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Title: A visually guided robot and a neural network join to grasp slanted objects Proceedings of the 3rd SNN Symposium on Neural Networks
Written by: Smagt P van der, Dev A, Groen F
in: Sep. 1995
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on pages: 144-150
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Editor: B. Kappen and S. Gielen
Publisher: Foundation for Neural Networks, Nijmegen
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Address: Nijmegen, Netherlands
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Abstract: In this paper we introduce a method for model-free monocular visual guidance of a robot arm. The robot arm, with a single camera in its end-effector, should be positioned above a target, with a changing pan and tilt, which is placed against a textured background. It is shown that a trajectory can be planned in visual space by using components of the optic flow, and this trajectory can be translated to joint torques by a self-learning neural network. No model of the robot, camera, or environment is used. The method reaches a high grasping accuracy after only a few trials.