Maximizing area coverage for multi-robots using cloud

Abstract

This paper presents an integrated framework for multi-robot task allocation and area coverage using an MQTT-based cloud communication architecture for waypoint delivery. A cost-based allocation strategy is proposed, incorporating factors such as battery level, robot age, approximate path distance, and number of turns required for coverage to optimize robot-to-region assignments using the Hungarian algorithm. For coverage, an A*-assisted Boustrophedon path planning algorithm is utilized, improving coverage efficiency and enabling effective navigation. The system is implemented using ROS2 with MQTT-based cloud communication for waypoint delivery and validated through simulations and hardware experiments. Across the simulated cases, the exact assignment attains a lower total allocation cost than distance-only, uniform, and greedy battery-aware selection rules scored on the same objective, with a mean saving of 11.0% over the greedy baseline, and the hardware trials achieved 93.3–96.4% area coverage. The cloud component is demonstrated as a feasible and practical waypoint delivery mechanism with measured latency of 120 to 180 ms and zero message loss; a rigorous comparison against edge or onboard alternatives is identified as future work. The primary contribution of this work is an integrated, cloud-assisted multi-robot coverage framework that couples cost-aware task allocation with A*-assisted coverage path planning, and validates the complete pipeline in both simulation and real-world hardware experiments.