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An improved inverse kinematics solution for 6-DOF robot manipulators with offset wrists

Published online by Cambridge University Press:  14 January 2022

Xing Zhou
Affiliation:
Foshan Institute of Intelligent Equipment Technology, Foshan528000, China School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan430000, China
Yaoqi Xian*
Affiliation:
Foshan Institute of Intelligent Equipment Technology, Foshan528000, China
Yuanhao Chen
Affiliation:
Foshan Institute of Intelligent Equipment Technology, Foshan528000, China
Tongshu Chen
Affiliation:
Foshan Institute of Intelligent Equipment Technology, Foshan528000, China
Lin Yang
Affiliation:
Huashu Robot Co., Ltd., Foshan528000, China
Simin Chen
Affiliation:
Foshan Institute of Intelligent Equipment Technology, Foshan528000, China
Jian Huang
Affiliation:
Foshan Institute of Intelligent Equipment Technology, Foshan528000, China
*
*Corresponding author. E-mail: xianyaoqi@gmail.com

Abstract

Efficiently solving inverse kinematics (IK) of robot manipulators with offset wrists remains a challenge in robotics due to noncompliance with Pieper criteria. In this paper, an improved method to solve the IK for 6-DOF robot manipulators with offset wrists is proposed. This method is based on the Newton iteration technique, but it does not require a selection of initial estimation of joint variables. The solution is divided into two parts: the first part is to reconstruct a simplified structure with analytical IK solution, and the second part is to obtain a numerical solution by iteration. Further, a robot manipulator HSR-BR606 with an offset wrist is used as an example to specifically elaborate the mathematical procedure of the method and to investigate the algorithm in terms of accuracy, efficiency, and application of motion planning. A comparative experiment is conducted with a typical IK algorithm, which demonstrates a higher accuracy and shorter calculation time of the proposed method. The mean calculation time for a single IK solution required for this algorithm is only 4% of the comparison algorithm.

Type
Research Article
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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