Torque Minimization of Kinematically Redundant Manipulators Using a Dual Neural Network

摘要：

A dual neural network is presented for real-time joint torque minimization of kinematically redundant manipulators, which corresponds to global kinetic energy minimization of robot mechanisms. Compared to other neural-network-based computation schemes for inverse kinematics, the proposed neural network with concise architecture is composed of only one single layer of neurons with the size equal to the dimensionality of the task workspace, and is proven to be globally exponentially stable, which guarantees the sufficiently small end-effector tracking error. The proposed dual neural network has been simulated on six degrees of freedom (DOF) robot arm PUMA560 with the effectiveness and efficiency demonstrated.

作者: Yunong Zhang Jun Wang

作者单位: Department of Automation and Computer-Aided Engineering The Chinese University of Hong Kong Shatin, New Territories, Hong Kong

会议类型: 国际会议

会议名称: 8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)

会议地点: 上海

会议语种:英文

页码: 1183-1188

在线出版日期: 2001-11-14（万方平台首次上网日期，不代表论文的发表时间）