.

Robot Teamwork

Visualize a world where robots from different manufacturers can effortlessly work together, just like a symphony orchestra playing in perfect harmony.

What are the 5 major fields of robotics?

Industrial wizards.

Think of robots working tirelessly on factory floors, welding, assembling, and ensuring top-notch quality in the products we use every day.

Helpful Companions

Imagine robots assisting us in non-industrial settings, from healthcare, where they assist in surgery and rehabilitation, to our homes, where they vacuum our floors and make life a little easier.

Mobile Marvels

Picture robots that can move and navigate on their own, exploring uncharted territories in space, performing search and rescue missions, or even delivering packages to our doorstep.

Human-Like Helpers

Envision robots that resemble humans, not just in appearance but also in their movements and interactions. These are the robots designed to understand and assist us in ways that feel natural.

AI-Powered Partners

Think of robots that aren’t just machines but intelligent partners. They learn from experience, adapt to different situations, and make decisions using cutting-edge artificial intelligence and machine learning.

Let’s wrap up our robotics research topics. As we have seen that there is endless innovation in robotics research topics. That is why there are lots of robotics research topics to explore.

As the technology is innovating everyday and continuously evolving there are lots of new challenges and discoveries are emerging in the world of robotics.

With these robotics research topics you would explore a lot about the future endeavors of robotics. These topics would also tap on your creativity and embrace your knowledge about robotics. So let’s implement these topics and feel the difference.

Frequently Asked Questions

How can i get involved in robotics research.

To get started in robotics research, you can pursue a degree in robotics, computer science, or a related field. Join robotics clubs, attend conferences, and seek out research opportunities at universities or tech companies.

Are there any ethical concerns in robotics research?

Yes, ethical concerns in robotics research include issues related to job displacement, privacy, and the use of autonomous weapons. Researchers are actively addressing these concerns to ensure responsible development.

What are the career prospects in robotics research?

Robotics research offers a wide range of career opportunities, including robotics engineer, AI specialist, data scientist, and robotics consultant. The field is constantly evolving, creating new job prospects.

How can robotics benefit society?

Robotics can benefit society by improving healthcare, increasing manufacturing efficiency, conserving the environment, and aiding in disaster response. It has the potential to enhance various aspects of our lives.

What is the role of AI in robotics research?

AI plays a crucial role in robotics research by enabling robots to make intelligent decisions, adapt to changing environments, and perform complex tasks. AI and robotics are closely intertwined, driving innovation in both fields.

australia (2)
duolingo (13)
Education (284)
General (78)
How To (18)
IELTS (127)
Latest Updates (162)
Malta Visa (6)
Permanent residency (1)
Programming (31)
Scholarship (1)
Sponsored (4)
Study Abroad (187)
Technology (12)
work permit (8)

101+ Simple Robotics Research Topics For Students

Imagine a world where machines come to life, performing tasks on their own or assisting humans with precision and efficiency. This captivating realm is the heart of robotics—a fusion of engineering, computer science, and technology. If you’re a student eager to dive into this mesmerizing field, you’re in for an electrifying journey.

In this blog, we’ll unravel the secrets of robotics research, highlight its significance, and unveil an array of interesting robotics research topics. These topics are perfect for middle and high school students, making the exciting world of robotics accessible to all. Let’s embark on this adventure into the future of technology and innovation!

In your quest to explore robotics, don’t forget the valuable support of services like Engineering Assignment Help . Dive into these fascinating research topics and let us assist you on your educational journey

What is Robotics Research Topic?

Table of Contents

A robotics research topic is a specific area of study within the field of robotics that students can investigate to gain a deeper understanding of how robots work and how they can be applied to various real-world problems. These topics can range from designing and building robots to exploring the algorithms and software that control them.

Research topics in robotics can be categorized into various subfields, including:

Mechanical Design: Studying how to design and build the physical structure of robots, including their components and materials.
Sensors and Perception: Investigating how robots can sense and understand their environment through sensors like cameras, infrared sensors, and ultrasonic sensors.
Control Systems: Exploring the algorithms and software that enable robots to move, make decisions, and interact with their surroundings.
Human-Robot Interaction: Researching how robots can collaborate with humans, including topics like natural language processing and gesture recognition.
Artificial Intelligence (AI): Studying how AI techniques can be applied to robotics, such as machine learning for object recognition and path planning.
Applications: Focusing on specific applications of robotics, such as medical robotics, autonomous vehicles, and industrial automation.

