Abstract
This paper presents an approach to predict energy consumption in mobility systems for wheeled ground robots. The energy autonomy is a critical problem for various battery-powered systems. Specifically, the consumption prediction in mobility systems, which is difficult to obtain due to its complex interactivity, can be used to improve energy efficiency. To address this problem, a self-supervised approach is presented which considers terrain geometry and soil types. Especially, this paper analyzes soil types which affect energy usage models, then proposes a prediction scheme based on terrain type recognition and simple consumption modeling. The developed vibration-based terrain classifier is validated with a field test in diverse volcanic terrain.
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Otsu, K., Kubota, T. (2016). Energy-Aware Terrain Analysis for Mobile Robot Exploration. In: Wettergreen, D., Barfoot, T. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-319-27702-8_25
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DOI: https://doi.org/10.1007/978-3-319-27702-8_25
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