This research explores swarms of small, modular robots that cooperate like ant colonies to perform complex tasks. Using control theory, optimization, and machine learning, the work enables resilient, energy-efficient robotic systems that adapt in real time, with applications ranging from disaster response and space exploration to medical technologies.

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.

This talk explains research that teaches legged robots how to walk reliably using machine learning, computer vision, advanced control theory, and Lyapunov-based safety guarantees. By improving robot stability on complex terrain, the work moves us closer to versatile, household multi-purpose robots capable of performing everyday chores safely and independently.

My research develops fault-tolerant, cooperative control algorithms for multi-drone formations carrying shared payloads. By detecting motor failures, restoring lost force, and autonomously reconfiguring drone positions, the system reduces load disturbances by up to 90%. These methods enable safer, more reliable drone-assisted rescue and delivery operations in hazardous conditions.