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Licensed Unlicensed Requires Authentication Published by De Gruyter May 16, 2022

Impacts of heuristic parameters in PSO inverse kinematics solvers

  • Nizar Rokbani , Raghvendra Kumar , Adel M. Alimi , Pham Huy Thong EMAIL logo , Ishaani Priyadarshini , Viet Ha Nhu ORCID logo EMAIL logo and Phuong Thao Thi Ngo


In this paper, an investigation is conducted in order to understand impacts of Particle Swarm Optimization (PSO) parameters on the convergence and the quality of the inverse kinematics solutions provided by the IK-PSO (inverse kinematics solver using PSO) – a heuristic inverse kinematics solver algorithm. Over a large panel of parameters investigations, a statistical proof of convergence is provided for 5 links to 60 links articulated system. A recommended set of parameters intervals are presented for this class of IK problems. Investigations are based on the standard inertia weight PSO, and concerned the impact of the inertia weight, the swarm size and the maximum iteration number. For a given set of parameters, the existence of a solution with a given position error is also proved. All tests were conducted over 100 times. The density of probability function, PDF, is used to approximate and analyze the fineness functions, which are the square of the position error. Results showed IK-PSO is an interesting IK solver when a set of good parameters are used. For these parameters, the algorithm showed a statistical proof of convergence with a high resolution, by mean of error position. The algorithm also showed time-effectiveness compared to CCD method, which is assumed to be a real-time IK heuristic solver used in gaming.

Corresponding authors: Pham Huy Thong, VNU Information Technology Institute, Vietnam National University, Hanoi, Vietnam, E-mail: ; and Viet Ha Nhu, Department of Geological-Geotechnical Engineering, Hanoi University of Mining and Geology, Hanoi, Vietnam, E-mail:

Funding source: ARUB program , Tunisia

Award Identifier / Grant number: General Direction of Scientific Research (DGRST), Ministry of Higher Education and Scientific Research, Tunisia.

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: The authors (Nizar Rokbani and Adel. M. Alimi) would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST) and Ministry of Higher Education and Scientific Research, Tunisia, under the ARUB program.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.


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Received: 2020-02-12
Revised: 2021-09-20
Accepted: 2022-01-18
Published Online: 2022-05-16
Published in Print: 2022-10-27

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