Publikation

Deep Terrain Estimation for Planetary Rovers

Fabio Vulpi, Annalisa Milella, Florian Cordes, Raúl Domínguez, Giulio Reina

In: 15th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS'20). International Symposium on Artificial Intelligence, Robotics and Automation in Space (iSAIRAS-2020) October 19 Online-Conference 10/2020.

Abstrakt

This research is developed within the ADE (Autono-mous DEcision making in Very Long Traverses) pro-ject 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 scien-tific 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 algo-rithms. 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.

Projekte

i-SAIRAS2020_final-SUBMITTED.pdf (pdf, 784 KB)

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence