An active stereo vision-based learning approach for robotic tracking, fixating and grasping control
Xiao, Nan-Feng and Nahavandi, Saeid 2002, An active stereo vision-based learning approach for robotic tracking, fixating and grasping control, in IEEE ICIT' 02 : 2002 IEEE International Conference on Industrial Technology : productivity reincarnation through robotics & automation : 11-14 December 2002, Shangri-La Hotel, Bangkok, Thailand, IEEE Xplore, Piscataway, N.J., pp. 584-587.
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IEEE ICIT' 02 : 2002 IEEE International Conference on Industrial Technology : productivity reincarnation through robotics & automation : 11-14 December 2002, Shangri-La Hotel, Bangkok, Thailand
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In this paper, an active stereo vision-based learning approach is proposed for a robot to track, fixate and grasp an object in unknown environments. First, the functional mapping relationships between the joint angles of the active stereo vision system and the spatial representations of the object are derived and expressed in a three-dimensional workspace frame. Next, the self-adaptive resonance theory-based neural networks and the feedforward neural networks are used to learn the mapping relationships in a self-organized way. Then, the approach is verified by simulation using the models of an active stereo vision system which is installed in the end-effector of a robot. Finally, the simulation results confirm the effectiveness of the present approach.
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