Why is Robotics Research Important?

Before knowing robotics research topics, you need to know the reasons for the importance of robotics research. Robotics research is crucial for several reasons:

Advancing Technology

Research in robotics leads to the development of cutting-edge technologies that can improve our daily lives, enhance productivity, and solve complex problems.

Solving Real-World Problems

Robotics can be applied to address various challenges, such as environmental monitoring, disaster response, and healthcare assistance.

Inspiring Innovation

Engaging in robotics research encourages creativity and innovation among students, fostering a passion for STEM (Science, Technology, Engineering, and Mathematics) fields.

Educational Benefits

Researching robotics topics equips students with valuable skills in problem-solving, critical thinking, and teamwork.

Career Opportunities

A strong foundation in robotics can open doors to exciting career opportunities in fields like robotics engineering, AI, and automation.

Also Read: Quantitative Research Topics for STEM Students

Easy Robotics Research Topics For Middle School Students

Let’s explore some simple robotics research topics for middle school students:

Robot Design and Building

1. How to build a simple robot using household materials.

2. Designing a robot that can pick up and sort objects.

3. Building a robot that can follow a line autonomously.

4. Creating a robot that can draw pictures.

5. Designing a robot that can mimic animal movements.

6. Building a robot that can clean and organize a messy room.

7. Designing a robot that can water plants and monitor their health.

8. Creating a robot that can navigate through a maze of obstacles.

9. Building a robot that can imitate human gestures and movements.

10. Designing a robot that can assemble a simple puzzle.

11. Developing a robot that can assist in food preparation and cooking.

Robotics in Everyday Life

1. Exploring the use of robots in home automation.

2. Designing a robot that can assist people with disabilities.

3. How can robots help with chores and housekeeping?

4. Creating a robot pet for companionship.

5. Investigating the use of robots in education.

6. Exploring the use of robots for food delivery in restaurants.

7. Designing a robot that can help with grocery shopping.

8. Creating a robot for home security and surveillance.

9. Investigating the use of robots for waste recycling.

10. Designing a robot that can assist in organizing a bookshelf.

Robot Programming

1. Learning the basics of programming a robot.

2. How to program a robot to navigate a maze.

3. Teaching a robot to respond to voice commands.

4. Creating a robot that can dance to music.

5. Programming a robot to play simple games.

6. Teaching a robot to recognize and sort recyclable materials.

7. Programming a robot to create art and paintings.

8. Developing a robot that can give weather forecasts.

9. Creating a robot that can simulate weather conditions.

10. Designing a robot that can write and print messages or drawings.

Robotics and Nature

1. Studying how robots can mimic animal behavior.

2. Designing a robot that can pollinate flowers.

3. Investigating the use of robots in wildlife conservation.

4. Creating a robot that can mimic bird flight.

5. Exploring underwater robots for marine research.

6. Investigating the use of robots in studying insect behavior.

7. Designing a robot that can monitor and report air quality.

8. Creating a robot that can mimic the sound of various birds.

9. Studying how robots can help in reforestation efforts.

10. Investigating the use of robots in studying coral reefs and marine life.

Robotics and Space

1. How do robots assist astronauts in space exploration?

2. Designing a robot for exploring other planets.

3. Investigating the use of robots in space mining.

4. Creating a robot to assist in space station maintenance.

5. Studying the challenges of robot communication in space.

6. Designing a robot for collecting samples on other planets.

7. Creating a robot that can assist in assembling space telescopes.

8. Investigating the use of robots in space agriculture.

9. Designing a robot for space debris cleanup.

10. Studying the role of robots in exploring and mapping asteroids.

These robotics research topics offer even more exciting opportunities for middle school students to explore the world of robotics and develop their research skills.

Latest Robotics Research Topics For High School Students

Let’s get started with some robotics research topics for high school students:

Advanced Robot Design

1. Developing a robot with human-like facial expressions.

2. Designing a robot with advanced mobility for rough terrains.

3. Creating a robot with a soft, flexible body.

4. Investigating the use of drones in agriculture.

5. Developing a bio-inspired robot with insect-like capabilities.

6. Designing a robot with the ability to self-repair and adapt to damage.

7. Developing a robot with advanced tactile sensing for delicate tasks.

8. Creating a robot that can navigate both underwater and on land seamlessly.

9. Investigating the use of drones in disaster response and relief efforts.

10. Designing a robot inspired by cheetahs for high-speed locomotion.

11. Developing a robot that can assist in search and rescue missions in extreme weather conditions, such as hurricanes or wildfires.

