A parallel learning particle swarm optimizer for inverse kinematics of robotic manipulator. Issue 10 (18th June 2021)

• Record Type: Journal Article
• Authors: Liu, Fei; Huang, Hailin; Li, Bing; Xi, Fengfeng
• Is Part Of: International journal of intelligent systems. Volume 36:Issue 10(2021)
• Page Start: 6101
• Page End: 6132
• Publication Date: 2021-06-18
• Subjects: CEC2013 -- inverse kinematics -- manipulator -- parallel learning -- particle swarm optimization; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22543
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: In this study, a novel parallel learning particle swarm optimizer (PLPSO) is proposed. The evolutionary strategy of the algorithm is quite different from that of the existing PSO algorithms. To enhance the global search capability of the particle swarm, the original particle swarm is divided into two parallel evolving independent particle subpopulations to explore the search space simultaneously. In addition, according to the evolutionary factor in each iteration, the poorly performing particles in the two subpopulations are spurred and learned to improve the search speed of the particle swarm. In the process of particle learning, delay information is added to the velocity update item to reduce the occurrence of local capture. Twenty‐eight benchmark functions of CEC2013 are used to evaluate the performance of the proposed PLPSO algorithm. Numerous comparisons demonstrate that the performance of the PLPSO algorithm is better than that of other nine PSO algorithms. Afterwards, the PLPSO algorithm is applied to the inverse kinematics (IK) solution of a robotic manipulator. By comparing with other three intelligent algorithms, the PLPSO algorithm shows an excellent performance in solving the IK problem. Finally, tests are carried out on an UR5 manipulator to confirm the practicability of the proposed PLPSO algorithm further. Thus, the feasibility of using the PLPSO algorithm for solving the IK problem of a robotic manipulator is verified.