Active stereovision-based robot learning control for object tracking, fixating and grasping

Abstract

In this paper, an active stereovision-based control approach is proposed for a robot to track, fixate, and grasp an object in an unknown environment. First, the functional mapping relationships between those joint angles of the active stereovision system and the three-dimensional (3-D) coordinates of the object are derived and expressed in the workspace frame. Second, two feed-forward neural networks are used to learn those functional mapping relationships, which are used for the robot tracking, fixating, and grasping control. Third, the present approach is verified by experiments based on the active stereovision system which is installed in the end-effecter of the robot. Last, the experimental results confirm the effectiveness of the present approach .