Deep Probabilistic Traversability with Test-time Adaptation for Uncertainty-aware Planetary Rover Navigation

TL;DR

This paper introduces a deep probabilistic ML framework for planetary rover navigation that quantifies and adapts to terrain uncertainties, improving path planning robustness in uncertain environments.

Contribution

It presents an end-to-end probabilistic model that predicts slip distributions, incorporates uncertainty into planning, and adapts with in-situ data for enhanced rover navigation.

Findings

Outperforms existing methods in uncertain terrains

Effectively quantifies slip prediction uncertainties

Adapts models with in-situ traverse experience

Abstract

Traversability assessment of deformable terrain is vital for safe rover navigation on planetary surfaces. Machine learning (ML) is a powerful tool for traversability prediction but faces predictive uncertainty. This uncertainty leads to prediction errors, increasing the risk of wheel slips and immobilization for planetary rovers. To address this issue, we integrate principal approaches to uncertainty handling -- quantification, exploitation, and adaptation -- into a single learning and planning framework for rover navigation. The key concept is \emph{deep probabilistic traversability}, forming the basis of an end-to-end probabilistic ML model that predicts slip distributions directly from rover traverse observations. This probabilistic model quantifies uncertainties in slip prediction and exploits them as traversability costs in path planning. Its end-to-end nature also allows adaptation of pre-trained models with in-situ traverse experience to reduce uncertainties. We perform extensive simulations in synthetic environments that pose representative uncertainties in planetary analog terrains. Experimental results show that our method achieves more robust path planning under novel environmental conditions than existing approaches.