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.

中文翻译：

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DMAPF：用于多智能体路径寻找问题的去中心化和分布式求解器

多代理路径查找 (MAPF) 是为代理找到一系列移动以到达其指定位置而不会发生冲突的问题。集中式算法通常会给出最佳解决方案，但在不采用各种技术的情况下难以扩展——通常会牺牲最优性；但是解决具有超过一千个代理数量的 MAPF 问题仍然是一个挑战。为了解决可扩展性问题，我们提出了 DMAPF——一个分散的分布式 MAPF 求解器，它是我们最近发表的工作 ros-dmapf 的延续。我们解决了 ros-dmapf 的问题，它 (i) 仅适用于没有障碍的地图；(ii) 使用密集地图的成功率较低。给定一个 MAPF 问题，ros-dmapf 和 DMAPF 都在空间上将地图划分为子问题，但后者进一步将每个子问题划分为称为区域的不连续区域。每个子问题都分配给一个分布式求解器，然后该求解器分别为其中的每个代理创建一个抽象计划——一个代理需要访问的区域序列，并将代理迁移与移动计划交织在一起。答案集编程以其在小而复杂的问题上的表现而著称，用于许多部分，包括问题划分、抽象规划、迁移的边界分配和运动规划。机器人操作系统用于促进求解器之间的通信，并提供与机器人系统集成的机会。DMAPF 引入了求解器之间的新交互协议，和机制共同导致更高的成功率和更好的解决方案质量，而不会牺牲很多性能。我们通过将 DMAPF 与其他最先进的 MAPF 求解器进行比较来实现和实验验证 DMAPF，结果表明我们的系统实现了更好的可扩展性。