基于有限时间神经网络的冗余机械臂可操纵性优化

Y. Kong, Jiajia Wu, Shiyong Chen, Junwen Zhou
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引用次数: 0

摘要

针对冗余机械手的协同运动规划问题,提出了一种基于有限时间神经网络的分布式可操纵性优化方案。在该方案中,机器人末端执行器在点对点通信下能够以协作的方式完成特定任务,实现了冗余机器人的最优运动时间。将DMO格式化为二次规划,并利用拉格朗日乘子定理求解。从理论上证明了所提DMO方案的稳定性和有限性。对三个冗余机械手的仿真结果表明了该方法的有效性和准确性。方法
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Distributed Manipulability optimization in a Finite Time Neural Network for Redundant Manipulators
A distributed manipulability optimization (DMO) scheme based on a finite time neural network is proposed in this paper to solve the cooperative motion planning of redundant manipulators. In this proposed kinematic scheme, the end-effectors of the manipulators can complete the specific task in a cooperative manner under peer-to-peer communication and the optimal kinematic time of redundant manipulators has achieved. The DMO scheme is formulated into a quadratic program and is solved by Lagrange multiplier theorem. The stability and finiteness of the proposed DMO scheme have been proved in theory. Simulation results on three redundant manipulators show the validity and accuracy of this new DMO scheme. method
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