Autonomous space rovers face significant challenges when navigating deformable and heterogeneous terrains during space exploration. The variability in terrain types, influenced by different soil properties, often results in severe wheel slip, compromising navigation efficiency and potentially leading to entrapment. This paper proposes SlipNet, an approach for predicting slip in segmented regions of heterogeneous deformable terrain surfaces to enhance navigation algorithms. Unlike previous methods, SlipNet does not depend on prior terrain classification, reducing prediction errors and misclassifications through dynamic terrain segmentation and slip assignment during deployment while maintaining a history of terrain classes. This adaptive reclassification mechanism has improved prediction performance. Extensive simulation results demonstrate that our model (DeepLab v3+ + SlipNet) achieves better slip prediction performance than the TerrainNet, with a lower mean absolute error (MAE) in five terrain sample tests.

Powered hip exoskeletons have shown the ability for locomotion assistance during treadmill walking. However, providing suitable assistance in real-world walking scenarios which involve changing terrain remains challenging. Recent research suggests that forecasting the lower limb joint's angles could provide target trajectories for exoskeletons and prostheses, and the performance could be improved with visual information. In this letter, We share a real-world dataset of 10 healthy subjects walking through five common types of terrain with stride-level label. We design a network called Sandwich Fusion Transformer for Image and Kinematics (SFTIK), which predicts the thigh angle of the ensuing stride given the terrain images at the beginning of the preceding and the ensuing stride and the IMU time series during the preceding stride. We introduce width-level patchify, tailored for egocentric terrain images, to reduce the computational demands. We demonstrate the proposed sandwich input and fusion mechanism could significantly improve the forecasting performance. Overall, the SFTIK outperforms baseline methods, achieving a computational efficiency of 3.31 G Flops, and root mean square error (RMSE) of 3.445 \textpm \ 0.804\textdegree \ and Pearson's correlation coefficient (PCC) of 0.971 \textpm\ 0.025. The results demonstrate that SFTIK could forecast the thigh's angle accurately with low computational cost, which could serve as a terrain adaptive trajectory planning method for hip exoskeletons. Codes and data are available at https://github.com/RuoqiZhao116/SFTIK.