用短时姿态数据识别地形，实现机器人自适应换策略。
Adaptive Policy Switching of Two-Wheeled Differential Robots for Traversing over Diverse Terrains
- 基于姿态数据的短期观测，动态判断地形类型。
- 70步窗口下地形分类准确率超98%。
- 适合无人探测、复杂地形移动的机器人系统。
探索月球熔岩管需机器人自主通行。由于预训练策略无法覆盖所有地形，本文目标是实现自适应策略切换：根据当前地形特征选择专用模型。研究考察了导航过程中采集的姿态相关观测是否可有效估计地形类型。通过近端策略优化（PPO）微调预训练策略，并在模拟熔岩管环境中收集机器人在平坦与粗糙地形上的3D姿态数据。分析发现，俯仰角标准差在两类地形间有显著差异。采用高斯混合模型（GMM）评估不同窗口尺寸下的分类效果，使用70步窗口时准确率超过98%。结果表明，短期姿态数据足以实现可靠地形估计，为自适应策略切换提供基础。
原文摘要 · Abstract (English)
Exploring lunar lava tubes requires robots to traverse without human intervention. Because pre-trained policies cannot fully cover all possible terrain conditions, our goal is to enable adaptive policy switching, where the robot selects an appropriate terrain-specialized model based on its current terrain features. This study investigates whether terrain types can be estimated effectively using posture-related observations collected during navigation. We fine-tuned a pre-trained policy using Proximal Policy Optimization (PPO), and then collected the robot's 3D orientation data as it moved across flat and rough terrain in a simulated lava-tube environment. Our analysis revealed that the standard deviation of the robot's pitch data shows a clear difference between these two terrain types. Using Gaussian mixture models (GMM), we evaluated terrain classification across various window sizes. An accuracy of more than 98% was achieved when using a 70-step window. The result suggests that short-term orientation data are sufficient for reliable terrain estimation, providing a foundation for adaptive policy switching.
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