提出增强探测车自主适应决策能力，提升远距离行星表面移动的可靠性。
The Importance of Adaptive Decision-Making for Autonomous Long-Range Planetary Surface Mobility
- 借鉴人类专家经验，改进机器人在复杂地形中的自适应规划方法
- 强调从历史经验中自主学习与使用随机世界模型的重要性
- 适合研究行星探测机器人自主导航的学者和工程师
远距离行驶是行星表面探索的重要组成部分。不可预见事件常需人工干预调整移动计划，但该方式难以扩展，未来任务将无法满足需求。当前对自主、自适应决策的研究关注不足。本文回顾了具体行星移动任务中人类引导自适应规划对任务安全与效率的关键作用。受人类专家能力启发，我们指出现有自主移动算法在非结构化环境（如行星表面）中的不足。倡导发展无需人工干预的经验学习能力和更强的随机世界模型依赖性。本工作旨在揭示提升地面规划工具与探测车机载长期自主算法的潜在研究方向。
原文摘要 · Abstract (English)
Long-distance driving is an important component of planetary surface exploration. Unforeseen events often require human operators to adjust mobility plans, but this approach does not scale and will be insufficient for future missions. Interest in self-reliant rovers is increasing, however the research community has not yet given significant attention to autonomous, adaptive decision-making. In this paper, we look back at specific planetary mobility operations where human-guided adaptive planning played an important role in mission safety and productivity. Inspired by the abilities of human experts, we identify shortcomings of existing autonomous mobility algorithms for robots operating in off-road environments like planetary surfaces. We advocate for adaptive decision-making capabilities such as unassisted learning from past experiences and more reliance on stochastic world models. The aim of this work is to highlight promising research avenues to enhance ground planning tools and, ultimately, long-range autonomy algorithms on board planetary rovers.
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