Adaptive vision-based force/position tracking of robotic manipulators interacting with uncertain environment

Abstract

In this paper, we study the fixed-camera visual servoing force/position tracking problem of robotic manipulators with uncertain robotic dynamics, kinematics and camera parameters. The end-effector of the robotic manipulator is in contact with an uncertain rigid constrained surface and the nonlinear depth information of the camera is taken into account. An image-based adaptive hybrid controller is proposed by constructing a modified sliding vector with the environment/force information. The convergence of tracking errors is proved via the Lyapunov stability analysis tools. Numerical simulations are provided to testify the effectiveness of the theoretical approach.