arXiv:2609.06273cs.ROcs.MA2026-09

研究机器人任务分配中任务包长度对效率与公平性的权衡,发现通信变差时最优包长会改变。

Bundle Length Tradeoffs in Decentralized Multi-Robot Task Allocation Under Degraded Communications

论文配图:Bundle Length Tradeoffs in Decentralized Multi-Robot Task Allocation Under Degraded Communications
图 1 · 摘自论文原文
  • 通过六种包长度测试三种算法在理想与丢包环境下的表现
  • 包长从1增至12使最小和成本降低19%~31.8%,但最大成本上升45.6%~94.3%
  • 通信差时最优包长从12降至2,沿用原设置会导致成本上升7%~14.4%

在多任务多机器人任务分配(MRTA)中,任务包长度B通常固定。尽管MinSum和MinMax目标倾向不同任务分布,但其与包长的关系尚未系统分析。此外,多数通信降级评估仍沿用理想通信下的配置,无法确定最优包长是否可迁移。本文在300组十目标协作访问场景下,考察了ACBBA、PI和HIPC在六种包长(1–12)下的表现,涵盖理想通信与25%伯努利丢包条件。理想通信下,包长由1增至12使三者MinSum成本分别下降19.0%、23.0%、31.8%,而MinMax成本则上升45.6%、94.3%、67.6%。丢包条件下,最优平均MinSum的包长由12变为2(适用于ACBBA和PI)。交叉验证显示,沿用理想网络设置将导致额外14.4%和7.2%的MinSum代价,并使MinMax成本增加30.0%和41.8%。HIPC具有宽广的低MinSum区域,而所有算法的MinMax设置保持稳定。在两种额外目标负载下重复实验,结果一致支持ACBBA和PI的包长转移现象。

原文摘要 · Abstract (English)

Bundle length B is commonly fixed when configuring multi-task multi-robot task allocation (MRTA) algorithms. MinSum and MinMax are known to favor different task distributions, but the role of B in this objective tradeoff has not been systematically characterized. Additionally, degraded-communication evaluations also often retain settings selected under ideal communication, leaving whether nominal bundle-length tuning transfers under message loss unresolved. We examine both questions for ACBBA, PI, and HIPC across six bundle lengths in 300 paired ten-target Collaborative Visit scenarios under ideal communication and 25% Bernoulli packet loss. Under ideal communication, increasing B from 1 to 12 reduces MinSum cost by 19.0%, 23.0%, and 31.8% for ACBBA, PI, and HIPC, respectively, while increasing MinMax cost by 45.6%, 94.3%, and 67.6%. Under packet loss, the lowest-mean MinSum setting shifts from B = 12 to B = 2 for ACBBA and PI. Repeated paired cross-fitting shows that retaining the ideal-network setting incurs held-out MinSum penalties of 14.4% and 7.2%, respectively, and increases MinMax cost by 30.0% and 41.8% relative to the loss-conditioned MinSum setting. HIPC retains a deep MinSum operating region, while the MinMax setting remains stable for all three allocators. Experiments at two additional target loads reproduce the ACBBA and PI MinSum shifts.

任务分配机器人通信降级优化

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