Arithmetic for Multi-joint Redundant Robot Inverse Kinematics Based on the Bayesian-BP Neural Network

摘要：

Based on the combination of Bayesian methods and BP neural network, a Bayesian-BP neural network model is presented to solve multi-joint redundant robot inverse kinematics in the continuous path. After inspecting joints movement rules of multi-joint robot, the knowledge distribution of nature connection tied in Bayesian methods is used to formalize all kinds of priori information and implement the durative process of learning. With BIC criteria, using a two-stage cross optimization method to amend parameters of network weights and improves the learning speed of neural networks, convergence and accuracy. The simulation shows that Rotations or move changes of per joints are smooth in the multiple working points of the robot continuous path, and the error of the method could be less than 0.001.

作者: Zhou Youhang Tang Wenzhuang Zhang Jianxun

作者单位: School of Mechanical Engineering, Xiangtan University, Xiangtan, China 411105

会议类型: 国际会议

会议名称: International Conference on Intelligent Computation Technology and Automation(2008 智能计算技术与自动化国际会议 ICICTA 2008)

会议地点: 长沙

会议语种:英文

页码: 173-178

在线出版日期: 2008-10-20（万方平台首次上网日期，不代表论文的发表时间）