Risk-aware Path Planning via Probabilistic Fusion of Traversability Prediction for Planetary Rovers on Heterogeneous Terrains

TL;DR

This paper introduces a probabilistic fusion-based path planning method for planetary rovers that explicitly accounts for ML prediction errors on heterogeneous terrains, improving path feasibility and safety.

Contribution

It presents a novel risk-aware path planning algorithm that fuses ML models into a multimodal distribution for better terrain and slip prediction in heterogeneous environments.

Findings

Generated more feasible paths on heterogeneous terrains

Improved safety by accounting for prediction errors

Outperformed existing path planning methods in simulations

Abstract

Machine learning (ML) plays a crucial role in assessing traversability for autonomous rover operations on deformable terrains but suffers from inevitable prediction errors. Especially for heterogeneous terrains where the geological features vary from place to place, erroneous traversability prediction can become more apparent, increasing the risk of unrecoverable rover's wheel slip and immobilization. In this work, we propose a new path planning algorithm that explicitly accounts for such erroneous prediction. The key idea is the probabilistic fusion of distinctive ML models for terrain type classification and slip prediction into a single distribution. This gives us a multimodal slip distribution accounting for heterogeneous terrains and further allows statistical risk assessment to be applied to derive risk-aware traversing costs for path planning. Extensive simulation experiments have demonstrated that the proposed method is able to generate more feasible paths on heterogeneous terrains compared to existing methods.