This research develops scalable motion-planning algorithms that enable large teams of robots to work together safely and efficiently. By combining machine learning with search algorithms, the work delivers both speed and reliability, supporting applications from automated warehouses to disaster response, infrastructure repair, and future space exploration.

This research develops a distributed multi-robot task allocation framework that enables autonomous robots to estimate tasks, share information, coordinate assignments, and avoid collisions without relying on a central server. The approach improves efficiency, scalability, and resilience, with applications in emergency response, particularly supporting firefighters during life-saving operations.

The speaker develops a decentralised communication framework for collaborative robots (cobots). By removing the central server and enabling robots to communicate directly through estimating unknown variables, the system reduces cost, time, and memory use. This foundation supports efficient task allocation for applications like delivery and firefighting.