|Table of Contents|

Solving robot inverse kinematics based on differential evolution and particle swarm optimization BP neural network

《南京理工大学学报》（自然科学版）[ISSN:1005-9830/CN:32-1397/N]

Issue:
2014年06期
Page:
763-
Research Field:
Publishing date:

Info

Title:
Solving robot inverse kinematics based on differential evolution and particle swarm optimization BP neural network
Author(s):
Chen Gui1; Chen Yaozhong2; Lin Jian1; Wen Xiulan1
1.School of Automation,Nanjing Institute of Technology,Nanjing 211167,China; 2.China North Industries Group Corporation,Nanjing 211153,China
Keywords:
differential evolution;  particle swarm optimization;  back propagation neural network;  robots;  inverse kinematics;  convergence speed;  weights;  thresholds;  joint angle error;  position error
PACS:
TP242
DOI:
-
Abstract:
Aiming at the problem of slow convergence speed of traditional back propagation(BP)neural network algorithms,differential evolution(DE)and particle swarm optimization(PSO)are combined to optimize BP neural network for robot inverse kinematics.An objective function of the optimization algorithm is formulated based on the mapping of robot forward kinematics.DE operation is employed to optimize particle evolution direction in PSO,and the weights and thresholds of the BP neural network are optimized.A simulation experiment is proposed for a KUKA robot,and the result shows that:the solution accuracy of robot inverse kinematics of the algorithm proposed here is high,and the joint angle error is below 0.1°; the position error between the initial pose matrix of the robot and that solved by the algorithm proposed here is of the order of magnitude of 0.1 mm,and has good generalization ability.The algorithm proposed here satisfies the accuracy requirements of robot locations and postures.

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Last Update: 2014-12-31