Using Optimal Control Theory to Improve Path Planning For Battery Energy Maximization of an Unmanned Ground Vehicle

Abstract

Solar powered unmanned ground vehicles (SPUGV) can be used to monitor remote points of interest. Heuristic algorithms have been developed for path planning of SPUGVs in known solar environments while considering the battery energy recharge. However, not all environments have detailed solar maps in general, obstructing the energy-efficient path planning. In this paper, a control algorithm is developed to prioritize the battery life of a SPUGV in an unknown solar environment. The algorithm incorporates a switching cost function where one cost function prioritizes the goal position when the battery on the SPUGV is above a set threshold and the other prioritizes finding solar irradiance peaks to charge the battery. Local solar irradiance peaks are identified by a filtering approach from collected data in a local sample area. From simulations, the algorithm results in the SPUGV reaching the point of interest with a higher battery charge than a direct path to the point without any prior solar mapping.