期刊
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
卷 29, 期 2, 页码 328-343出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.rcim.2012.09.004
关键词
Repetitive motion planning; Zhang et al's neural-dynamic method; Joint-acceleration level; Simplified LVI-based primal-dual; neural network; Multi-link planar robot arms
资金
- National Natural Science Foundation of China [61075121, 60935001]
- Fundamental Research Funds for the Central Universities of China
In this paper, we propose a novel repetitive motion planning (RMP) scheme at the joint-acceleration level (termed, the acceleration-level RMP scheme, the ARMP scheme), which incorporates joint-angle limits, joint-velocity limits and joint-acceleration limits. To do this, Zhang et al's neural-dynamic method is employed to derive and design such an ARMP scheme. Such a scheme is then reformulated as a quadratic program (QP). To solve this QP problem online, a simplified linear-variational-inequality based primal-dual neural network (i.e., S-LVI-PDNN) is designed. With simple piecewise-linear dynamics and global exponential convergence to the optimal solution, such an S-LVI-PDNN solver can handle the strictly convex QP problem in an inverse-free manner. Finally, three given tasks, i.e., rhombic path, straight-line path and square path tracking tasks, are fulfilled by three-link, four-link and five-link planar robot arms, respectively. Computer-simulation and physical experiment results validate the physical realizability, efficacy and accuracy of the ARMP scheme and the corresponding S-LVI-PDNN solver. (C) 2012 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据