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Learn all about Multi-Agent Path Finding (MAPF)


W. Hoenig, S. Kumar, L. Cohen, H. Ma, H. Xu, N. Ayanian and S. Koenig. Multi-Agent Path Finding with Kinematic Constraints. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), pages 477-485, 2016. Best ICAPS Paper Award (Robotics Track).

Abstract: Multi-Agent Path Finding (MAPF) is well studied in both AI and robotics. Given a discretized environment and agents with assigned start and goal locations, MAPF solvers from AI find collision-free paths for hundreds of agents with user-provided sub-optimality guarantees. However, they ignore that actual robots are subject to kinematic constraints (such as finite maximum velocity limits) and suffer from imperfect plan-execution capabilities. We therefore introduce MAPF-POST, a novel approach that makes use of a simple temporal network to postprocess the output of a MAPF solver in polynomial time to create a plan-execution schedule that can be executed on robots. This schedule works on non-holonomic robots, takes their maximum translational and rotational velocities into account, provides a guaranteed safety distance between them, and exploits slack to absorb imperfect plan executions and avoid time-intensive replanning in many cases. We evaluate MAPF-POST in simulation and on differential-drive robots, showcasing the practicality of our approach.

Download the paper in pdf.

(last updated in 2022)