Obstacle negotiation learning for a compliant wheel-on-leg robot
Résumé
Building versatile control for wheel-on-leg robots on various uneven terrains still constitutes a challenge. In this paper, we propose to combine a continuous state space Q-learning algorithm with a compliant wheeled locomotion structure capable of measuring forces applied by the environment. These forces are used to choose a behavior from the modulation of actuation distribution to simple configuration adjustments. Then, the robot does not need any prior knowledge on the ground geometry and is able to react to non-anticipated obstacles. The learned policy proves to be generic and allows the robot to negotiate complex obstacles that were not seen during learning.
Domaines
Automatique / RobotiqueOrigine | Fichiers produits par l'(les) auteur(s) |
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