module specification

The "AI with ROS" module has been meticulously crafted to offer students a comprehensive hands-on experience in implementing cutting-edge AI algorithms. Designed to equip students with the skills required to thrive as ROS developers in the robotics industry, this module encompasses practical workshops focused on implementing SLAM and AutoNAV algorithms. Through immersive learning experiences, students will tackle diverse scenarios, honing their ability to implement various AI algorithms while enhancing critical thinking skills. Emphasizing the utmost importance of safety and compliance, students will navigate health and safety regulations pertinent to AI and robotics. This includes considerations for intellectual property rights and ethical guidelines, ensuring responsible and lawful implementation of algorithms. Distinguished as a pioneering initiative, this module prioritizes hands-on learning, providing students with unparalleled opportunities to engage directly with ROS development. Students undertaking this module will have the chance to cultivate their own ROS environments, facilitating the seamless integration of sensors and hardware within simulation environments tailored to specific applications. During the course of this module, students will also be given TurtleBots to implement their algorithms which they have simulated in a real-life environment. Through the "AI with ROS" module, students will not only gain practical expertise but also develop a profound understanding of the ethical, legal, and technical dimensions of AI and robotics.

CT5055

Basics of Ubuntu environment to run the latest version of ROS available during the academic term.

ROS architecture and commands: Master, Nodes, Topics, Messages, Services, Actions and commands (rostopic, rosservice, rosnode, roslaunch etc.,)

ROS topics and development terms in detail: ROS messages (msg), Publishers, Subscribers (pub/sub), catkin packages for creating a workspace.

Developing a complex ROS environment with specific robot and the surrounding requirements.

ROS Simulation tools: rviz, rqt, Gazebo etc.,

TurtleBot integration, TurtleBot ROS packages, TurtleBot sensor, configuring sensor data streams.

AI algorithms in ROS: SLAM, AutoNAV, Object Detection and Recognition etc.,

Configuring and tuning AI parameters for AI algorithms in ROS launch files.

Safety standards and guidelines in robotics industries such as ISO 13482

Program design, implantation, maintenance and safe disposal of end-of-life products. Laboratory skills and safety, LSEP: legal, social, ethical and professional issues in context of artificial intelligence and robotics industries.

Students enrolled in the "AI with ROS" module will embark on a journey of comprehensive understanding and hands-on exploration through a series of weekly lectures, tutorials, and supervised workshops tailored specifically to the integration of AI with Robotics within the ROS framework. These interactive sessions will leverage practical examples and case studies to illuminate core principles essential for robotics applications, with a strong emphasis on ROS-based development. Tailored workshops will immerse students in problem-solving scenarios drawn from real-life contexts, empowering them to develop and implement innovative solutions within the ROS ecosystem. Hands-on experiences with TurtleBots will be integrated into these workshops, providing students with practical exposure to ROS-based robotics development. Through these activities, students will sharpen their analytical skills and deepen their understanding of AI algorithms in the context of ROS-based robotic systems.

To enrich the learning experience, a diverse array of blended learning methodologies and cutting-edge technologies will be employed. Leveraging the University's Virtual Learning Environment (VLE), simulation tools, and state-of-the-art laboratory equipment, students will engage with course content dynamically. These tools will facilitate content delivery, foster active participation, administer formative and summative assessments, and provide timely feedback, thereby maximizing student engagement and optimizing learning outcomes.

Furthermore, students will be encouraged to maintain reflective portfolios documenting their learning journey and practical tasks, with a specific focus on ROS-based development. Both individual and collaborative efforts will be emphasized, as students collaborate to devise and implement innovative solutions to workshop exercises and coursework assignments. This collaborative approach will nurture teamwork and problem-solving skills crucial for success in the AI-based robotics domain within the ROS ecosystem.

On successful completion of this module students should be able to:

LO1. Create and configure ROS workspaces for specific projects, gaining skills in integrating sensors, actuators, and hardware components to build functional robotic systems.

LO2. Gain proficiency in configuring, operating, and programming TurtleBots in a ROS environment, deploying AI algorithms to solve real-world robotics challenges.

LO3. Develop teamwork skills through collaborative AI robotics projects, enhancing communication, problem-solving, project management, and fluency in ROS terminology for professional collaboration.

LO4. Examine the legal, social, and ethical implications of AI and robotics, gaining a thorough understanding of intellectual property rights, privacy issues, safety regulations, and ethical considerations in AI-driven robotics.

LO5. Critically analyse AI algorithms for robotics, assessing their performance, efficiency, and ethical implications to build essential skills in innovation and problem-solving

Core Text:

Anis Koubaa, (2021) Robot Operating System (ROS), The Complete Reference (Volume 6), Springer Cham, ISBN:978-3-030-75471-6

Ahmad Taher Azar, Anis Koubaa., (2023) Artificial Intelligence for Robotics and Autonomous Systems Applications, Springer Cham, ISBN: 978-3-031-28714-5

Other Text:

Rabindra Nath Shaw, Ankush Ghosh, Valentina Emilia Balas, Monica Bianchini., (2021) Artificial Intelligence for Future Generation Robotics, Elsevier, ISBN: 978-0-323-85498-6

Robin R. Murphy., (2019) Introduction to AI Robotics, Second Edition, The MIT Press, ISBN: 9780262038485

Miller R., Miller M., (2017) Robots and Robotics: Principles, Systems, and Industrial Applications, McGraw-Hill Education, ISBN-13: 978-1259859786

Vaish, D. (2018) Python Robotics Projects: Build smart and collaborative robots using Python, Packt Publishing, ISBN-13: 978-1788832922

Journals:

A. R. Khairuddin, M. S. Talib and H. Haron, "Review on simultaneous localization and mapping (SLAM)," 2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE), Penang, Malaysia, 2015, pp. 85-90, doi: 10.1109/ICCSCE.2015.7482163.

Chao Duan, Steffen Junginger, Jiahao Huang, Kairong Jin, Kerstin Thurow, Deep Learning for Visual SLAM in Transportation Robotics: A review, Transportation Safety and Environment, Volume 1, Issue 3, 12 December 2019, Pages 177–184, https://doi.org/10.1093/tse/tdz019

X. Ding, J. Guo, Z. Ren and P. Deng, "State-of-the-Art in Perception Technologies for Collaborative Robots," in IEEE Sensors Journal, vol. 22, no. 18, pp. 17635-17645, 15 Sept.15, 2022, doi: 10.1109/JSEN.2021.3064588.

Lindsay J. Robertson (2019), Engineering-Based Design Methodology for Embedding Ethics in Autonomous Robots, Proceedings of the IEEE, DOI: 10.1109/JPROC.2018.2889678

Websites:

University Library website- https://student.londonmet.ac.uk/library/

Electronic Databases:

IEEE Xplore / IET Digital Library (IEL) - https://ieeexplore.ieee.org/Xplore/home.jsp

Springer Nature - https://www.springernature.com/gp/products/books

Wiley Online Library - https://0-www-onlinelibrary-wiley-com.emu.londonmet.ac.uk/