Planning energy-efficient paths is an important capability in many robotics applications. Obtaining an energy-cost map for a given environment enables planning such paths between any given pair of locations within the environment. However, efficiently building an energy map is challenging, especially for large environments. Some of the prior work uses physics-based laws (friction and gravity force) to model energy costs across environments. These methods work well for uniform surfaces, but they do not generalize well to uneven terrains. In this letter, we present a method to address this mapping problem in a data-driven fashion for the cases where an aerial image of the environment can be obtained. To efficiently build an energy-cost map, we train a neural network that learns to predict the complete energy maps by combining aerial images and sparse ground robot energy-consumption measurements. Field experiments are performed to validate our results. We show that our method can efficiently build an energy-cost map accurately even across different types of ground robots.

中文翻译：

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使用半监督深度学习从航空图像和地面机器人测量构建能源成本图

规划节能路径是许多机器人应用中的一项重要能力。获得给定环境的能源成本图可以规划环境内任何给定位置对之间的此类路径。然而，有效地构建能量图具有挑战性，尤其是对于大型环境。一些先前的工作使用基于物理的定律（摩擦力和重力）来模拟跨环境的能源成本。这些方法适用于均匀的表面，但它们不能很好地推广到不平坦的地形。在这封信中，我们提出了一种方法，可以在可以获得环境航拍图像的情况下以数据驱动的方式解决此映射问题。为了有效地构建能源成本图，我们训练了一个神经网络，该网络通过结合航拍图像和稀疏地面机器人能耗测量来学习预测完整的能量图。进行现场实验以验证我们的结果。我们表明，即使在不同类型的地面机器人之间，我们的方法也可以有效地准确构建能源成本图。