Intelligent Mobile Robotics (MSc RAS)

Learning Outcomes

The module has two series of lectures and associated practicals. Lectures teach the theory and highlight areas of additional reading. Practicals implement methods taught in the lectures on a small robotic platform. Practical sessions are in small teams, and consist of laboratory based tutorials and design workshops that provide each team with opportunities for data gathering, performance demonstration and presentation. Learning outcomes are assessed through two assignments, each where marks are split between team (25%) and individual (75%) contributions. Part A: The Robot: Probabilistic Localisation Lectures: • Introduction to probabilistic robotics • Fundamentals of sensing • Bayesian framework for recursive state estimation • Probabilistic localisation with Extended Kalman Filters • Probabilistic localisation with Particle Filters Practicals: • Kinematics and sensing (tutorial and labs) • Probabilistic localisation with Extended Kalman Filters (tutorial and labs) • Probabilistic localisation with Particle Filters (tutorial and labs) Assignment A: The Robot - Probabilistic Localisation: Students implement probabilistic localisation methods on a small autonomous robotic platform to conduct a mission using probabilistic localisation. Part B: The World: Perception, Localisation and Mapping Lectures: • Perception of surroundings • Gaussian Processes for classification and regression • SLAM and map representation • Feature-based SLAM with pose-graphs part 1 • Feature-based SLAM with pose-graphs part 2 • Featureless SLAM with particle trajectory filters part 1 • Featureless SLAM with particle trajectory filters part 2 Practicals: • Perception (tutorial and workshops) • Feature-based SLAM using pose graphs (tutorial and workshops) • Featureless SLAM using particle trajectories (tutorial and workshops) Assignment B: The World: Perception, Localisation and Mapping: Students implement perception and SLAM frameworks for real-time operation of a small autonomous robotic platform to conduct mission using observations of its surroundings.

Teaching methods include •~5 lectures on probabilistic localisation •~6 lectures on sensing, perception and cognition •~7 lectures on simultaneous localisation and mapping •~12 tutorials on robot programming and implementation of methods •~6 labs with physical robots Learning activities include • Directed reading/independent learning • Directed use of robotic software libraries • Implementation of probabilistic methods on provided robotic hardware in tutorials and assignments • Design of experimental methods to demonstrate learning outcomes for the two assignments • Task management when working in groups for the two assignments • Report-writing for the two assignments • Live-demonstration and presentation for the two assignments