The Importance of Adaptive Decision-Making for Autonomous Long-Range Planetary Surface Mobility

TL;DR

The paper addresses the need for autonomous long-range mobility on planetary rovers, noting that current onboard autonomy and ground-in-the-loop operations limit productivity. It analyzes flown missions (Apollo, MSL, and M2020) to extract human-driven adaptive decision-making patterns used to mitigate hazards and optimize traverses. It advocates two core capabilities—unassisted learning from past experiences and explicit use of stochastic rover-terrain interaction models—to handle epistemic and aleatoric uncertainties in remote environments. The findings outline research directions to enhance onboard autonomy, improve operator-rover collaboration, and enable expansive exploration of distant planetary surfaces.

Abstract

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.