SUMMARYWheel slip prediction on rough terrain is crucial for secure, long-term operations of planetary exploration rovers. Although rough, unstructured terrain hampers mobility, prediction by modeling wheel–terrain interactions remains difficult owing to unclear terrain conditions and complexities of terramechanics models. This study proposes a vision-based approach with machine learning for predicting wheel slip risk by estimating the slope from 3D information and classifying terrain types from image information. It considers the slope estimation accuracy for risk prediction under sharp increases in wheel slip due to inclined ground. Experimental results obtained with a rover testbed on several terrain types validate this method.

中文翻译：

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行星探测漫游车的地形相关滑动风险预测

摘要粗糙地形上的车轮滑移预测对于行星探测漫游车的安全、长期运行至关重要。尽管粗糙、非结构化的地形阻碍了机动性，但由于地形条件不明确和地形力学模型的复杂性，通过模拟车轮-地形相互作用进行预测仍然很困难。本研究提出了一种基于视觉的机器学习方法，通过根据 3D 信息估计坡度并根据图像信息对地形类型进行分类，从而预测车轮滑移风险。它考虑了坡度估计精度，用于在由于倾斜地面导致车轮滑移急剧增加的情况下进行风险预测。在几种地形类型上使用流动站测试台获得的实验结果验证了这种方法。