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

TL;DR

This paper emphasizes the critical need for autonomous, adaptive decision-making in planetary surface rovers to improve safety and efficiency during long-range exploration, highlighting current shortcomings and future research directions.

Contribution

It identifies gaps in existing autonomous mobility algorithms and advocates for adaptive decision-making methods inspired by human expertise, such as learning from experience and stochastic modeling.

Findings

Analysis of human-guided adaptive planning in planetary missions

Identification of limitations in current autonomous algorithms

Proposed research directions for adaptive decision-making

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.