Artificial Intelligence and Robotics

1. How can artificial intelligence enhance robot decision-making?

2. Creating a robot that can recognize and respond to emotions.

3. Investigating ethical concerns in AI-driven robotics.

4. Developing a robot that can learn from its mistakes.

5. Exploring the use of machine learning in robotic vision.

6. Exploring the role of AI-driven robots in space exploration and colonization.

7. Creating a robot that can understand and respond to human emotions in healthcare.

8. Investigating the ethical implications of autonomous vehicles in urban transportation.

9. Developing a robot that can analyze and predict weather patterns using AI.

10. Exploring the use of machine learning to enhance robotic prosthetics.

Human-Robot Interaction

1. Studying the impact of robots on human mental health.

2. Designing a robot that can assist in therapy sessions.

3. Investigating the use of robots in elderly care facilities.

4. Creating a robot that can act as a language tutor.

5. Developing a robot that can provide emotional support.

6. Studying the psychological impact of humanoid robots in educational settings.

7. Designing a robot that can assist individuals with neurodegenerative diseases.

8. Investigating the use of robots for mental health therapy and counseling.

9. Creating a robot that can help children with autism improve social skills.

10. Developing a robot companion for the elderly to combat loneliness.

Robotics and Industry

1. How are robots transforming the manufacturing industry?

2. Investigating the use of robots in 3D printing.

3. Designing robots for warehouse automation.

4. Developing robots for precision agriculture.

5. Studying the role of robotics in supply chain management.

6. Exploring the integration of robots in the construction and architecture industry.

7. Investigating the use of robots for recycling and waste management in cities.

8. Designing robots for autonomous maintenance and repair of industrial equipment.

9. Developing robotic solutions for monitoring and managing urban traffic.

10. Studying the role of robotics in the development of smart factories and Industry 4.0.

Cutting-Edge Robotics Applications

1. Exploring the use of swarm robotics for search and rescue missions.

2. Investigating the potential of exoskeletons for enhancing human capabilities.

3. Designing robots for autonomous underwater exploration.

4. Developing robots for minimally invasive surgery.

5. Studying the ethical implications of autonomous military robots.

6. Exploring the use of robotics in sustainable energy production.

7. Investigating the use of swarming robots for ecological conservation and monitoring.

8. Designing exoskeletons for individuals with mobility impairments for daily life.

9. Developing robots for autonomous planetary exploration beyond our solar system.

10. Studying the ethical and legal aspects of AI-powered military robots in warfare.

These robotics research topics offer high school students the opportunity to delve deeper into advanced robotics concepts and address some of the most challenging and impactful issues in the field.

Robotics research is a captivating field with a wide range of robotics research topics suitable for students of all ages. Whether you’re in middle school or high school, you can explore robot design, programming, AI integration , and cutting-edge applications. Robotics research not only fosters innovation but also prepares you for a future where robots will play an increasingly important role in various aspects of our lives. So, pick a topic that excites you, and embark on your journey into the fascinating world of robotics!

I hope you enjoyed this blog about robotics research topics for middle and high school students.

How to write the best quality programming assignment?

Here in this blog, Codeavail programming assignment help experts will help you know the best steps of how to write assignment and explain to you…

How to do my assignment | Good Assignment Writing Tips

When you ask CodeAvail experts how to do my assignment, Our assignment writing experts will motivate you and provide the best ways to do the…

Information

Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

Active Journals
Find a Journal
Proceedings Series
For Authors
For Reviewers
For Editors
For Librarians
For Publishers
For Societies
For Conference Organizers
Open Access Policy
Institutional Open Access Program
Special Issues Guidelines
Editorial Process
Research and Publication Ethics
Article Processing Charges
Testimonials
Preprints.org
SciProfiles
Encyclopedia

Article Menu

Subscribe SciFeed
Recommended Articles
Google Scholar
on Google Scholar
Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Research on lstm-based maneuvering motion prediction for usvs.

1. Introduction

2. sample data set construction and data processing, 2.1. the maneuvering motion model of usv, 2.2. sample data acquisition, 2.3. sample data processing, 3. lstm-based usv motion black-box prediction model, 4. analysis on network structure and simulation, 4.1. discussion of network structure, 4.2. discussion of training settings, 4.3. comparing simulations, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest, abbreviations.

