基于几何约束的多智能体寻径

Dor Atzmon, S. Bernardini, F. Fagnani, D. Fairbairn
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引用次数: 1

摘要

在解决多智能体寻路问题(MAPF)时,我们研究了一类特定的路径,该路径是通过从起点到目标位置的最短路径并在路径开始处添加安全延迟来构建的,这保证了它们之间的不冲突。安全延迟是通过利用一组基本的几何约束来计算所有智能体的起始点和目标点之间的距离。这些约束很简单,但是根据它们重新表述的MAPF问题在计算上仍然很困难。尽管如此,基于安全延迟,我们设计了一种新的,快速和轻量级的算法,称为延迟最短路径(DSP),以找到MAPF问题的解决方案。通过对标准基准的广泛实验评估,我们表明,在许多情况下,我们的技术在处理数千个代理的问题并返回低成本解决方案时,比相关方法快几个数量级。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploiting Geometric Constraints in Multi-Agent Pathfinding
In tackling the multi-agent pathfinding problem (MAPF), we study a specific class of paths that are constructed by taking the agents’ shortest paths from the start to the goal locations and adding safe delays at the beginning of the paths, which guarantee that they are non-conflicting. Safe delays are calculated by exploiting a set of fundamental geometric constraints among the distances between all agents’ start and goal locations. Those constraints are simple, but the MAPF problem reformulated in terms of them remains computationally hard. Nonetheless, based on safe delays, we devise a new, fast and lightweight algorithm, called Delayed Shortest Path (DSP), to find solutions to the MAPF problem. Via an extensive experimental evaluation on standard benchmarks, we show that, in many cases, our technique runs several orders of magnitudes faster than related methods while addressing problems with thousands of agents and returning low-cost solutions.
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