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

2024-09-28

arXiv.org

AI Summary

Current deep-space planetary surface navigation faces critical limitations in adaptive decision-making under unforeseen environmental conditions and excessive reliance on ground-based human intervention. To address this, we propose an adaptive decision-making framework inspired by human operators’ on-orbit adaptive planning expertise. The framework integrates unsupervised experience reuse, Bayesian stochastic world modeling, and uncertainty-aware navigation to enable mission-level online replanning and real-time environmental perception. For the first time, it systematically distills expert decision logic in unstructured extraterrestrial environments, establishing a dual-core paradigm of “experience-driven reasoning + probabilistic modeling.” Experimental results demonstrate significant improvements in robustness and response latency for autonomous decision-making over unknown terrain. This work provides a verifiable technical pathway for co-evolution of onboard long-range autonomy algorithms and ground-based planning tools.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Reasoning under Uncertainty: Sequential Decision Making
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Adaptive Decision-Making
• Autonomous Navigation
• Complex Environments

Innovation

• Autonomous Driving
• Machine Learning
• Random Models