Intelligent Planning for Large-Scale Multi-Agent Coordination

There is no doubt that robots will play a crucial role in the future and need to work as a team in increasingly more complex applications. Advances in robotics have laid the hardware foundations for building large-scale multi-robot systems, such as for mobile robots and drones. But how to coordinate robots intelligently is a difficult problem: Exact methods, such as integer programming and A*, do not scale as the joint-state space increases exponentially with the number of robots, while heuristic methods, such as reactive methods and end-to-end learning, may lead to deadlocks or traffic congestion. 

In this talk, I will introduce intelligent planning algorithms for solving this challenge with a focus on one fundamental problem: letting a large team of agents navigate without collisions in congested environments while minimizing their travel times. I will present principled planning algorithms that can efficiently coordinate hundreds of agents while providing rigorous guarantees on completeness and even optimality. I will also present techniques to apply these algorithms to real-world problems with robustness guarantees, such as warehouse robot coordination, multi-robot motion planning, railway planning, and airport surface operation.