English(EN) Simultaneous Computation with Multiple Prioritizations in Multi-Agent Motion Planning

新的MAPF方法支持智能体的同步多优先级

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员开发了一种新颖的多智能体路径查找（MAPF）方法，允许智能体同时使用多种优先级策略进行计算。该方法解决了大型网络中MAPF的计算挑战，传统优先级规划（PP）解决方案高度依赖于所选的优先级。新技术具有通用性，无需领域特定知识，并在实验中显示出近乎最优的优先级，计算时间略有增加但优于现有方法。在涉及道路网络中多辆车的模拟中，它还显示了实时能力。 AI

影响这项研究可以提高复杂环境中多智能体系统的效率和有效性，可能对机器人和自主系统产生影响。

排序理由这是一篇详细介绍多智能体路径查找新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Patrick Scheffe, Julius Kahle, Bassam Alrifaee · 2026-10-09 04:00

多智能体运动规划中的多重优先级同步计算

arXiv:2501.10781v2 Announce Type: replace-cross Abstract: Multi-agent path finding (MAPF) in large networks is computationally challenging. An approach for MAPF is prioritized planning (PP), in which agents plan sequentially according to their priority. Albeit a computationally e…

本文如何被排名

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多智能体运动规划中的多重优先级同步计算

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