Robotics and autonomous systems are transforming industries ranging from manufacturing and logistics to healthcare, transportation, and exploration. In this module, you will learn the principles that enable intelligent agents and robots to perceive their environment, make decisions, and act autonomously in dynamic environments.
You will study agent architectures, autonomous decision making, sensing and actuation, robot locomotion, localisation, navigation, and mapping. The module places particular emphasis on dealing with uncertainty through probabilistic techniques such as Bayesian filters, particle filters, and Kalman filters, culminating in methods for simultaneous localisation and mapping (SLAM). Through practical work using a contemporary robotics simulation platform, you will develop and evaluate autonomous robotic behaviours in realistic environments.
By the end of the module, you will understand the design of intelligent autonomous agents, be able to analyse the challenges of perception and decision making under uncertainty, and develop software for robotic systems capable of performing autonomous tasks.