  To circumvent persistent connectivity to the cloud infrastructure, the
current emphasis on computing at network edge devices in the multi-robot domain
is a promising enabler for delay-sensitive jobs, yet its adoption is rife with
challenges. This paper proposes a novel utility-aware dynamic task offloading
strategy based on a multi-edge-robot system that takes into account
computation, communication, and task execution load to minimize the overall
service time for delay-sensitive applications. Prior to task offloading,
continuous device, network, and task profiling are performed, and for each task
assigned, an edge with maximum utility is derived using a weighted utility
maximization technique, and a system reward assignment for task connectivity or
sensitivity is performed. A scheduler is in charge of task assignment, whereas
an executor is responsible for task offloading on edge devices. Experimental
comparisons of the proposed approach with conventional offloading methods
indicate better performance in terms of optimizing resource utilization and
minimizing task latency.