USVs	Unmanned Surface Vehicles
MMG	Maneuvering Modeling Group
SVM	Support Vector Machines
3DoF	Three-Degree-of-Freedom
WAM-V	Wave Adaptive Modular Vessel
CFD	Computational Fluid Dynamics
SVR	Support Vector Regression
LSSVM	Least Squares Support Vector Machines
WNN	Wavelet Neural Networks
BP	Back Propagation
WEC	Wave Energy Converter
LSTM	Long Short-Term Memory Network
OU	Ornstein–Uhlenbeck
HMM	Hidden Markov Model
RNN	Recurrent Neural Network
RMSE	Root Mean Square Error
CC	Correlation Coefficient

Wang, S. Research on development status and combat applications of USVs in worldwide. Command. Syst. 2019 , 44 , 11–15. [ Google Scholar ]
Yuan, X.Y. Hierarchical model identification method for unmanned surface vehicle. J. Shanghai Univ. (Nat. Sci.) 2020 , 26 , 896–908. [ Google Scholar ]
Abkowitz, M.A. Measurement of hydrodynamic characteristics from ship maneuvering trials by system identification. Trans. Soc. Nav. Archit. Mar. Eng. 1980 , 88 , 283–318. [ Google Scholar ]
Liu, Y.; Zou, L.; Zou, Z.; Guo, H.P. Predictions of ship maneuverability based on virtual captive model tests. Eng. Appl. Comput. Fluid Mech. 2018 , 12 , 334–353. [ Google Scholar ] [ CrossRef ]
Xu, H.; Soares, C.G. Hydrodynamic coefficient estimation for ship manoeuvring in shallow water using an optimal truncated LS-SVM. Ocean Eng. 2019 , 191 , 106488. [ Google Scholar ] [ CrossRef ]
He, H.; Wang, Z.; Zou, Z.; Liu, Y. System Identification Based on Completely Connected Neural Networks for Black-Box Modeling of Ship Maneuvers. In Advances in Guidance, Navigation and Control ; Lecture Notes in Electrical Engineering; Springer: Singapore, 2022; Volume 644. [ Google Scholar ] [ CrossRef ]
Zhang, X.; Zou, Z. Identification of models of ship manoeuvring motion using Support Vector Regression and Particle Swarm Optimization. J. Ship Mech. 2016 , 20 , 1427–1432. [ Google Scholar ] [ CrossRef ]
Xu, H.; Hinostroza, M.A.; Wang, Z.; Guedes Soares, C. Experimental investigation of shallow water effect on vessel steering model using system identification method. Ocean Eng. 2020 , 199 , 106940. [ Google Scholar ] [ CrossRef ]
Xu, H.; Hassani, V.; Soares, C.G. Uncertainty analysis of the hydrodynamic coefficients estimation of a nonlinear manoeu-vring model based on planar motion mechanism tests. Ocean Eng. 2019 , 173 , 450–459. [ Google Scholar ] [ CrossRef ]
Pandey, J.; Hasegawa, K. Study on turning manoeuvre of catamaran surface vessel with a combined experimental and simulation method. IFAC-PapersOnLine 2016 , 49 , 446–451. [ Google Scholar ] [ CrossRef ]
Luo, W.; Zhang, Z. Modeling of ship maneuvering motion using neural networks. J. Mar. Sci. Appl. 2016 , 15 , 426–432. [ Google Scholar ] [ CrossRef ]
Carrillo, S.; Contreras, J. Obtaining first and second order nomoto models of a fluvial support patrol using identification techniques. Ship Sci. Technol. 2018 , 11 , 19–28. [ Google Scholar ] [ CrossRef ]
Liu, C.D.; Zhang, H.; Han, Y.; Shi, C. Black-box modeling and prediction of ship maneuverability based on Least Square Support Vector Machine. J. Ship Mech. 2013 , 17 , 872–877. [ Google Scholar ] [ CrossRef ]
Xu, F.; Chen, Q.; Zhou, Z. Modeling of Underwater Vehicles’ Maneuvering Motion by Using Integral Sample Structure for Identification. J. Ship Mech. 2014 , 211–220. [ Google Scholar ]
Bonci, M.; Viviani, M.; Broglia, R.; Dubbioso, G. Method for estimating parameters of practical ship manoeuvring models based on the combination of RANSE computations and System Identification. Appl. Ocean Res. 2015 , 52 , 274–294. [ Google Scholar ] [ CrossRef ]
Gupta, P.; Rasheed, A.; Steen, S. Ship performance monitoring using machine-learning. Ocean Eng. 2022 , 254 , 111094. [ Google Scholar ] [ CrossRef ]
Zhang, Y.Y.; Wang, Z.H.; Zou, Z.J. Black-box modeling of ship maneuvering motion based on multi-output nu-support vector regression with random excitation signal. Ocean Eng. 2022 , 257 , 111279. [ Google Scholar ] [ CrossRef ]
Song, L.; Hao, L.; Tao, H.; Xu, C.; Guo, R.; Li, Y.; Yao, J. Research on Black-Box Modeling Prediction of USV Maneuvering Based on SSA-WLS-SVM. J. Mar. Sci. Eng. 2023 , 11 , 324. [ Google Scholar ] [ CrossRef ]
He, H.; Zou, Z. Black-Box Modeling of Ship Maneuvering Motion Using System Identification Method Based on BP Neural Network. In Proceedings of the ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering, Virtual, Online, 3–7 August 2020; Volume 6B: Ocean Engineering. [ Google Scholar ] [ CrossRef ]
Liu, Y.; Xue, Y.; Huang, S.; Xue, G.; Jing, Q. Dynamic Model Identification of Ships and Wave Energy Converters Based on Semi-Conjugate Linear Regression and Noisy Input Gaussian Process. J. Mar. Sci. Eng. 2021 , 9 , 194. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Scenarios	Conditions
	10°/10° Zigzag
	15°/15° Zigzag
	20°/20° Zigzag
	25° Turning
	30° Turning
	35° Turning
	10°/10° Zigzag
	15°/15° Zigzag
	20°/20° Zigzag
	25° Turning
	30° Turning
	35° Turning

