Neural-Dynamic-Method-Based Dual-Arm CMG Scheme With Time-Varying Constraints Applied to Humanoid Robots
In: IEEE transactions on neural networks and learning systems, Jg. 26 (2015-09-05), Heft 12
Online
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Zugriff:
We propose a dual-arm cyclic-motion-generation (DACMG) scheme by a neural-dynamic method, which can remedy the joint-angle-drift phenomenon of a humanoid robot. In particular, according to a neural-dynamic design method, first, a cyclic-motion performance index is exploited and applied. This cyclic-motion performance index is then integrated into a quadratic programming (QP)-type scheme with time-varying constraints, called the time-varying-constrained DACMG (TVC-DACMG) scheme. The scheme includes the kinematic motion equations of two arms and the time-varying joint limits. The scheme can not only generate the cyclic motion of two arms for a humanoid robot but also control the arms to move to the desired position. In addition, the scheme considers the physical limit avoidance. To solve the QP problem, a recurrent neural network is presented and used to obtain the optimal solutions. Computer simulations and physical experiments demonstrate the effectiveness and the accuracy of such a TVC-DACMG scheme and the neural network solver.
Titel: |
Neural-Dynamic-Method-Based Dual-Arm CMG Scheme With Time-Varying Constraints Applied to Humanoid Robots
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Autor/in / Beteiligte Person: | Li, Yuanqing ; Luo, Yamei ; Li, Zhijun ; Zhang, Zhijun ; Zhang, Yunong |
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Zeitschrift: | IEEE transactions on neural networks and learning systems, Jg. 26 (2015-09-05), Heft 12 |
Veröffentlichung: | 2015 |
Medientyp: | unknown |
ISSN: | 2162-2388 (print) |
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