Path Planning for Multiple Agents under Uncertainty

Multi-agent systems in cluttered environments require path planning that not only prevents collisions with static obstacles, but also safely coordinates the motion of many agents. The challenge of multi-agent path finding becomes even more difficult when the agents experience uncertainty in their pose. In this work, we develop a multi-agent path planner that considers uncertainty, called uncertainty M* (UM*), which is based on a prior multi-agent path approach called M*. UM* plans a path through the belief space for each individual agent and then uses a strategy similar to M* that coordinates only agents that are “likely” to collide. This approach has the same scalability advantages as M*. We then introduce an extension called Permuted UM* (PUM*) that uses randomized restarts to enhance performance. We finish by presenting a belief space representation appropriate for multi-agent path planning with uncertainty and validate the performance of UM* and PUM* in simulation and mixed-reality experiments.