Intelligent Mobile Robotics (MSc RAS)

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

• Knowledge of how to implement probabilistic methods on a simple mobile robot using python language and robot middleware (e.g., ROS). [EA-C3, DI-C6, EP-C12]
• Function effectively as an individual, and as a member or leader of a team. Evaluate effectiveness of own and team performance. [EP-M16]
• Understand the operating principles, assumptions and limiting factors of different Bayesian frameworks (e.g., Kalman Filters, Particle Filters, Graphical Methods) in the context of robot localisation and mapping. [SM-M1, EA-M2, EA-M3]
• Knowledge of social, environmental impacts of robotics and automation, including ethical considerations, security risks, diversity and inclusion in future job markets. [ES-C7, ES-C8, ES-C9, ES-C11]
• Understand the use of statistical modelling techniques (e.g., Gaussian Processes) to allow robots to interpret sensor data and make sense of their surroundings. [SM-M1, EA-M2]
• Be able to adapt and apply robotic concepts to design and develop practical robotic solutions for different application domains. [EA-C4, EP-C13]
• Understand how probabilistic methods can address the uncertainty that is inherent due to real-world non-determinism. [SM-M1, EA-M2]
• Be able to design practical experiments in order to assess the success and sensitivities of implemented methods in practical problem solving tasks. [EA-M2, EA-M3, DI-C4, EP-C12]
• Understand the key issues surrounding uncertainty of sensors, actuators and dynamic models used in different robotic application domains that motivate the need for probabilistic methods. [SM-M1, EA-M2]
• Be able to accurately express basic robotic concepts and critically assess their suitability for different application domains based on their assumptions and their limitations. [EA-M2, EA-M3, EA-C4, EP-C17]

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

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.