Inverse kinematics solution for robotic manipulator based on extreme learning machine and sequential mutation genetic algorithm. (14th August 2018)

• Record Type: Journal Article
• Authors: Zhou, Zhiyu; Guo, Hanxuan; Wang, Yaming; Zhu, Zefei; Wu, Jiang; Liu, Xiangqi
• Is Part Of: International journal of advanced robotic systems. Volume 15:Number 4(2018:Jul./Aug.)
• Publication Date: 2018-08-14
• Subjects: Robotic manipulator -- inverse kinematics -- extreme learning machine -- sequential mutation genetic algorithm -- robotic control; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881418792992
• Languages: English
• ISSNs: 1729-8806

Abstract

This article presents an intelligent algorithm based on extreme learning machine and sequential mutation genetic algorithm to determine the inverse kinematics solutions of a robotic manipulator with six degrees of freedom. This algorithm is developed to minimize the computational time without compromising the accuracy of the end effector. In the proposed algorithm, the preliminary inverse kinematics solution is first computed by extreme learning machine and the solution is then optimized by an improved genetic algorithm based on sequential mutation. Extreme learning machine randomly initializes the weights of the input layer and biases of the hidden layer, which greatly improves the training speed. Unlike classical genetic algorithms, sequential mutation genetic algorithm changes the order of the genetic codes from high to low, which reduces the randomness of mutation operation and improves the local search capability. Consequently, the convergence speed at the end of evolution is improved. The performance of the extreme learning machine and sequential mutation genetic algorithm is also compared with that of a hybrid intelligent algorithm, and the results showed that there is significant reduction in the training time and computational time while the solution accuracy is retained. Based on the experimental results, the proposed extreme learning machine and sequential mutation genetic algorithm can greatly improve the time efficiency while ensuring high accuracy of the end effector.