Multi-robot systems (MRS) increasingly offload compute-intensive perception tasks to edge nodes to meet strict time-sensitive Quality-of-Service (QoS) constraints. However, static task orchestration on a shared edge node can severely degrade QoS due to network latency, jitter, and edge-resource contention. We present a pilot edge-centric MRS testbed using Raspberry Pi nodes to evaluate a camera-to-manipulator pipeline under three modes: local execution, static offloading, and a QoS-aware Adaptive Task Placement (ATP) controller. ATP scores candidate placements using a multi-metric cost (normalized latency, CPU utilization, and switching overhead) over two-second control windows. The closed-loop visual servoing testbed is instrumented with sub-millisecond clock synchronization, network emulation, and detailed monitoring of multiple metrics across nodes to capture realistic jitter. Experimental results under compute-stress and network-fault scenarios show that static edge offloading reduces on-board CPU load but amplifies tail latency and deadline misses. In contrast, the QoS-aware ATP controller, by switching task placement based on measured latency and utilization thresholds, consistently lowers deadline violations and tail latency. Overall, the results position ATP as a practical edge-side control primitive for MRS and concrete design guidelines for Cloud-Edge Robotics deployments within the broader cloud-fog automation, while motivating QoS-aware multi-objective workload orchestration for industrial cyber-physical systems.

翻译：多机器人系统（MRS）越来越多地将计算密集型感知任务卸载到边缘节点，以满足严格的时间敏感服务质量（QoS）约束。然而，在共享边缘节点上执行静态任务编排可能会因网络延迟、抖动和边缘资源争用而严重降低QoS。我们提出了一个采用Raspberry Pi节点构建的以边缘为中心的MRS试验平台，用于评估摄像头-机械臂流水线在三种模式下的性能：本地执行、静态卸载以及QoS感知的自适应任务放置（ATP）控制器。ATP在每个两秒的控制窗口内，使用多度量成本（归一化延迟、CPU利用率和切换开销）对候选放置方案进行评分。该闭环视觉伺服试验平台配备了亚毫秒级时钟同步、网络仿真以及跨节点的多指标详细监控，以捕获真实的抖动。在计算压力和网络故障场景下的实验结果表明，静态边缘卸载降低了机载CPU负载，但增加了尾部延迟和截止时间错过次数。相比之下，基于测量延迟和利用率阈值切换任务放置的QoS感知ATP控制器，能够持续降低截止时间违规次数和尾部延迟。总体而言，这些结果将ATP定位为MRS的实用边缘侧控制原语，并为更广泛的云-雾自动化中的云-边缘机器人部署提供了具体设计指南，同时激发了面向工业信息物理系统的QoS感知多目标工作负载编排。