It is interesting to observe that humans are able to manipulate an object easily and skillfully without the exact knowledge of the object, contact points, or kinematics of our fingers. However, research so far on multifingered robot control has assumed that the kinematics and contact points of the fingers are known exactly. In many applications of multifingered robot hands, the kinematics and contact points of the fingers are uncertain and structures of the Jacobian matrices are unknown. In this paper, we propose an adaptive neural network (NN) Jacobian controller for multifingered robot hand with uncertainties in kinematics, Jacobian matrices, and dynamics. It is shown that using NNs, the uniform ultimate boundedness of the position error can be achieved in the presence of the uncertainties. Simulation results are presented to illustrate the performance of the proposed controller.

In many applications of robot manipulators, the end-effector is required to make contact with environment. In these applications, it is necessary to control not only the position but also the interaction force between the robot end-effector and environment. Most research so far on motion and force tracking control has assumed that the kinematics and constraint surface are exactly known. In this paper, we propose a visually-servoed adaptive Jacobian controller for motion and force tracking control with structural uncertainties in kinematics, dynamics and constraint surface. It is shown that uniform ultimate boundedness of the tracking errors can be guaranteed. Simulation results are presented to illustrate the performance of the proposed control law.

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

Most research so far on motion and force tracking control of robots has assumed that the kinematics and dynamics are exactly known. In this paper, we propose an adaptive Jacobian controller for motion and force tracking with uncertainties in kinematics and dynamics. It is shown that the robot end-effector can track the desired position and force trajectories with the uncertain parameters updated online. Simulation results are presented to illustrate the performance of the proposed control law

Most research so far on of multi-fingered robot control has assumed that the kinematics is known exactly. However, in many applications of multi-fingered robot hands, the kinematics is uncertain. In this paper, a vision based control problem for multi-fingered robot hands with uncertain kinematics, dynamics and camera model is addressed. Adaptive neural network control law is proposed and it is shown that the stability can be achieved in the presence of the uncertainties. Sufficient conditions for choosing the feedback gains are presented to guarantee the stability

Most research so far on motion and force tracking has assumed that the kinematics and dynamics are exactly known. In this paper, we propose an visually-servoed adaptive Jacobian controller for motion and force tracking with uncertainties in kinematics, dynamics and camera model. It is shown that the robot end-effector can track the desired position and force trajectories with the uncertain parameters updated online. Simulation results are presented to illustrate the performance of the proposed control law

Most research so far on vision control of robot manipulator has been focused on free motion control. In order to expand the applications of vision-based controllers, it is necessary to control the force in addition to the motion. The vision and force control problem of robot manipulators with uncertain kinematics, dynamics, camera model and constraint surface is addressed. An adaptive set point control law is proposed for vision and force control. Sufficient conditions for choosing the feedback gains are presented to guarantee the stability. It is shown that the stability can be achieved in the presence of the uncertainties.