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.
Plonski, Patrick A.; Vander Hook, Joshua; Isler, Volkan.
Environment and Solar Map Construction for Solar-Powered Mobile Systems.
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