# Deep probabilistic traversability with test-time adaptation for uncertainty-aware planetary rover navigation

## TL;DR

This paper introduces a new method for rover navigation on planetary surfaces that uses machine learning to predict and adapt to uncertain terrain conditions.

## Contribution

The paper presents a deep probabilistic model that integrates uncertainty quantification, exploitation, and adaptation for robust rover path planning.

## Key findings

- The proposed method achieves more robust path planning under novel environmental conditions.
- The model reduces prediction errors by adapting pre-trained models with in-situ traverse experience.
- Simulations in planetary analog terrains validate the effectiveness of the approach.

## 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 its inherent predictive uncertainty increases the risk of wheel slips and permanent rover immobilization. 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 deep probabilistic traversability, an end-to-end probabilistic ML model that predicts slip distributions directly from terrain appearance and geometry. This probabilistic model quantifies uncertainties in slip prediction and exploits them as uncertainty-aware traversability costs in path planning. Its end-to-end nature also allows adaptation of pre-trained models with in-situ traverse experience to reduce prediction errors. We perform extensive simulations in synthetic environments posing representative uncertainties in planetary analog terrains. Simulation results show that our method achieves more robust path planning under novel environmental conditions than existing methods.

The online version contains supplementary material available at 10.1038/s41598-026-40109-1.