Research on the inverse kinematics of manipulator using an improved self-adaptive mutation differential evolution algorithm

Qianqian Zhang,Lifu Gao,Daqing Wang

doi:10.1177/17298814211014413

Abstract

To assess the inverse kinematics (IK) of multiple degree-of-freedom (DOF) serial manipulators, this article proposes a method for solving the IK of manipulators using an improved self-adaptive mutation differential evolution (DE) algorithm. First, based on the self-adaptive DE algorithm, a new adaptive mutation operator and adaptive scaling factor are proposed to change the control parameters and differential strategy of the DE algorithm. Then, an error-related weight coefficient of the objective function is proposed to balance the weight of the position error and orientation error in the objective function. Finally, the proposed method is verified by the benchmark function, the 6-DOF and 7-DOF serial manipulator model. Experimental results show that the improvement of the algorithm and improved objective function can significantly improve the accuracy of the IK. For the specified points and random points in the feasible region, the proportion of accuracy meeting the specified requirements is increased by 22.5% and 28.7%, respectively.

Highlights

The inverse kinematics (IK) of a manipulator is an important part of its design and control, which has guiding significance for motion analysis and trajectory planning
D and P refer to search space dimension and population size, respectively, N means the power exponent of the adaptive mutation operator (AMO), F0 is the original scaling factor, Nmax is the maximum number of iterations, and symmetric mapping is used after the mutated individual crosses the boundary
The simulation results of the IK of the 6-DOF manipulator show that among the three differential evolution (DE) algorithms, the ISAMDE algorithm has the best performance on the IK problem

Summary

Introduction

The inverse kinematics (IK) of a manipulator is an important part of its design and control, which has guiding significance for motion analysis and trajectory planning. Traditional solution methods can be divided into two categories: analytic and numerical methods. When the manipulator does not meet Pieper criterion,[1] the analytic solution cannot be obtained. For PUMA-type manipulators, when the last three axes do not exactly intersect at a common point and the shoulder joint axes are not exactly orthogonal, there is an error in the analytic solution. In this case, the numerical method can be used to find the IK. Even in cases where an analytic solution does exist, numerical methods are often used to improve the accuracy of these solutions.[2,3]

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Discussion

Conclusion