Abstract
This paper studies coordination control for an uncalibrated single eye-in-hand robotic system to track an object in 3-D space with simultaneous translations and rotations. A set of object features is properly defined to derive an invertible nonlinear visual mapping model from visual feedback to robot control. A novel fuzzy neural network is proposed to realize the nonlinear mapping model effectively, which is essential to implement six-degree-of-freedom control of robot hand. Simulation results show the performance of the proposed method.
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© 2004 Springer-Verlag Berlin Heidelberg
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Su, J., Pan, Q., Luo, Z. (2004). Full-DOF Calibration-Free Robotic Hand-Eye Coordination Based on Fuzzy Neural Network. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_2
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DOI: https://doi.org/10.1007/978-3-540-28648-6_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
Online ISBN: 978-3-540-28648-6
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