Vision-based Redundancy Control and Image Feature Estimation of Robot Manipulators for Obstacle Avoidance

We propose a new redundancy control method for robot manipulators based on visual servoing, which enables obstacles to be avoided while positioning an end-effector in its target position. The control inputs to the manipulator are calculated both from the image features of the end-effector, other parts of the manipulator and obstacles, that are obtained from a stereo vision system, and from the known kinematic manipulator model. Moreover, our method enables the manipulator to avoid singular configurations simultaneously while taking joints limits of the manipulator into consideration. However, it is not easy to obtain all necessary image features of the manipulator by image processing in a real workspace. We estimate image features that cannot be obtained by image processing or which are hidden by some obstacles. Necessary image features of the manipulator are estimated from calibrated camera parameters and the known kinematic model of the manipulator for obstacle avoidance. Experimental and simulation results reveal the validity and effectiveness of our proposed redundancy control method for avoiding obstacles and singular configurations.