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）在自主探测车运行于可变形地形时评估穿越性方面发挥着关键作用，但难以避免预测误差。特别是在地质特征随空间变化的异构地形上，错误的穿越性预测可能更加显著，从而增加探测车车轮打滑或陷车不可恢复的风险。本文提出一种新型路径规划算法，明确考虑了此类预测误差。核心思想是将用于地形分类与滑移预测的两种独特ML模型进行概率融合，形成单一分布。该分布可提供适应异构地形的多模态滑移分布，并进一步支持统计风险评估，从而导出用于路径规划的风险感知穿越代价。大量仿真实验表明，与现有方法相比，所提方法能够在异构地形上生成更可行的路径。