Developing a New Robust Swarm-Based Algorithm for Robot Analysis

Abubakar Umar, Zhanqun Shi, Alhadi Khlil and Zulfiqar I. B. Farouk
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Abubakar Umar: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
Zhanqun Shi: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
Alhadi Khlil: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
Zulfiqar I. B. Farouk: School of Mechanical engineering, Tianjin University, Tianjin 300350, China

Mathematics, 2020, vol. 8, issue 2, 1-30

Abstract: Metaheuristics are incapable of analyzing robot problems without being enhanced, modified, or hybridized. Enhanced metaheuristics reported in other works of literature are problem-specific and often not suitable for analyzing other robot configurations. The parameters of standard particle swarm optimization (SPSO) were shown to be incapable of resolving robot optimization problems. A novel algorithm for robot kinematic analysis with enhanced parameters is hereby presented. The algorithm is capable of analyzing all the known robot configurations. This was achieved by studying the convergence behavior of PSO under various robot configurations, with a view of determining new PSO parameters for robot analysis and a suitable adaptive technique for parameter identification. Most of the parameters tested stagnated in the vicinity of strong local minimizers. A few parameters escaped stagnation but were incapable of finding the global minimum solution, this is undesirable because accuracy is an important criterion for robot analysis and control. The algorithm was trained to identify stagnating solutions. The algorithm proposed herein was found to compete favorably with other algorithms reported in the literature. There is a great potential of further expanding the findings herein for dynamic parameter identification.

Keywords: PSO; robot; manipulator; analysis; kinematic parameters; identification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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