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Optimizing energy consumption for robot navigation in fields requiresenergy-cost maps. However, obtaining such a map is still challenging,especially for large, uneven terrains. Physics-based energy models work foruniform, flat surfaces but do not generalize well to these terrains.Furthermore, slopes make the energy consumption at every location directionaland add to the complexity of data collection and energy prediction. In thispaper, we address these challenges in a data-driven manner. We consider afunction which takes terrain geometry and robot motion direction as input andoutputs expected energy consumption. The function is represented as aResNet-based neural network whose parameters are learned from field-collecteddata. The prediction accuracy of our method is within 12% of the ground truthin our test environments that are unseen during training. We compare our methodto a baseline method in the literature: a method using a basic physics-basedmodel. We demonstrate that our method significantly outperforms it by more than10% measured by the prediction error. More importantly, our method generalizesbetter when applied to test data from new environments with various slopeangles and navigation directions.

Predicting Energy Consumption of Ground Robots On Uneven Terrains