Mission planning for a multi-robot team with a solar-powered charging station

Abstract

This paper presents a mission planning problem for a cooperative team of unmanned ground vehicles (UGVs), which includes multiple rovers and a solar-powered mobile charging station. The team is required to start at an initial point and visit a series of objective points before arriving at the final point selected from the set of objective points, where the UGVs will be charged from the solar-powered mobile charging station. This mission is represented as a multi-Hamiltonian Path Problem (mHPP). In order to effectively coordinate the team, an understanding of the mission environment is first obtained by generating a scalar field representation of the solar insolation of the environment from a visual-spectrum image. Then, a cascaded heuristic optimization algorithm, using modified genetic algorithm and particle swarm optimization, is used to generate a time-optimized mission plan for the team of UGVs, which guides each UGV to its assigned objective points and then rendezvous at the final charging location while guaranteeing compliance with the net energy gain constraint. The feasibility and efficiency of the proposed algorithm are verified using an experimental testbed and constructed indoor simulation environments.