Direct Mapping of Visual Input to Motor Torques

Author

Neubert, Jeremiah J. ; Ferrier, Nicola J.

Author_Institution

Dept. of Eng., Cambridge Univ.

Volume

4

fYear

0

fDate

0-0 0

Firstpage

634

Lastpage

638

Abstract

Most methods for visual control of robots formulate the robot command in joint or Cartesian space. To move the robot these commands are remapped to motor torques usually requiring a dynamic model of the robot. In this paper we present a method for parameterizing joint torques and learning to map visual input directly to them. The system is implemented and used to control a CRS 465 robot. The results of the implementation demonstrate that the parameterization of the torques allows both the motion and position of the robot´s end effectors to be controlled. Moreover, it is shown that it is possible to map visual input directly to joint torques

Keywords

end effectors; learning (artificial intelligence); manipulator dynamics; manipulator kinematics; motion control; neurocontrollers; position control; robot vision; torque control; CRS 465 robot; Cartesian space; direct visual input mapping; joint torque parameterization; learning; motion control; motor torques; position control; robot command; robot dynamic model; robot end effectors; robot visual control; Control systems; Data mining; End effectors; Motion control; Orbital robotics; Robot control; Robot motion; Shape control; Table lookup; Torque control;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2006. ICPR 2006. 18th International Conference on

Conference_Location

Hong Kong

ISSN

1051-4651

Print_ISBN

0-7695-2521-0

Type

conf

DOI

10.1109/ICPR.2006.452

Filename

1699921