  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.
