Plan Execution for Multi-Agent Path Finding with Indoor Quadcopters

TL;DR

This paper adapts a multi-agent pathfinding algorithm for indoor quadcopters, enabling safe navigation by incorporating cylindrical protection zones, and verifies plan execution with a positioning system.

Contribution

It modifies the conflict-based search algorithm for safe quadcopter navigation and demonstrates its effectiveness in indoor environments.

Findings

Modified CCBS produces safe, executable plans for quadcopters.

Protection zones improve collision avoidance.

Plan execution verified with Loco positioning system.

Abstract

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.