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.

翻译：长距离行驶是行星表面探测的重要组成部分。意外事件常需人工操作员调整移动计划，但这种方法不具备可扩展性，难以满足未来任务需求。尽管对自主探测车的关注日益增加，但研究界尚未对自适应自主决策给予足够重视。本文回顾了特定行星移动任务中人工引导的自适应规划对任务安全性与作业效率的关键作用。受人类专家能力的启发，我们指出了现有自主移动算法在行星表面等越野环境中运行的缺陷。我们主张发展自适应决策能力，包括从过往经验中自主学习以及更充分地利用随机世界模型。本研究旨在指明增强地面规划工具及最终实现行星探测车长距离自主算法的前景研究方向。