Parameters on the X-Axis	Meaning
L	the number of LSTM layers
N	the number of neurons per layer
D	the dropout regularization rate
LR	the initial learning rate
T	the window function width

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

Guo, R.; Mao, Y.; Xiang, Z.; Hao, L.; Wu, D.; Song, L. Research on LSTM-Based Maneuvering Motion Prediction for USVs. J. Mar. Sci. Eng. 2024 , 12 , 1661. https://doi.org/10.3390/jmse12091661

Guo R, Mao Y, Xiang Z, Hao L, Wu D, Song L. Research on LSTM-Based Maneuvering Motion Prediction for USVs. Journal of Marine Science and Engineering . 2024; 12(9):1661. https://doi.org/10.3390/jmse12091661

Guo, Rong, Yunsheng Mao, Zuquan Xiang, Le Hao, Dingkun Wu, and Lifei Song. 2024. "Research on LSTM-Based Maneuvering Motion Prediction for USVs" Journal of Marine Science and Engineering 12, no. 9: 1661. https://doi.org/10.3390/jmse12091661

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

IMAGES

research paper on artificial intelligence and robotics pdf
(PDF) Research Paper on Robotics-New Era
Robotics Essay
(PDF) Artificial Intelligence and Robotics
research paper on artificial intelligence and robotics pdf
research paper on artificial intelligence and robotics pdf

VIDEO

Loading and Unloading for Sample Testing in Pharmaceutical Industry
Automated sample reception with AI robotics
ROBOTC for VEX Robotics 4.0
Replicate work on "Robots that can adapt like animals" ~intro~
Sample Research Paper
Sample Research Paper Topics and Titles

