论文标题
预测地形不平的地面机器人的能耗
Predicting Energy Consumption of Ground Robots On Uneven Terrains
论文作者
论文摘要
优化在田间机器人导航的能源消耗需要能量成本图。但是，获得这样的地图仍然具有挑战性，尤其是对于大型，不均匀的地形。基于物理的能量模型适用于均匀的平坦表面，但不能很好地推广到这些地形。此外，斜率可以使每个位置方向的能源消耗，并增加数据收集和能量预测的复杂性。在本文中，我们以数据驱动的方式解决了这些挑战。我们认为，将地形几何形状和机器人运动方向的功能视为投入和输出预期的能源消耗。该函数表示为基于重新连接的神经网络，其参数是从现场收集的数据中学到的。我们方法的预测准确性在我们的测试环境中在训练过程中看不见的地面真相的12％以内。我们将我们的方法与文献中的基线方法进行比较：使用基于基于物理的模型的方法。我们证明，通过预测误差，我们的方法显着优于10％以上。更重要的是，当应用于来自各种坡度角度和导航方向的新环境测试数据时，我们的方法可以更好地推广。
Optimizing energy consumption for robot navigation in fields requires energy-cost maps. However, obtaining such a map is still challenging, especially for large, uneven terrains. Physics-based energy models work for uniform, flat surfaces but do not generalize well to these terrains. Furthermore, slopes make the energy consumption at every location directional and add to the complexity of data collection and energy prediction. In this paper, we address these challenges in a data-driven manner. We consider a function which takes terrain geometry and robot motion direction as input and outputs expected energy consumption. The function is represented as a ResNet-based neural network whose parameters are learned from field-collected data. The prediction accuracy of our method is within 12% of the ground truth in our test environments that are unseen during training. We compare our method to a baseline method in the literature: a method using a basic physics-based model. We demonstrate that our method significantly outperforms it by more than 10% measured by the prediction error. More importantly, our method generalizes better when applied to test data from new environments with various slope angles and navigation directions.
