Risk-aware Path Planning via Probabilistic Fusion of Traversability Prediction for Planetary Rovers on Heterogeneous Terrains
This work addresses the risk of rover immobilization on heterogeneous terrains, which is critical for autonomous planetary exploration, but it appears incremental as it builds on existing ML methods for traversability prediction.
The paper tackled the problem of erroneous traversability prediction for planetary rovers on heterogeneous terrains, which increases the risk of wheel slip and immobilization, by proposing a new path planning algorithm that uses probabilistic fusion of ML models for terrain classification and slip prediction to generate more feasible paths in simulations.
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.