Joint Resource Scheduling for AMR Navigation Over Wireless Edge Networks

Abstract

The future of autonomous systems will rely on the usage of wireless time-sensitive networks to connect mobile cyberphysical systems, such as Autonomous Mobile Robots (AMRs), to Edge compute platforms to offload computationally intensive workloads necessary to complete tasks. In the case of AMRs, due to their mobility, the offloading of expensive processes such as localization and tracking methods to the Edge computing infrastructure must also be done over dynamic wireless networks. In larger scale systems, the network and compute resource requirements can quickly become prohibitively large due to network traffic and heavy workloads and tight deadline requirements for proper execution of time-critical tasks. In this paper, we formulate the problem of jointly allocating network and compute resources for time sensitive systems as the state of the wireless channel changes over time. By characterizing a compute model for AMR workloads, we further demonstrate how the network and compute scheduling decisions can be serialized, thus making the optimal scheduling problem significantly more tractable, via the incorporation of a compute-utility aware network cost function. Simulation results of AMR systems in a Wi-Fi network demonstrate substantial gains over baseline scheduling methods in total resource efficiency.