Self-learning control of cooperative motion for a humanoid robot

Author

Hwang, Yoon Kwon ; Choi, Kook Jin ; Hong, Dae Sun

Author_Institution

Sch. of Mechatronics Eng., Changwon Nat. Univ.

fYear

2006

fDate

15-19 May 2006

Firstpage

475

Lastpage

480

Abstract

This paper deals with the problem of self-learning cooperative motion control for a heavy work of a humanoid robot in the sagittal plane. A model with 27 linked rigid bodies is developed to simulate the system dynamics. A simple genetic algorithm (SGA) is used to find the necessary torques in each joint to obtain a desired cooperative motion, which is to minimize the total energy consumption, for the humanoid robot´s postures of trunk and hands. And the multilayer neural network using the backpropagation is also described in order to control the system in real time

Keywords

adaptive control; backpropagation; cooperative systems; genetic algorithms; humanoid robots; learning systems; mobile robots; motion control; multilayer perceptrons; neurocontrollers; torque; backpropagation; cooperative motion; genetic algorithm; humanoid robot; multilayer neural network; self-learning control; torques; Backpropagation algorithms; Genetics; Humanoid robots; Humans; Joints; Leg; Motion control; Multi-layer neural network; Neural networks; Robot kinematics;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on

Conference_Location

Orlando, FL

ISSN

1050-4729

Print_ISBN

0-7803-9505-0

Type

conf

DOI

10.1109/ROBOT.2006.1641756

Filename

1641756