COMMENTS

500 research papers and projects in robotics
In this post, we have listed 500+ recent research papers and projects for those who are interested in robotics. These free, downloadable research papers can shed lights into the some of the complex areas in robotics such as navigation, motion planning, robotic interactions, obstacle avoidance, actuators, machine learning, computer vision ...
(Pdf) Artificial Intelligence in Robotics: From Automation to
This research paper explores the integration of artificial intelligence (AI) in robotics, specifically. focusing on the transition from automation to autonomous systems. The paper provides an ...
Free Sample Articles from Science Robotics
Research Articles. Deployment of an electrocorticography system with a soft robotic actuator. Sukho Song, Florian Fallegger, Alix Trouillet, Kyungjin Kim, Stéphanie P. Lacour. Science Robotics | Vol 8, Issue 78. Robust flight navigation out of distribution with liquid neural networks. Makram Chahine, Ramin Hasani, Patrick Kao, Aaron Ray, Ryan ...
Review of Robotics Technologies and Its Applications
Based on a brief introduction of the development history of robotics, this paper reviews the classification of the type of robots, the key technologies involved, and the applications in various fields, analyze the development trend of robotics and recent research hotspots, and provides an outlook on the future development of robotics and its ...
(PDF) Advanced Applications of Industrial Robotics: New Trends and
robotics, were used to survey published papers over a four-year period. Because this is a Because this is a widely researched and dynamic area, the review focused on a relatively short time period
Free Sample Articles from Science Robotics
The Science Robotics sample articles below are freely available to all readers. EDITORIALS. Combating COVID-19—The role of robotics in managing public health and infectious diseases. By Guang-Zhong Yang, Bradley J. Nelson, Robin R. Murphy, Howie Choset, Henrik Christensen, Steven H. Collins, Paolo Dario, Ken Goldberg, Koji Ikuta, Neil Jacobstein, Danica Kragic, Russell H. Taylor and Marcia ...
The International Journal of Robotics Research: Sage Journals
International Journal of Robotics Research (IJRR) was the first scholarly publication on robotics research; it continues to supply scientists and students in robotics and related fields - artificial intelligence, applied mathematics, computer science, electrical and mechanical engineering - with timely, multidisciplinary material... This journal is peer-reviewed and is a member of the ...
Reinforcement learning for robot research: A comprehensive review and
Reinforcement learning (RL), 1 one of the most popular research fields in the context of machine learning, effectively addresses various problems and challenges of artificial intelligence. It has led to a wide range of impressive progress in various domains, such as industrial manufacturing, 2 board games, 3 robot control, 4 and autonomous driving. 5 Robot has become one of the research hot ...
Robotics
This literature review presents a comprehensive analysis of the use and potential application scenarios of collaborative robots in the industrial working world, focusing on their impact on human work, safety, and health in the context of Industry 4.0. The aim is to provide a holistic evaluation of the employment of collaborative robots in the current and future working world, which is being ...
Selected Topics of Artificial Intelligence in Robotics
The paper is focused on the selected area of artificial intelligence (AI) applications, and presents only two examples relating to robotics and robotic systems. It is particularly interested in swarm intelligence method applied to path planning problems, and machine and deep learning approaches to anomaly detection. Based on existing methods of AI, we discuss important aspects for the ...
Robotics
Robotics. Having a machine learning agent interact with its environment requires true unsupervised learning, skill acquisition, active learning, exploration and reinforcement, all ingredients of human learning that are still not well understood or exploited through the supervised approaches that dominate deep learning today.
(PDF) Research Paper on Robotics-New Era
Student. , M.Sc. I.T., I.C.S. College, Khed, Ratnagri. Abstract: This paper contains of detailed statistics about the robot's method and system. As one and all knows, how artificial. intelligence ...
Autonomous robotics is driving Perseverance rover's ...
The NASA Mars 2020 Perseverance rover and Ingenuity helicopter landed in the Jezero crater on 18 February 2021. This paper discusses the autonomous capability that has enabled the mission to achieve its prime objective to select, document, core, and deploy a high-value sample collection on the surface of Mars within one Mars year of landing (one Mars year is 687 Earth days).
Human robot interaction in industrial robotics. Examples from research
Contrary to this typical industrial situation, current research activities in industrial robotics are focused on human robot interaction and safety, to carry out added value operations that need to be performed both the human and the robot working together. ... This paper outlines real research cases of this latest type, which have as the final ...
Robotics and Autonomous Systems
Affiliated with the Intelligent Autonomous Systems (IAS) Society. Robotics and Autonomous Systems will carry articles describing fundamental developments in the field of robotics, with special emphasis on autonomous systems. An important goal of this journal is to extend the state of the art in both …. View full aims & scope.
Frontiers in Robotics and AI
1,009 views. A multidisciplinary journal focusing on the theory of robotics, technology, and artificial intelligence, and their applications - from biomedical to space robotics.
Exploring the impact of Artificial Intelligence and robots on higher
AI and robotics in research: Fiction 5, "The Research Management Suite TM" So far in this paper most of the focus has been on the application of AI and robotics to learning. AI also has applications in university research, but it is an area far less commonly considered than learning and teaching.
Robotics Research Papers
This paper gives brief introduction about various sensors used in robotics and their applications. A sensor is a device that detects the changes in electrical or physical or other quantities and thereby produces an output and whose... more. Download. by J4R - Journal for Research.
(PDF) The evolution of robotics research
This article addresses the evolution of r obotics research. in three different areas: r obot manipulators, mobile robots, and biologically inspired robots. Although these three ar eas. share some ...
Topics for Research in Robotics and Intelligent Systems
Robotic devices and systems. Autonomous air, sea, undersea, and land vehicles. Space exploration and development. Intelligent control systems. Biomimetic modeling, dynamics, and control. Cooperating robots for manufacturing and assembly. Cooperative control of natural and engineered groups. Identification of dynamic system models.
200+ Robotics Research Topics: Discovering Tomorrow's Tech
September 15, 2023. Explore cutting-edge robotics research topics and stay ahead of the curve with our comprehensive guide. Discover the latest advancements in the field today. Robotics research topics are not like any other research topics. In these topics science fiction meets reality and innovation knows no bounds.
101+ Simple Robotics Research Topics For Students
Easy Robotics Research Topics For Middle School Students. Let's explore some simple robotics research topics for middle school students: Robot Design and Building. 1. How to build a simple robot using household materials. 2. Designing a robot that can pick up and sort objects. 3. Building a robot that can follow a line autonomously. 4.
(PDF) Robotic Process Automation
Abstract and Figures. Within digital transformation, which is continuously progressing, robotic process automation (RPA) is drawing much corporate attention. While RPA is a popular topic in the ...
Research on LSTM-Based Maneuvering Motion Prediction for USVs
Maneuvering motion prediction is central to the control and operation of ships, and the application of machine learning algorithms in this field is increasingly prevalent. However, challenges such as extensive training time, complex parameter tuning processes, and heavy reliance on mathematical models pose substantial obstacles to their application. To address these challenges, this paper ...

