Multi-Agent Path Finding (MAPF) is a problem of finding a sequence of movements for agents to reach their assigned location without collision. Centralized algorithms usually give optimal solutions, but have difficulties to scale without employing various techniques - usually with a sacrifice of optimality; but solving MAPF problems with the number of agents greater than a thousand remains a challenge nevertheless. To tackle the scalability issue, we present DMAPF - a decentralized and distributed MAPF solver, which is a continuation of our recently published work, ros-dmapf. We address the issues of ros-dmapf where it (i) only works in maps without obstacles; and (ii) has a low success rate with dense maps. Given a MAPF problem, both ros-dmapf and DMAPF divide the map spatially into subproblems, but the latter further divides each subproblem into disconnected regions called areas. Each subproblem is assigned to a distributed solver, which then individually creates an abstract plan - a sequence of areas that an agent needs to visit - for each agent in it, and interleaves agent migration with movement planning. Answer Set Programming, which is known for its performance in small but complex problems, is used in many parts including problem division, abstract planning, border assignment for the migration, and movement planning. Robot Operating System is used to facilitate communication between the solvers and to enable the opportunity to integrate with robotic systems. DMAPF introduces a new interaction protocol between the solvers, and mechanisms that together result in a higher success rate and better solution quality without sacrificing much of the performance. We implement and experimentally validate DMAPF by comparing it with other state-of-the-art MAPF solvers and the results show that our system achieves better scalability.
- Pub Date:
- September 2021
- Computer Science - Multiagent Systems;
- Computer Science - Distributed;
- and Cluster Computing;
- Computer Science - Logic in Computer Science
- In Proceedings ICLP 2021, arXiv:2109.07914