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.
Real World Computing Partnership
Dutch Foundation for Neural Networks
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© 1995 Springer-Verlag London Limited
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van der Smagt, P., Dev, A., Groen, F.C.A. (1995). A visually guided robot and a neural network join to grasp slanted objects. In: Kappen, B., Gielen, S. (eds) Neural Networks: Artificial Intelligence and Industrial Applications. Springer, London. https://doi.org/10.1007/978-1-4471-3087-1_25
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DOI: https://doi.org/10.1007/978-1-4471-3087-1_25
Publisher Name: Springer, London
Print ISBN: 978-3-540-19992-2
Online ISBN: 978-1-4471-3087-1
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