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As mobile robots find increasing use in outdoor applications, designingenergy-efficient robot navigation algorithms is gaining importance. There aretwo primary approaches to energy efficient navigation: Offline approaches relyon a previously built energy map as input to a path planner. Obtaining energymaps for large environments is challenging. Alternatively, the robot cannavigate in an online fashion and build the map as it navigates. Onlinenavigation in unknown environments with only local information is still achallenging research problem. In this paper, we present a novel approach whichaddresses both of these challenges. Our approach starts with a segmented aerialimage of the environment. We show that a coarse energy map can be built fromthe segmentation. However, the absolute energy value for a specific terraintype (e.g. grass) can vary across environments. Therefore, rather than usingthis energy map directly, we use it to build the covariance function for aGaussian Process (GP) based representation of the environment. In the onlinephase, energy measurements collected during navigation are used for estimatingenergy profiles across the environment using GP regression. Coupled with anA*-like navigation algorithm, we show in simulations that our approachoutperforms representative baseline approaches. We also present results fromfield experiments which demonstrate the practical applicability of our method.

Energy-efficient Path Planning for Ground Robots by Combining Air and Ground Measurements