Energy is an important factor in planning for ground robots, particularly in off‐road environments where the terrain significantly impacts energy usage. Unfortunately, energy costs in these environments are variable and uncertain, making planning difficult. In this paper, we present a method, based on Gaussian process regression (GPR) and known vehicle modeling information, for predicting future path energy costs. The method uses data, collected by a robot operating in a 3D environment with varying terrains, to build a map from inputs (including position, heading, slope, and satellite imagery) to power consumption. The energy costs of future paths are predicted through the summation of power predictions. Importantly, correlations in those predictions are considered to avoid overconfidence. Experimental cross‐validation results demonstrate improved accuracy of path energy cost predictions against a baseline approach, as well as the effect of Gaussian process inputs and kernel choice. Additionally, we show how vehicle modeling can aid in predicting energy costs, particularly when data on the environment is sparse.

中文翻译：

---

通过概率空间映射的越野地面机器人路径能源成本预测

能源是规划地面机器人的重要因素，尤其是在地形会严重影响能源使用的越野环境中。不幸的是，在这些环境中的能源成本是可变的和不确定的，从而使规划变得困难。在本文中，我们提出了一种基于高斯过程回归（GPR）和已知车辆建模信息的预测未来道路能源成本的方法。该方法使用在3D环境中具有变化地形的机器人收集的数据来构建从输入（包括位置，航向，坡度和卫星图像）到功耗的地图。未来路径的能源成本通过功率预测的总和进行预测。重要的是，这些预测中的相关性被认为是避免过度自信。实验交叉验证结果表明，相对于基线方法，路径能量成本预测的准确性有所提高，并且具有高斯过程输入和核选择的影响。此外，我们展示了车辆建模如何帮助预测能源成本，尤其是在环境数据稀疏的情况下。