Energy‐efficient Path Planning for Solar‐powered Mobile Robots*

Abstract

We explore the problem of energy‐efficient, time‐constrained path planning of a solar‐powered robot embedded in a terrestrial environment. Because of the effects of changing weather conditions, as well as sensing concerns in complex environments, a new method for solar power prediction is desirable. We present a method that uses Gaussian Process regression to build a solar map in a data‐driven fashion. Using this map and an empirical model for energy consumption, we perform dynamic programming to find energy‐minimal paths. We validate our map construction and path‐planning algorithms with outdoor experiments, and we perform simulations on our solar maps to further determine the limits of our approach. Our results show that we can effectively construct a solar map using only a simple current measurement circuit and basic GPS localization, and this solar map can be used for energy‐efficient navigation. This establishes informed solar harvesting as a viable option for extending system lifetime even in complex environments with low‐cost commercial solar panels.