{"ID":2921985,"CreatedAt":"2026-06-02T02:42:49.606572591Z","UpdatedAt":"2026-06-02T05:43:05.476461329Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.00552","arxiv_id":"2606.00552","title":"Edge-Based QoS-Aware Adaptive Task Placement: A Closed-Loop Control in Multi-Robot Systems","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2606.00552","url_pdf":"https://arxiv.org/pdf/2606.00552v1","authors":"[\"Thien Tran\",\"Jonathan Kua\",\"Thuong Hoang\",\"Minh Tran\",\"Honghao Lyu\",\"Jiong Jin\"]","published":"2026-05-30T05:54:44Z","proceeding":"cs.OS","tasks":"[\"cs.OS\",\"cs.DC\",\"cs.NI\",\"cs.RO\",\"eess.SY\"]","methods":"[]","has_code":false}