Pareto Optimal Task Offloading and Mobile Robots Paths in Edge Cloud Assisted mmWave Networks

The emerging beyond fifth-generation (B5G) and envisioned sixth-generation (6G) wireless networks are considered as key enablers in supporting a diversified set of applications for industrial mobile robots (MRs). The scenario under investigation in this paper relates to mobile robots that autonomously roam on an industrial floor and assist in offloading tasks generated at different workstations. In such scenarios, the potential simultaneous task offloading during multiple MR movements may cause an excessive edge computing burden for offloaded tasks. To jointly consider MR path planning and efficient task offloading strategy, a novel weighted-sum multi-objective optimization problem is proposed where the robot total travel time and aggregated computation workload are optimized jointly to provide Pareto efficient optimal non-dominated solutions. An extensive set of numerical investigations reveal that compared with the time-only and workload-only optimization schemes, the proposed multi-objective optimization scheme can reduce the total travel time and aggregated computation workload by 39.2%, 89.2%, respectively, while achieving a decrease of the aggregated computation workload by an average of 85% compared to the Vehicle Routing Problem (VRP) solution.