Link to video on YouTube

Abstract: “Teams of robots often have to assign target locations among themselves and then plan collision-free paths to their target locations. Examples include autonomous aircraft towing vehicles and automated warehouse systems. For example, in the near future, autonomous aircraft towing vehicles might tow aircraft all the way from the runways to their gates (and vice versa), thereby reducing pollution, energy consumption, congestion and human workload. Today, hundreds of robots already navigate autonomously in Amazon fulfillment centers to move inventory pods all the way from their storage locations to the packing stations. Path planning for these robots can be NP-hard, yet one must find high-quality collision-free paths for them in real-time. The shorter these paths are, the fewer robots are needed and the cheaper it is to open new fulfillment centers. In this talk, I describe several variants of the multi-robot path-planning problem, their complexities and algorithms for solving them. I also present a hierarchical planning architecture that combines ideas from artificial intelligence and robotics. It makes use of a simple temporal network to post-process the output of a multi-robot path-finding algorithm in polynomial time to create a plan-execution schedule that take the maximum translational and rotational velocities of non-holonomic robots into account, provides a guaranteed safety distance between them, and exploits slack to absorb imperfect plan executions and avoid time-intensive re-planning in many cases. This research is joint research with N. Ayanian, T. Cai, L. Cohen, W. Hoenig, S. Kumar, H. Ma, G. Sharon, C. Tovey, T. Uras, H. Xu, S. Young, D. Zhang, and other colleagues and students.”