1. Demonstrate comprehensive understanding of advanced concepts related to autonomous and robotic systems. (AHEP 4: M1)

Knowledge and Understanding,

Analysis

2. Develop autonomous system-based solutions for real-life problems and apply appropriate analytical techniques to critically evaluate the methodologies, performance, reliability, and real-world applicability of systems. (AHEP 4: M5)

Knowledge and Understanding, Problem Solving, Analysis, Learning, Application

3. Critically appraise recent research developments and emerging technologies in autonomous systems. (AHEP 4: M4, M17)

Enquiry, Learning

4. Critically evaluate the societal, environmental, and ethical implications of an autonomous systems-based solution, including potential security risks. (AHEP 4: M7)

Enquiry, Analysis, Reflection

5. Demonstrate ability to compute kinematics and dynamics of a range of robotic systems. (AHEP 4: M1)

Analysis, Application

A 2500-word individual report weighted at 50%, assessing Learning Outcomes 2, 3 and 4. The report will require the development of a solution based on an autonomous system for a real-life application, including a critical assessment of the techniques, tools, and performance measures involved. It will include examining recent technological advances and research in the field, as well as reflecting on the broader implications of autonomous systems, including ethical, environmental, and security-related considerations. Meeting AHEP 4 Outcomes: M4, M5, M7, M17.

A 2-hour examination weighted at 50%, assessing Learning Outcomes 1 and 5. Several questions to be answered covering topics included in the module to demonstrate comprehensive knowledge and understanding of advanced concepts related to autonomous and robotic systems. Meeting AHEP 4 Outcome: M1.

Professional Body requirements mean that a minimum overall score of 50% is required to pass a module, with each element of assessment requiring a minimum mark of 40%.

This module explores the latest trends in research and the applications of autonomous systems, including aspects such as ethical decision-making. It will also provide an understanding of the levels of autonomy, and performance metrics. Additionally, the module will delve into the design, simulation, and testing of autonomous systems, providing an understanding of their classification, limitations, and interactions. The module further focuses on the intelligent control of autonomous vehicles, visual object detection and recognition for intelligent vehicles, cognition and decision-making in autonomous vehicles, and the development and validation of intelligent vehicle control systems. Additionally, the module covers machine learning within the context of autonomous systems.

In order to provide comprehensive understanding of achieving autonomy in industrial applications, the module explores advanced robotic systems and their components. Starting from coordinate transformation, kinematics and dynamics of robot manipulators will be covered in relation to their corresponding applications. Complex computation techniques such as Lagrange mechanics and Jacobian matrix calculation will be studied to uncover characteristics of a robot manipulator. Trajectory generation and optimisation as well as advanced control techniques will be considered.

Autonomous and robotic systems are at the core of intelligent technologies used in applications such as self-driving vehicles, industrial automation, smart robotics, and intelligent transportation systems. This module provides a study of modern autonomous and robotic systems, focusing on design, simulation, control, and intelligent decision-making. The module focuses on developing skills in areas such as machine learning for autonomy, robotic kinematics and dynamics, intelligent vehicle control, and system validation - all of which are highly sought after in the industry. It equips you with the knowledge and hands-on experience needed to address real-world challenges in the rapidly growing field of autonomous systems.

This module will enable students to gain understanding, apply knowledge, analyse and evaluate problems and create solutions through a variety of activities, including problem-based lectures, tutorials, laboratories and independent study.

Azar, A. T., & Koubaa, A. (2023). Artificial Intelligence for Robotics and Autonomous Systems Applications. Berlin/Heidelberg, Germany: Springer.

Corke, P. (2023). Robotics, Vision and Control: Fundamental Algorithms in Python (Vol. 146). Springer Nature.

Hopgood, A. A. (2021). Intelligent systems for engineers and scientists: a practical guide to artificial intelligence. CRC press.

Niku, S. B. (2020). Introduction to robotics: analysis, control, applications. John Wiley & Sons.

Raol, J. R., & Gopal, A. K. (2016). Mobile intelligent autonomous systems. CRC Press.

Yu, H., Li, X., & Murray, R. M., (2018); Safe, Autonomous and Intelligent Vehicles (Unmanned System Technologies), 1st edn. Springer.

PCs running MATLAB or equivalent software with Control Toolbox and Robotics Toolbox

Microcontrollers (such as Arduino Nano, ESP32 or equivalent)

Various sensors and other equipment such as Rechargeable Batteries, Motor Drivers, Motors, Camera, PCB Board, Vero Boards etc.