Individual Report – AI Model Implementation
A 2500-word individual report requiring technical literature review, AI model implementation and analysis, weighted at 50% meeting Learning Outcomes 1 and 2. Assessing AHEP 4 Outcomes: C2, C3, C4, C5.

Group Report – Autonomous Robot Control Project
A 3000-word group report on autonomous robot control including reflection on societal impacts and responsible deployment of autonomous technologies, weighted at 50% meeting Learning Outcomes 3 and 4. Assessing AHEP 4 Outcomes: C5, C7, C8, C16.

Formative assessment to include the review of a journal paper on a chosen AI application.

This module equips students with knowledge and understanding of advanced AI and ML techniques, and their use in a range of real-world applications. The module will also focus on the use of such techniques for robotics systems, focusing on perception, control, and real-world deployment.

The module will cover the following topics:
- Core AI algorithms: Machine learning, and deep learning including their use in real-world applications
- AI Foundations for Robotics: Deep learning models (such as CNN for vision, LSTM for temporal control), Mathematical foundations for robotic perception and decision-making.
- Robotics Perception & Control: Computer vision, Object detection, SLAM (Simultaneous Localisation and Mapping), and motion planning algorithms.
- Robot Operating System (ROS): Node communication, sensor data fusion, AI-robotics integration, Application of reinforcement learning in robot control (such as DQN, PPO algorithms), human-robot interaction and collaborative robots (HRI).
- Ethics & Safety: Ethical challenges of AI robots.

1. Demonstrate a comprehensive understanding of contemporary Artificial Intelligence (AI) applications and Machine Learning (ML) techniques informed by the investigation and evaluation of current research and technical literature. (C2, C3, C4)

Learning Outcome: Knowledge & understanding, Research skills

2. Critically analyse a real-world application-based scenario, select an appropriate ML technique to implement an AI-based solution and critically appraise the solution. (C2, C4, C5)

Learning Outcome: Application & problem-solving, Digital skills

3. Collaboratively design and implement AI-driven solutions for robotic perception and control, critically evaluating the performance in real-world scenarios including autonomous navigation and object manipulation. (C5, C16)

Learning Outcome: Critical reasoning & collaboration

4. Apply ethical and safety considerations to AI-robotics systems, reflecting on societal impacts and responsible deployment of autonomous technologies. (C5, C7, C8)

Learning Outcome: Reflection

Whole group lectures will be used to deliver new material and to consolidate previous material. Small-group tutorials, with activities designed to enhance the understanding of the material delivered in the lectures, will be used to apply the skills and knowledge learned. A mixture of classroom based, and practical activities will take place supported by staff.

- Suitable software tools and programming language compilers such as Python (NumPy, OpenCV), PyTorch/TensorFlow, Gazebo, or equivalent
- Suitable hardware/software facilities for robotics

Must be registered on BEng (Hons) Electronic and Information Engineering provision at XUPT, China.

Babu, B.S. and Kaushik, K. (2022) Industrial Automation and Robotics: Techniques and Applications. 1st Ed. Boca Raton, FL: CRC Press.

Khanna, V.K. (2025) AI Robotics: Ethics, Algorithms, and Technology of Artificial Intelligence-Powered Robots. 1st Ed. Boca Raton, FL: CRC Press.

Russell, S. and Norvig, P. (2020) Artificial Intelligence: A Modern Approach. 4th Ed. Harlow: Pearson Education.

Sutton, R.S. and Barto, A.G. (2018) Reinforcement Learning: An Introduction. 2nd Ed. Cambridge, MA: The MIT Press.

Thrun, S., Burgard, W. and Fox, D. (2005) Probabilistic Robotics. Cambridge, MA: The MIT Press.

The module will introduce the principles of AI Techniques and Robotics. Learners will be able to gain a comprehensive understanding of the use of AI techniques in a range of real-world applications. The module will also provide hands-on experience in designing AI-driven solutions for a range of autonomous robots, addressing engineering challenges and ethical considerations.