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Multi-robot cooperation and performance analysis with particle swarm optimization variants

Multi-robot cooperation and performance analysis with particle swarm optimization variants Cooperation and synchronization of multi-robots is a major concern in robotics research field. Two autonomous robots are assumed to carry a stick and called as the twin robots. Different types of Particle Swarm Optimization (PSO) are analyzed for stick carrying task and a brief review of extension and enhancement of PSO is done to identify the parameters used. Path planning of twin robot is done with variants of PSO. Performance of each variant-applied twin is evaluated based on several parameters. These parameters are execution time, number of steps, number of turns, path travelled and path deviated. Fitness value of each twin is calculated in each algorithm to obtain the next position along the solution path. All the algorithms are executed and the pixels are plotted to represent the twin’s trajectory and the performance of PSO variants compared with Artificial Bee Colony Optimization (ABCO) and differential Evolutionary (DE) algorithm. It is observed that PSO variants outperforms with respect to distance value. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Multi-robot cooperation and performance analysis with particle swarm optimization variants

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References (33)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
ISSN
1380-7501
eISSN
1573-7721
DOI
10.1007/s11042-021-10986-x
Publisher site
See Article on Publisher Site

Abstract

Cooperation and synchronization of multi-robots is a major concern in robotics research field. Two autonomous robots are assumed to carry a stick and called as the twin robots. Different types of Particle Swarm Optimization (PSO) are analyzed for stick carrying task and a brief review of extension and enhancement of PSO is done to identify the parameters used. Path planning of twin robot is done with variants of PSO. Performance of each variant-applied twin is evaluated based on several parameters. These parameters are execution time, number of steps, number of turns, path travelled and path deviated. Fitness value of each twin is calculated in each algorithm to obtain the next position along the solution path. All the algorithms are executed and the pixels are plotted to represent the twin’s trajectory and the performance of PSO variants compared with Artificial Bee Colony Optimization (ABCO) and differential Evolutionary (DE) algorithm. It is observed that PSO variants outperforms with respect to distance value.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Nov 1, 2022

Keywords: PSO variants; Twin robot; Path planning; Path deviation; Performance

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