We propose integrating the edge-computing paradigm into the multi-robot collaborative scheduling to maximize resource utilization for complex collaborative tasks, which many robots must perform together. Examples include collaborative map-merging to produce a live global map during exploration instead of traditional approaches that schedule tasks on centralized cloud-based systems to facilitate computing. Our decentralized approach to a consensus-based scheduling strategy benefits a multi-robot-edge collaboration system by adapting to dynamic computation needs and communication-changing statistics as the system tries to optimize resources while maintaining overall performance objectives. Before collaborative task offloading, continuous device, and network profiling are performed at the computing resources, and the distributed scheduling scheme then selects the resource with maximum utility derived using a utility maximization approach. Thorough evaluations with and without edge servers on simulation and real-world multi-robot systems demonstrate that a lower task latency, a large throughput gain, and better frame rate processing may be achieved compared to the conventional edge-based systems.

翻译：我们提出将边缘计算范式融入多机器人协作调度中，以最大化复杂协作任务（需多机器人共同执行）的资源利用率。例如，在探索过程中通过协作地图拼接生成实时全局地图，而非采用传统基于集中式云系统调度任务的计算方式。我们提出的基于共识的分散式调度策略使多机器人-边缘协作系统能够适应动态计算需求及通信统计变化，在维持整体性能目标的同时优化资源分配。在协作任务卸载前，持续对计算资源的设备与网络进行性能剖析，随后分布式调度方案采用效用最大化方法选择效用最优的资源。通过在仿真与真实多机器人系统中进行有无边缘服务器的全面评估，结果表明，与传统边缘系统相比，该方法可降低任务延迟、提升吞吐量并实现更优的帧率处理效果。