Impacts of heuristic parameters in PSO inverse kinematics solvers-Reference-Cited by-同舟云学术

Impacts of heuristic parameters in PSO inverse kinematics solvers

Published:2022-05-16 Issue:6 Volume:23 Page:833-858
ISSN:1565-1339
Container-title:International Journal of Nonlinear Sciences and Numerical Simulation
language:en
Short-container-title:

Author:

Rokbani Nizar¹²,Kumar Raghvendra³,Alimi Adel M.²⁴,Thong Pham Huy⁵,Priyadarshini Ishaani⁶,Nhu Viet Ha⁷^ORCID,Thi Ngo Phuong Thao⁸

Affiliation:

1. Higher Institute of Applied Sciences and Technology of Sousse, University of Sousse , Sousse , Tunisia

2. REsearch Groups in Intelligent Machines (REGIM Lab) , University of Sfax, National Engineering School of Sfax (ENIS) , BP 1173 , Sfax , 3038 , Tunisia

3. Department of Computer Science and Engineering , GIET University , Gunupur , India

4. Department of Electrical and Electronic Engineering Science, Faculty of Engineering and the Built Environment , University of Johannesburg , Johannesburg , South Africa

5. VNU Information Technology Institute, Vietnam National University , Hanoi , Vietnam

6. School of Information , University of California , Berkeley , USA

7. Department of Geological-Geotechnical Engineering , Hanoi University of Mining and Geology , Hanoi , Vietnam

8. Faculty of Information Technology , Hanoi University of Mining and Geology , Duc Thang, Bac Tu Liem , Hanoi , Vietnam

Abstract

Abstract 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.

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,General Physics and Astronomy,Mechanics of Materials,Engineering (miscellaneous),Modeling and Simulation,Computational Mechanics,Statistical and Nonlinear Physics

Link

https://www.degruyter.com/document/doi/10.1515/ijnsns-2020-0031/pdf

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