Artificial Intelligence

Machine Learning

Top AI meeting assistants for effortless recording, transcription, and summarization

500 research papers and projects in robotics – Free Download

Navigation and Motion Planning

Robotic Interactions

Obstacle Avoidance

Computer Vision

Probabilistic Programming

Collaborative Robotics

Mobile Robotics

Nano robotics

Social Robotics

Humanoid robot

Cloud Robotics

Swarm Robotics

Soft Robotics

Service Robotics

Construction

Defence and Military

Space Robotics

Other Industries

Future of Robotics / Trends

RELATED ARTICLES MORE FROM AUTHOR

Recent Publications

IEEE Account

Purchase Details

Profile Information

Exploring the impact of Artificial Intelligence and robots on higher education through literature-based design fictions

Introduction

Method: design fictions

AI and robots in learning: Fiction 1, “AIDan, the teaching assistant”

Social robots in learning: Fiction 2, “Footbotball”

Fiction 3: “Criticalbot in conversation”

The intelligent campus: Fiction 4, “The intelligent campus app”

AI and robotics in research: Fiction 5, “The Research Management Suite TM”

Tackling pragmatic barriers: Fiction 6, “Verbatim minutes of University AI project steering committee: AI implementation phase 3”

Learning analytics and datafication: Fiction 7, “Dashboards”

Infrastructure: Fiction 8, “Minnie—the AI admin assistant”

Conclusion: using the fictions together

Availability of data and materials

Acknowledgements

Author information

Contributions

Corresponding author

Ethics declarations

Additional information

Rights and permissions

About this article

Share this article

Robotics and Intelligent Systems Certificate Program

Chemical and Biological Engineering

Civil and Environmental Engineering

Computer Science

Electrical Engineering

Mechanical and Aerospace Engineering

Operations Research and Financial Engineering

200+ Robotics Research Topics: Discovering Tomorrow’s Tech

Robotics Research Topics

Autonomous Robots

Robot Manipulation and Grasping

Robot Vision and Perception:

Human-Robot Collaboration

Bio-Inspired Robotics

Robot Learning and AI

Robotics in Healthcare

Robots in Industry

Robots in Space Exploration

Environmental Robotics

Robotics Research Topics for high school students

Easy Robotics Research Topics

History of Robotics

Robotic Sensors

Simple Robot Building

Programming a Robot

Robots in Entertainment

Robotics in Toys

Robotic Pets

Robotics in Science Fiction

Robotic Safety