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

[1]Gustavi, T., and Hu, X. (2008). Observer-BasedLeader-Following FormationControl Using Onboard Sensor Information. IEEE Transaction on RoboticsandAutomation, 24(6), 292–297.
[2]Tanner,H.G.,andPiovesan,J.L.(2010).RandomizedRecedingHorizonNavigation. IEEE TransactiononAutomatiCControl,55(11),2640–2644.
[3]Peng, Z., Wen, G., Rahmani, A., and Yu, Y. (2013). Leader –follower formationcontrol of nonholonomic mobile robots based on a bioinspired neurodynamicbasedapproach.RoboticsandAutonomousSystems,61(9), 988–996.[4]Peng, Z., Wen, G., and Rahmani, A. (2013). Leader-Follower Formation ControlofMultiple nonholonomic RobotsBasedonBackstepping,211–216.
[5]Xing Chu, ZhaoxiaPeng, Guoguang W en and Ahmad Rahmani (2018). Distributedfixed-time formation tracking of multi-robot systems with nonholonomicconstraints, Neurocomputing Volume 313, 167-174
[6]Fang W., Wang, T., Li, Q., & Xin, J. (2020). An iterative optimization approachfor multi-robot pattern formation in obstacle environment. Robotics andAutonomous Systems, 133, 103645. doi:10.1016/j.robot.2020.103645
[7]Raditya I P., B AlldinoAS., T Wahyu W.(2018). Implementasi Algoritma PSOPada Multi Mobile Robot Dalam Penentuan Posisi Target Terdekat. IndonesianJournal of Electronics and Instrumentation Systems (IJEIS) Vol.8, No.1, April2018, pp. 13~24 ISSN (print): 2088-3714, ISSN (online): 2460-7681
[8]El-Desouky, and Hagras, H. (2009). An Adaptive Type-2 Fuzzy based Agent forMultiOccupantAmbientIntelligentEnvironment.InProceedingof5thInternationalConferenceonIntelligentEnvironment.
[9]Nurmaini, S., and Primanita, A. (2012). Modeling of Mobile Robot System withControl Strategy Based on Type-2FuzzyLogic. International Journal ofInformationandComunicationTehnologyResearch,2(3), 235–242.
[10] Lei, B., and Li, W. (2007). A Fuzzy Behaviours Fusion Algorithm for MobileRobotReal-timePathPlanninginUnknownEnvironment.IEEEInternationalConferenceOnIntegrationTechnology,173–178.[11]Hagras, H., Callaghan, V., and Colley, M. (2004). Learning and adaptation of anintelligentmobilerobotnavigatoroperatinginunstructuredenvironmentbased on a novel onlineFuzzy–Genetic system.FuzzySetsandSystems,141(1),107-160.
[12]Chen, Y.Q.C.Y.Q., and Wang, Z.W.Z. (2005). Formation control: a review and anewconsideration.IEEE/RSJInt.Conf.Intell.Robot.Syst.,no.435,pp.3664–3669.
[13]Gazi, V., Marques, L., and Ordonez, R. “Robot Swarms: Dynamics and Control,”pp.1–32,2014.
[14]Mohan, Y., and Ponnambalam, S. G. (2009). An Extensive Review of Research inSwarm Robotics.IEEE,140–145.[15]Barnes, L., Fields, M., & Valavanis, K. (2007). Unmanned Ground Vehicle SwarmFormation Control Using Potential Fields. Medierranean Conference Control andAutomation.
[16]Benbouabdallah, K., and Qi, Z. (2013). A Fuzzy Logic Behavior ArchitectureController for a Mobile Robot Path Planning in Multi-obstacles Environment.Research Journal of Applied Sciences, Engineering and Technology, 5(14),3835–3842.
[17]Kowdiki,K.H.,Barai,R.K.,andBhattacharya,S.(2012).Leader-FollowerFormationControlUsingArtificialPotentialFunctions :AKinematicApproach.IEEEInternationalConferenceOnAdvancesinEngineering,Science andManagement,500 –505.
[18]Pimenta, L. C. A., Pereira, G. A. S., Michael, N., Mesquita, R. C., Bosque, M. M.,Chaimowicz,L.,andKumar,V.(2013).SwarmCoordinationBasedonSmoothedParticleHydrodynamicsTechnique,29(2),383–399.
[19]Rigatos, G. G. (2008). Distributed gradient and particle swarm optimization formulti-robot motionplanning.Robotica,26,357–370
[20]Pugh,J.,andMartinoli,A.(2007).Inspiringandmodellingmulti-robotsearchwith particle swarm optimization. In Swarm Intelligence Symposium, 2007.SIS2007.IEEE(pp. 332-339).IEEE.
[21]Abdessemed, F., Benmahammed, K., and Monacelli, E. (2004). A fuzzy-basedreactive controller for a non-holonomic mobile robot. Robotics andautonomous Systems, 47(1), 31-46.
[22]Rao, A. M., Ramji, K., Rao, B. S. S., Vasu, V., and Puneeth, C. (2017).Navigation of non-holonomic mobile robot using neuro-fuzzy logic withintegrated safe boundary algorithm. International Journal of Automation andComputing, 14(3), 285-294
[23]Xing Chu, Zhaoxia Peng, Guoguang W en and Ahmad Rahmani (2018). Distributedfixed-time formation tracking of multi-robot systems with nonholonomicconstraints, Neurocomputing Volume 313, 167-174
[24]Pugh, J., and Martinoli, A. (2007). Inspiring and modelling multi-robot searchwith particle swarm optimization. In Swarm Intelligence Symposium, 2007. SIS2007. IEEE (pp. 332-339). IEEE
[25]Castillo, O, et al. (2016). A comparative study of type-1 fuzzy logic systems,interval type-2 fuzzy logic systems and generalized type-2 fuzzy logicsystems in control problems." Information Sciences 354: 257-274

Abstract viewed - 871 times
PDF downloaded - 623 times

Affiliations

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

Siti Nurmaini
Affiliation not stated

Hadipurnawan Satria
Affiliation not stated

Content Top

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.