DEEP TERRAIN ESTIMATION FOR PLANETARY ROVERS

This research is developed within the ADE (Autonomous DEcision making in Very Long Traverses) project funded by the European Union to develop novel technologies for future space robotics missions. ADE's objective is to increase the range of traveled distance of a planetary exploration rover up to 1 km/sol, while ensuring at the same time optimal scientific data return. In this context, the ability to sense and classify the type of traversed surface plays a critical role. The paper presents a terrain classifier that is based on the measurements of motion states and wheel forces and torques to predict characteristics relevant for locomotion using machine and deep learning algorithms. The proposed approach is tested and demonstrated in the field using the SherpaTT rover, that uses an active suspension system to adapt to terrain unevenness.