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Performance evaluation of particle swarm intelligence based optimization techniques in a novel AUV path planner

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Lim, HS, Fan, S ORCID: 0000-0002-1865-9730, Chin, CKH ORCID: 0000-0002-3070-731X and Chai, S ORCID: 0000-0001-5186-4456 2018 , 'Performance evaluation of particle swarm intelligence based optimization techniques in a novel AUV path planner', in Proceedings of the 2018 IEEE OES Autonomous Underwater Vehicle Symposium , IEEE, United States, pp. 1-7 .

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Abstract

Over years of development, many optimizationtechniques have been proposed for the path planning of theAutonomous Underwater Vehicle (AUV). The development inswarm intelligence optimization, particularly the particle swarmoptimization (PSO), has significantly improved the performanceof the AUV path planner. This study presents 12 variants ofparticle swarm intelligence (PSI)-based algorithms, which wereapplied to evaluate their performances in solving the optimal pathplanning problem of an AUV operating in 2D and 3D oceanenvironments with obstacles and non-uniform currents.Throughout the structure of the optimization problem, thepracticability of the path planning algorithms were considered bytaking into account the physical limitations of the AUV actuations.To compare the performances of these PSI-based algorithms,extensive Monte Carlo simulations were conducted to evaluatethese algorithms based on their respective solution qualities,stabilities and computational efficiencies. Ultimately, the strengthsand weaknesses of these algorithms were comprehensivelyanalyzed, in order to identify the most appropriate optimizationalgorithm for AUV path planning in dynamic environments.

Item Type: Conference Publication
Authors/Creators:Lim, HS and Fan, S and Chin, CKH and Chai, S
Keywords: autonomous underwater vehicle, dynamic modeling, control and estimation, path planning, optimization, swarm intelligence, particle swarm optimization
Journal or Publication Title: Proceedings of the 2018 IEEE OES Autonomous Underwater Vehicle Symposium
Publisher: IEEE
Copyright Information:

Copyright 2018 IEEE

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