Plan Execution for Multi-Agent Path Finding with Indoor Quadcopters
This addresses collision avoidance for small indoor quadcopters in multi-agent systems, but it is incremental as it builds on existing CCBS methods.
They tackled the problem of executing multi-agent path finding plans with indoor quadcopters by modifying the CCBS algorithm to include cylindrical protection zones, resulting in safe plans suitable for quadcopter execution.
We study the planning and acting phase for the problem of multi-agent path finding (MAPF) in this paper. MAPF is a problem of navigating agents from their start positions to specified individual goal positions so that agents do not collide with each other. Specifically we focus on executing MAPF plans with a group of Crazyflies, small indoor quadcopters . We show how to modify the existing continuous time conflict-based search algorithm (CCBS) to produce plans that are suitable for execution with the quadcopters. The acting phase uses the the Loco positioning system to check if the plan is executed correctly. Our finding is that the CCBS algorithm allows for extensions that can produce safe plans for quadcopters, namely cylindrical protection zone around each quadcopter can be introduced at the planning level.