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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. C 2013 Wiley Periodicals, Inc.

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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.

Robotic routers (mobile robots with wireless communication capabilities) can create an adaptive wireless network and provide communication services for mobile users ondemand. Robotic routers are especially appealing for applications in which there is a single mobile user whose connectivity to a base station must be maintained in an environment that is large compared to the wireless range.In this paper, we study the problem of computing motion strategies for robotic routers in such scenarios, as well as the minimum number of robotic routers necessary to enact our motion strategies. Assuming that the routers are as fast as the user, we present an optimal solution for cases where the environment is a simply-connected polygon, a constant factor approximation for cases where the environment has a single obstacle, and an O(h) approximation for cases where the environment has h circular obstacles. The O(h) approximation also holds for cases where the environment has h arbitrary polygonal obstacles, provided they satisfy certain geometric constraints -e.g. when the set of their minimum bounding circles is disjoint.