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.

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.