Summary: Distributed Task Assignment and Path Planning with Limited Communication for Robot Teams

We consider multi-robot service scenarios, where tasks appear at any time and in any location of the working area. A solution to such a service task problem requires finding a suitable task assignment and a collision-free trajectory for each robot of a multi-robot team. In cluttered environments, such as indoor spaces with hallways, those two problems are tightly coupled. We propose a decentralized algorithm for simultaneously solving both problems, called Hierarchical Task Assignment and Path Finding (HTAPF). HTAPF extends a previous bio-inspired Multi-Robot Task Allocation (MRTA) framework [1]. In this work, task allocation is performed on an arbitrarily deep hierarchy of work areas and is tightly coupled with a fully distributed version of the priority-based planning paradigm [12], using only broadcast communication. Specifically, priorities are assigned implicitly by the order in which data is received from nearby robots. No token passing procedure or specific schedule is in place ensuring robust execution also in the presence of limited probabilistic communication and robot failures.