For large-scale tasks, a multi-robot system can effectively improve efficiency, especially for heterogeneous multi-robot system (HMRS), the system can benefit more from utilizing different capabilities, mobility and functionality of each robot in the HMRS.
In this research, we introduce a worker-station HMRS consisting of multiple workers with limited energy or consumable capacity for actual work and one station with enough resources for supply replenishment to solve the multi-robot coverage path planning (mCPP) problem.
We design an efficient modeling approach of the mCPP problem for worker-station HMRS, and propose a novel end-to-end decentralized online planning approach based on Deep Reinforcement Learning (DRL), which solves coverage planning for workers as well as rendezvous planning for station, with the ability of collision avoidance with dynamic interferers in the environment.
With the help of the AirStation with massive training capability, our DRL based method successfully generates cooperative coordination behaviors towards the coverage task for each robot in the HMRS, leading to a satisfying and competitive performance.