  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.
