The aim of this course is to discuss about sensor fusion architectures, algorithms and applications in the context of autonomous vehicles navigation, guidance and control both for linear and non-linear systems. The module aims also to give the students an understanding of the appropriate tools for error analysis, diagnostic statistics and heuristics enabling them to critically evaluate the performance of a sensor fusion architecture and algorithm. The main emphasis is on the Bayesian Filter algorithm together with variants and generalisations, applied to various applications such as navigation, SLAM (Simultaneous Localization and Mapping),target tracking.

This course aims to introduce optimisation methods for control of networked multi-agent systems. A revolution is underway for key societal infrastructures such as robotics, transport and energy towards new operational paradigms of networked control systems, where multiple (sub)systems autonomously act and plan based on the interactions with other agents. This is paving our future: providing a means of solving the societal challenges we encounter and consequently bringing great benefits to our society. Despite the great potential, there remain challenges we need to address to unlock their benefits in full. The challenges arising in these systems include i) distributing computational power in large-scale networks, ii) achieving optimality of multiple, potentially heterogeneous, agents without global knowledge, and iii) enhancing robustness against uncertainties. This course introduces a set of the principles of optimisation and optimal control, optimisation algorithms, and distributed optimisation algorithms for control, addressing the aforementioned challenges. The course delivers not only fundamental and theoretical understanding on the algorithms, but also computational and practical frameworks to analyse their characteristics.