Multi-Agent Path Finding (MAPF) problems are traditionally solved in a centralized manner. There are works focusing on completeness, optimality, performance, or a tradeoff between them. However, there are only a few works based on spatial distribution. In this paper, we introduce ros-dmapf, a distributed MAPF solver. It consists of multiple MAPF sub-solvers, which---besides solving their assigned sub-problems---interact with each other to solve a given MAPF problem. In the current implementation, the sub-solvers are answer set planning systems for multiple agents, and are created based on spatial distribution of the problem. Interactions between components of ros-dmapf are facilitated by the Robot Operating System (ROS). The highlights of ros-dmapf are its scalability and a high degree of parallelism. We empirically evaluate ros-dmapf using the move-only domain of the asprilo system and results suggest that ros-dmapf scales up well. For instance, ros-dmapf gives a solution of length around 600 for a MAPF problem with 2000 robots in randomly generated 100×100 obstacle-free maps---a problem beyond the capability of a single sub-solver---within 7 minutes on a consumer laptop. We also evaluate ros-dmapf against some other MAPF solvers and results show that the system performs well. We also discuss possible improvements for future work.
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A Hierarchical Approach to Multi-Agent Path Finding
The Multi-Agent Path Finding (MAPF) problem arises in many real-world applications, ranging from automated warehousing to multi-drone delivery. Solving the MAPF problem optimally is NP-hard, and existing optimal and bounded-suboptimal MAPF solvers thus usually do not scale to large MAPF instances. Greedy MAPF solvers scale to large MAPF instances, but their solution qualities are often bad. In this paper, we therefore propose a novel MAPF solver, Hierarchical Multi-Agent Path Planner (HMAPP), which creates a spatial hierarchy by partitioning the environment into multiple regions and decomposes a MAPF instance into smaller MAPF sub-instances for each region. For each sub-instance, it uses a bounded-suboptimal MAPF solver to solve it with good solution quality. Our experimental results show that HMAPP solves as large MAPF instances as greedy MAPF solvers while achieving better solution qualities on various maps.
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- Award ID(s):
- 1837779
- NSF-PAR ID:
- 10296533
- Date Published:
- Journal Name:
- International Symposium on Combinatorial Search
- Page Range / eLocation ID:
- 209-211
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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