退化通信下分布式多机器人任务分配中的束长度权衡
Bundle Length Tradeoffs in Decentralized Multi-Robot Task Allocation Under Degraded Communications
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中文总结 AI 辅助
本研究系统分析了束长度 B 在理想和退化通信下对多机器人任务分配 MinSum 与 MinMax 权衡的影响，发现丢包下最优 B 向较小值偏移，并量化了保留理想设置带来的性能惩罚。
中文摘要 AI 辅助
在多任务多机器人任务分配（MRTA）算法的配置中，束长度 B 通常是固定的。已知 MinSum 和 MinMax 目标偏好不同的任务分布，但 B 在此目标权衡中的作用尚未被系统地表征。此外，退化通信评估也常常保留在理想通信下选择的设置，使得名义上的束长度调优在消息丢失下是否仍然有效的问题悬而未决。我们针对 ACBBA、PI 和 HIPC，在理想通信和 25% 伯努利丢包下，对六种束长度在 300 个配对的十目标协作访问场景中考察了这两个问题。在理想通信下，将 B 从 1 增加到 12，ACBBA、PI 和 HIPC 的 MinSum 成本分别降低 19.0%、23.0% 和 31.8%，而 MinMax 成本分别增加 45.6%、94.3% 和 67.6%。在丢包下，ACBBA 和 PI 的最低平均 MinSum 设置从 B = 12 变为 B = 2。重复的配对交叉拟合表明，保留理想网络设置会导致持有样本的 MinSum 惩罚分别为 14.4% 和 7.2%，并且相对于损失条件下的 MinSum 设置，MinMax 成本分别增加 30.0% 和 41.8%。HIPC 保留了较深的 MinSum 操作区域，而所有三种分配器的 MinMax 设置保持稳定。在两个额外的目标负载下的实验重现了 ACBBA 和 PI 的 MinSum 偏移。
英文摘要
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.
发表机构
- University of San Diego（圣地亚哥大学）
机构由 AI 辅助整理，请以论文原文为准。
