Computation Offloading and Task Scheduling among Multi-Robot Systems

Abstract: In a Multi-Robot System (MRS), robots perform some collaborative behaviors so that some goals that are impossible for a single robot to achieve become feasible and attainable. Developing rapidly and exploited widely, cloud further extends the resources a robot can access thereby bringing significant potential benefits to robotic and automation systems. One of the potential benefits is Computation Offloading that moves the computational heavily parts of an application onto a server to reduce the execution time. However, to enable the computation offloading, the question must be answered when, what, where and how to offload? While some offloading mechanisms proposed in the mobile computing area (i.e., smartphones, pads), the question remains not fully answered and many new challenges emerge when it comes to the robotic realm. This paper aims to apply computation offloading technology to a Multi-Robot System and investigate the performance impact it has on the processing time of robot applications. For this purpose, a computation offloading framework is proposed for an elastic computing model with the engagement of a two-tier cloud infrastructure, i.e., a public cloud infrastructure and an ad-hoc local network (fog) formed by a cluster of robots. Two scheduling algorithms: Heterogeneous-Earliest-Finish-Time (HEFT) and Critical-Path-on-a-Processor (CPOP) are implemented to schedule the offloaded tasks to available robots and servers such that the total execution time of the application is minimized. The offloading framework is implemented based on Robot Operating System (ROS) and tested through simulated applications. The results prove the feasibility of proposed offloading framework and indicate potential execution speeding up of robot applications by exploiting offloading technology.