Environment and Solar Map Construction for Solar-Powered Mobile Systems
Energy harvesting using solar panels can significantly increase the operational life of mobile robots. If a map of expected solar power is available, energy efficient paths can be computed. However, estimating this map is a challenging task, especially in complex environments. In this paper, we show how the problem of estimating solar power can be decomposed into the steps of magnitude estimation and solar classification. Then we provide two methods to classify a position as sunny or shaded: a simple data-driven Gaussian Process method, and a method which estimates the geometry of the environment as a latent variable. Both of these methods are practical when the training measurements are sparse, such as with a simple robot that can only measure solar power at its own position. We demonstrate our methods on simulated, randomly generated environments. We also justify our methods with measured solar data by comparing the constructed height maps with satellite images of the test environments, and in a cross-validation step where we examine the accuracy of predicted shadows and solar current.