Dynamic recurrent neural network for biped robot equilibrium control: preliminary results - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2005

Dynamic recurrent neural network for biped robot equilibrium control: preliminary results

Résumé

The purpose of the research addressed in this paper is to develop a real time neural control algorithm for the balance of a biped robot. Our approach is based on dynamic recurrent neural networks and dynamic backpropagation through time algorithm. The neural architecture and its learning process are validated on the control of the ROBIAN biped torso. The neural controller described is trained to compensate, by the torso's joint motions, applied external perturbations. The algorithm is embedded in the real time electronic unit of the robot and online learning is achieved. The learning behavior and the control performances are the preliminary results presented in this paper. These experimental results show the ability and efficiency of the proposed approach.
Fichier non déposé

Dates et versions

hal-00523322 , version 1 (04-10-2010)

Identifiants

  • HAL Id : hal-00523322 , version 1

Citer

Vincent Scesa, Bachar Mohamed, Patrick Henaff, Fethi Ben Ouezdou. Dynamic recurrent neural network for biped robot equilibrium control: preliminary results. IEEE International Conference on Robotics and Automation, ICRA 2005, Apr 2005, Barcelone, Spain. pp.4125-4130. ⟨hal-00523322⟩

Collections

CNRS UVSQ
68 Consultations
0 Téléchargements

Partager

Gmail Facebook X LinkedIn More