Vision-based Control of Constrained Robots using Neural Networks

Author

Zhao, Y. ; Cheah, C.C.

Author_Institution

Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore

fYear

2006

fDate

5-8 Dec. 2006

Firstpage

1

Lastpage

6

Abstract

Most research on vision and force control of robot manipulators has assumed that the kinematics and constraint surface are known exactly. In this paper, the vision and force control problem of robots with uncertain kinematics, dynamics and constraint is addressed. An adaptive setpoint control law based on neural networks is proposed. Sufficient conditions for choosing the feedback gains are presented to guarantee the stability. Simulation results are presented to demonstrate the effectiveness of the proposed controller

Keywords

adaptive control; feedback; force control; manipulator dynamics; manipulator kinematics; neurocontrollers; robot vision; stability; adaptive setpoint control; constrained robots; constraint surface; feedback gain; force control; manipulator dynamics; manipulator kinematics; neural networks; robot manipulators; stability; vision-based control; Adaptive control; Force control; Kinematics; Manipulator dynamics; Neural networks; Neurofeedback; Programmable control; Robot control; Robot vision systems; Sufficient conditions;

fLanguage

English

Publisher

ieee

Conference_Titel

Control, Automation, Robotics and Vision, 2006. ICARCV '06. 9th International Conference on

Conference_Location

Singapore

Print_ISBN

1-4244-0341-3

Electronic_ISBN

1-4214-042-1

Type

conf

DOI

10.1109/ICARCV.2006.345318

Filename

4150321