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This paper addresses the robust visual tracking of multi-feature points for a 3D manipulator with unknown intrinsic and extrinsic parameters of the vision system.This class of control systems are highly nonlinear control systems characterized as time-varying and strong coupling in states and unknown parameters.It is first pointed out that not only is the Jacobian image matrix nonsingular,but also its minimum singular value has a positive limit.This provides the foundation of kinematics and dynamics control of manipulators with visual feedback.Second,the Euler angle expressed rotation transformation is employed to estimate a subspace of the parameter space of the vision system.Based on the two results above,and arbitrarily chosen parameters in this subspace,the tracking controllers are proposed so that the image errors can be made as small as desired so long as the control gain is allowed to be large.The controller does not use visual velocity to achieve high and robust performance with low sampling rate of the vision system.The obtained results are proved by Lyapunov direct method.Experiments are included to demonstrate the effectiveness of the proposed controller.
This paper addresses the robust visual tracking of multi-feature points for a 3D manipulator with unknown intrinsic and extrinsic parameters of the vision system. This class of control systems are highly nonlinear control systems characterized as time-varying and strong coupling in states and unknown parameters .It is first pointed out that not only is the Jacobian image matrix nonsingular, but also its minimum singular value has a positive limit. This provides the foundation of kinematics and dynamics control of manipulators with visual feedback. Second, the Euler angle represented rotation transformation is employed to estimate a subspace of the parameter space of the vision system. Based on the two results above, and arbitrarily chosen parameters in this subspace, the tracking controllers are proposed so that the image errors can be made as small as desired so long as the control gain is allowed to be large. The controller does not use visual velocity to achieve high and robust performance with low sampling rate of the vision system. The obtained results were proved by Lyapunov direct method. Experiments are included to demonstrate the effectiveness of the proposed controller.