Energy harvesting aware path planning for ambiently-powered multi-robot systems

Abstract

Autonomous multi-robot systems are increasingly deployed in energy-variable environments where sustained operation depends on efficient energy harvesting. Traditional path-planning methods overlook ambient energy variability and inter-robot coordination, reducing overall efficiency. This paper introduces Hierarchically Conditioned Multi-Agent Reinforcement Learning with Meta-Differential Evolution (H-CMARL-DE) for energy harvesting-aware multi-robot path planning. The method centralizes only meta-level parameters priority order, shaping weights, and subgoal settings, while policy learning and execution remain decentralized, ensuring scalability and safety. Implemented in ROS–Gazebo, the system operates in a closed-loop observe–plan–act cycle, enabling robots to coordinate via hierarchical time–space reservations while adapting to dynamic obstacles and energy fields. Simulations demonstrate up to 240% improvement in energy-harvesting efficiency with minimal increase in path length compared to non-energy-harvesting-aware approaches, confirming H-CMARL-DE’s robustness and adaptability for long-term cooperative operation in resource-constrained environments.