Machine learning (ML) plays a crucial role in assessing traversability for autonomous rover operations on deformable terrains but suffers from inevitable prediction errors. Especially for heterogeneous terrains where the geological features vary from place to place, erroneous traversability prediction can become more apparent, increasing the risk of unrecoverable rover's wheel slip and immobilization. In this work, we propose a new path planning algorithm that explicitly accounts for such erroneous prediction. The key idea is the probabilistic fusion of distinctive ML models for terrain type classification and slip prediction into a single distribution. This gives us a multimodal slip distribution accounting for heterogeneous terrains and further allows statistical risk assessment to be applied to derive risk-aware traversing costs for path planning. Extensive simulation experiments have demonstrated that the proposed method is able to generate more feasible paths on heterogeneous terrains compared to existing methods.
翻译:机器学习(ML)在评估在变形地形上自主越野作业的可穿越性方面发挥着关键作用,但必然会发生预测错误。特别是对于地质特征各异的多变地形,错误的跨地预测会变得更加明显,增加无法回收的越野轮滑和移动的风险。在这项工作中,我们提出了一条新的路径规划算法,明确说明这种错误预测。关键的想法是将独特的多路模型在地形类型分类和滑坡预测方面的概率性结合成一个单一分布。这为我们提供了多种不同地形的多式差错分布核算,并进一步允许进行统计风险评估,以得出道路规划所需的风险-认识穿行成本。广泛的模拟实验表明,与现有方法相比,拟议的方法能够在多地形上创造更可行的路径。</s>