多代理路径搜索的虚拟障碍调节

IF 4.6 2区 计算机科学 Q2 ROBOTICS
Sike Zeng;Xi Chen;Li Chai
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引用次数: 0

摘要

多代理路径搜索(MAPF)涉及为多个代理寻找无碰撞路径,同时使路径总成本最小化。基于冲突的显式估计搜索(EECBS)是广泛使用的基于冲突的搜索(CBS)方法的最新变体,可提供有界次优解。然而,CBS 及其变体都依赖于成对冲突解决方法。冲突繁荣指的是在一个地点发生许多冲突,这种情况经常出现在大量代理在狭小空间内运行的场景中,通常会导致沉重的计算负担。冲突繁荣发生的位置被视为冲突繁荣顶点。本文提出了一种新方法--虚拟障碍规程,以加快 MAPF 的算法求解过程(如 EECBS)。所提方法能识别冲突繁荣顶点,并将其作为全局或局部虚拟障碍进行策略性调节,以规避集中冲突。这样,冲突繁荣顶点上的成对冲突解决过程就大大简化了,从而加快了整个算法的运行时间--通常冲突解决时间是算法运行时间的主要部分。数值研究验证了这种方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Virtual Obstacles Regulation for Multi-Agent Path Finding
Multi-agent path finding (MAPF) involves finding collision-free paths for multiple agents while minimizing the total path costs. Explicit estimation conflict-based search (EECBS) represents a state-of-the-art variant of the widely used conflict-based search (CBS) method, offering bounded-suboptimal solutions. However, both CBS and its variants rely on pairwise conflict resolution methods. A conflict boom means many conflicts occur at one location, which frequently exists in scenarios that a large number of agents operate in small space, and usually leads to heavy computational burden. The location that conflict boom occurs is regarded as conflict boom vertex. This letter proposes a novel method, the Virtual Obstacles Regulation, to expedite algorithmic solving processes (such as EECBS) for MAPF. The proposed method identifies conflicts boom vertices and strategically regulates them as global or local virtual obstacles to circumvent concentrated conflicts. Then, the pairwise conflict resolution processes on conflicts boom vertices are significantly simplified, hence accelerating overall algorithm runtime–often dominated by conflict resolution. Numerical studies validate the efficacy of this approach.
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来源期刊
IEEE Robotics and Automation Letters
IEEE Robotics and Automation Letters Computer Science-Computer Science Applications
CiteScore
9.60
自引率
15.40%
发文量
1428
期刊介绍: The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.
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