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。AI 中文总结
本研究评估去中心化多机器人系统中任务准入合并机制，通过AGX Orin和RP2040实验发现其效果取决于到达率、分配器类型及平台计算成本，可减少处理器工作但可能增加延迟。
AI 中文摘要
在动态任务中，多机器人任务分配器通常立即接纳新发布的任务，这可能导致每次新任务到达时都触发分配过程。我们评估了任务准入合并作为一种控制分配器处理器工作量的机制，同时考虑目标服务延迟，使用CBAA、ACBBA、PI和HIPC作为代表性MRTA套件。该研究包括一次包含3,000次任务的AGX Orin实验，测量了计算延迟，3,000次配对零计算任务，以及一次包含96次任务的Pololu 3pi+ RP2040分配器硬件在环实验。将即时（Eager）准入与两个、四个和八个任务的阈值以及一个具有10秒等待界限的四任务策略在三种到达率下进行比较。在AGX实验中，合并减少了48个评估条件中31个条件下的分配器调用和处理器工作量，尽管节省幅度因分配器而异。主要代价出现在稀疏到达情况下，四任务批处理将在线目标平均延迟增加了20.08-23.57秒，主要是通过准入等待。有界准入在十二个分配器负载条件中的十个条件下减少了平均工作量，包括在高到达HIPC下平均延迟增加1.40秒时工作量减少19.35%。RP2040实验表明，随着处理器约束收紧，这种权衡可能变得更加有利。在四个匹配的中等到达HIPC场景中，Count b=4将RP2040平均工作量减少了59.85%，服务延迟减少了39.47%，而相应的AGX案例延迟增加了27.69%。这些结果表明，任务准入合并的有用性取决于到达强度、分配器特定行为以及执行平台上计算的相对成本。
英文摘要
Multi-robot task allocators in dynamic missions commonly admit newly released tasks immediately, potentially invoking allocation for each new arrival. We evaluate task-admission coalescing as a mechanism for controlling allocator processor work while accounting for target-service latency, using CBAA, ACBBA, PI, and HIPC as a representative MRTA suite. The study comprises a 3,000-mission AGX Orin campaign with measured computation delay, 3,000 paired zero-compute missions, and a 96-mission Pololu 3pi+ RP2040 allocator hardware-in-the-loop campaign. Immediate (Eager) admission is compared with thresholds of two, four, and eight tasks and a four-task policy with a 10-s waiting bound across three arrival rates. Across the AGX experiments, coalescing reduces both allocator calls and processor work in 31 of 48 evaluated conditions, although the magnitude of the saving varies by allocator. The principal cost appears under sparse arrivals, where four-task batching increases online-target mean latency by 20.08-23.57 s, predominantly through admission waiting. Bounded admission reduces mean work in ten of twelve allocator-load conditions, including a 19.35% reduction with a 1.40-s mean latency increase for high-arrival HIPC. RP2040 experiments show that this tradeoff can become more favorable as processor constraints tighten. Across four matched medium-arrival HIPC scenarios, Count b=4 reduces mean RP2040 work by 59.85% and service latency by 39.47%, while the corresponding AGX cases increase latency by 27.69%. These results show that the usefulness of task-admission coalescing depends on arrival intensity, allocator-specific behavior, and the relative cost of computation on the execution platform.
