Title :
Uncalibrated Direct Visual Servoing Based on State Estimation
Author :
Li, Fei ; Xie, Hualong ; Xu, Xinhe
Author_Institution :
Inst. of Artificial Intelligence & Robotics, Northeastern Univ., Shenyang
Abstract :
In order to cope with the low sampling rate of standard cameras and the time delay introduced by image processing, a predictor is constructed to estimate future positions of a moving target using BP neural network. The controller presented adopts adaptive PD control algorithm with uncertain gravity compensation and computes the demand torques for the robot based on current joint angles and joint angle rates. The direct visual servoing system is proved to be asymptotic stable with Lyapunov stability theory. The system does not require a robot kinematics model or a calibrated camera model. Simulation results show that this method provides a good steady-state tracking behaviour and keeps good robustness and adaptability at the same time. Additionally, the algorithm is very easy to be implemented with low computational complexity
Keywords :
Lyapunov methods; PD control; adaptive control; asymptotic stability; backpropagation; image motion analysis; neural nets; robot vision; state estimation; Lyapunov stability theory; adaptive PD control algorithm; asymptotic stability; backpropagation neural networks; computational complexity; image processing; state estimation; time delay; uncalibrated direct visual servoing; uncertain gravity compensation; Cameras; Delay effects; Delay estimation; Image processing; Image sampling; Neural networks; Robot vision systems; State estimation; Torque control; Visual servoing; BP neural network; Direct visual servoing; Gravity compensation; State estimation; Uncalibrated eye-in-hand visual servoing;
Conference_Titel :
Mechatronics and Automation, Proceedings of the 2006 IEEE International Conference on
Conference_Location :
Luoyang, Henan
Print_ISBN :
1-4244-0465-7
Electronic_ISBN :
1-4244-0466-5
DOI :
10.1109/ICMA.2006.257400