Safe Robot Swarms: Methods to Prevent, Detect, and Mitigate Faults.

Robot swarms are susceptible to failure - both at the level of an individual robot and the swarm as a collective. Developing methods for fault detection, diagnosis and recovery (FDDR) will therefore be critical for successful deployment in real-world applications. My work focuses largely on data-driven methods for FDDR in swarms, methods which allow individuals to self-detect faults or to mitigate their impact on overall swarm performance. Additionally, in collaborative work, we design data-centric processes for the assurance of safe swarm behaviour, bridging the gap between experimental results and real-world safety requirements.

Suet Lee earned an integrated Master’s degree in Mathematics from Imperial College London, UK. Since then she has worked in industry in the fields of software development and robotics, before returning to academia to complete a PhD in Swarm Robotics at the University of Bristol, Hauert Lab. Suet is currently a postdoctoral researcher at the University of Konstanz in the Cyber-Physical Systems group.