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- Title
Active stereovision-based robot learning control for object tracking, fixating and grasping.
- Authors
Xiao Nan-Feng
- 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 . Significance: The present approach does not necessitate the tedious CCD camera calibration and the complicated coordinate transformations between the visual frames and the joint space frames, and the experimental results show the effectiveness of the present approach.
- Subjects
ROBOT control systems; AUTOMATIC tracking; ARTIFICIAL neural networks; CCD cameras; MANIPULATORS (Machinery)
- Publication
International Journal of Advanced Manufacturing Technology, 2006, Vol 28, Issue 1/2, p184
- ISSN
0268-3768
- Publication type
Article
- DOI
10.1007/s00170-004-2413-z