Comparative Analysis Multi-Robot Formation Control Modeling Using  Fuzzy Logic Type 2 – Particle Swarm Optimization

Anggun Islami; Siti Nurmaini; Hadipurnawan Satria

doi:10.18495/comengapp.v11i3.413

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Affiliations

Anggun Islami
Department of Computer Engineering,Faculty of Computer Science, Universitas Sriwijaya

Siti Nurmaini
Affiliation not stated

Hadipurnawan Satria
Affiliation not stated

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Comparative Analysis Multi-Robot Formation Control Modeling Using Fuzzy Logic Type 2 – Particle Swarm Optimization

Anggun Islami ,
Siti Nurmaini ,
Hadipurnawan Satria

Vol 11 No 3 (2022)

DOI 10.18495/comengapp.v11i3.413

Submitted: May 14, 2022

Published: Oct 1, 2022

Abstract

Multi-robot is a robotic system consisting of several robots that are interconnected and can communicate and collaborate with each other to complete a goal. With physical similarities, they have two controlled wheels and one free wheel that moves at the same speed. In this Problem, there is a main problem remaining in controlling the movement of the multi robot formation in searching the target. It occurs because the robots have to create dynamic geometric shapes towards the target. In its movement, it requires a control system in order to move the position as desired. For multi-robot movement formations, they have their own predetermined trajectories which are relatively constant in varying speeds and accelerations even in sudden stops. Based on these weaknesses, the robots must be able to avoid obstacles and reach the target. This research used Fuzzy Logic type 2 – Particle Swarm Optimization algorithm which was compared with Fuzzy Logic type 2 – Modified Particle Swarm Optimization and Fuzzy Logic type 2 – Dynamic Particle Swarm Optimization. Based on the experiments that had been carried out in each environment, it was found that Fuzzy Logic type 2 - Modified Particle Swarm Optimization had better iteration, time and resource and also smoother robot movement than Fuzzy Logic type 2 – Particle Swarm Optimization and Fuzzy Logic Type 2 - Dynamic Particle Swarm Optimization.