Optimization of Communication Tasks in an Energy-Efficient Swarm and the Spatial Distribution of Robots

Energy-efficient coordination of robotic swarms requires effective integration of task scheduling, motion planning, and communication management, particularly in resource-constrained environments where computation and wireless communication compete for limited energy resources. Existing multi-robot approaches typically address these concerns in separate stages: task-allocation methods (e.g., market- and auction-based schemes) price assignments by distance, and computation-offloading methods decide execution placement after a route has been fixed. This paper's specific contribution is to fold the execution-placement decision (local computation versus offloading to a peer) into the edge-relaxation step of an A* path search, using a composite cost whose communication term is derived from the instantaneous neighborhood of each node; routing and compute placement are therefore co-optimized within a single search rather than in decoupled stages. The framework is evaluated in simulation with a swarm of 25 robots against two decoupled baselines: a path-only planner that ignores workload and communication costs, and a workload-only scheduler that ignores travel and communication costs. Across 20 randomized trials, the proposed heuristic reduces total swarm energy consumption by approximately 22% relative to the path-only baseline and 9% relative to the workload-only baseline, shortens average task completion time by roughly 20%, and lowers the load imbalance factor from 6.7 (path-only) and 3.2 (workload-only) to 1.9. We report these gains for the tested configurations and delimit their scope: the search retains the asymptotic complexity of standard A*, but path optimality does not extend to the compute-placement decisions, which are locally greedy, and all results are obtained in simulation rather than on hardware.