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arXiv 2409.00641cs.RO
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面向不确定性感知的行星探测车导航的深度概率可通行性及测试时自适应

Deep Probabilistic Traversability with Test-time Adaptation for Uncertainty-aware Planetary Rover Navigation

Keio University（庆应义塾大学）；OMRON SINIC X Corporation（欧姆龙 SINIC X 公司）

Masafumi Endo, Tatsunori Taniai, Genya Ishigami

备注：
8 pages, 4 figures. Submitted to IEEE Robotics and Automation Letters (RA-L)
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AI总结：

本文提出深度概率可通行性框架，将不确定性量化、利用与自适应统一到端到端概率机器学习模型中，直接预测滑移分布并用于路径规划，通过现场经验自适应减少不确定性，在行星类比地形仿真中实现了比现有方法更鲁棒的导航。

摘要：

可变形地形的可通行性评估对于行星表面探测车的安全导航至关重要。机器学习（ML）是可通行性预测的有力工具，但面临预测不确定性。这种不确定性会导致预测误差，从而增加行星探测车车轮打滑和无法移动的风险。为解决这一问题，我们将不确定性处理的主要方法——量化、利用和自适应——整合到一个用于探测车导航的统一学习与规划框架中。其核心概念是深度概率可通行性，它构成了一个端到端概率机器学习模型的基础，该模型直接从探测车的行进观测中预测滑移分布。该概率模型量化了滑移预测中的不确定性，并将其作为路径规划中的可通行性代价加以利用。其端到端特性还允许利用现场行进经验对预训练模型进行自适应，以减少不确定性。我们在模拟环境中进行了大量仿真实验，这些环境呈现了行星类比地形中具有代表性的不确定性。实验结果表明，与现有方法相比，我们的方法在新环境条件下实现了更鲁棒的路径规划。

英文摘要：

Traversability assessment of deformable terrain is vital for safe rover navigation on planetary surfaces. Machine learning (ML) is a powerful tool for traversability prediction but faces predictive uncertainty. This uncertainty leads to prediction errors, increasing the risk of wheel slips and immobilization for planetary rovers. To address this issue, we integrate principal approaches to uncertainty handling -- quantification, exploitation, and adaptation -- into a single learning and planning framework for rover navigation. The key concept is \emph{deep probabilistic traversability}, forming the basis of an end-to-end probabilistic ML model that predicts slip distributions directly from rover traverse observations. This probabilistic model quantifies uncertainties in slip prediction and exploits them as traversability costs in path planning. Its end-to-end nature also allows adaptation of pre-trained models with in-situ traverse experience to reduce uncertainties. We perform extensive simulations in synthetic environments that pose representative uncertainties in planetary analog terrains. Experimental results show that our method achieves more robust path planning under novel environmental conditions than existing approaches.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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