eCite Digital Repository

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

Citation

Lim, HS and Fan, S and Chin, CKH and Chai, S, Performance evaluation of particle swarm intelligence based optimization techniques in a novel AUV path planner, Proceedings of the 2018 IEEE OES Autonomous Underwater Vehicle Symposium, 06-09 November 2018, Porto, Portugal, pp. 1-7. ISBN 9781728102535 (2018) [Refereed Conference Paper]


Preview
PDF (2018 AUV Conference)
Available from 01 November 2020
1Mb
  

Copyright Statement

Copyright 2018 IEEE

Official URL: http://dx.doi.org/10.1109/AUV.2018.8729773

Abstract

Over years of development, many optimization techniques have been proposed for the path planning of the Autonomous Underwater Vehicle (AUV). The development in swarm intelligence optimization, particularly the particle swarm optimization (PSO), has significantly improved the performance of the AUV path planner. This study presents 12 variants of particle swarm intelligence (PSI)-based algorithms, which were applied to evaluate their performances in solving the optimal path planning problem of an AUV operating in 2D and 3D ocean environments with obstacles and non-uniform currents. Throughout the structure of the optimization problem, the practicability of the path planning algorithms were considered by taking 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 evaluate these algorithms based on their respective solution qualities, stabilities and computational efficiencies. Ultimately, the strengths and weaknesses of these algorithms were comprehensively analyzed, in order to identify the most appropriate optimization algorithm for AUV path planning in dynamic environments.

Item Details

Item Type:Refereed Conference Paper
Keywords:autonomous underwater vehicle, dynamic modeling, control and estimation, path planning, optimization, swarm intelligence, particle swarm optimization
Research Division:Engineering
Research Group:Electrical and Electronic Engineering
Research Field:Signal Processing
Objective Division:Defence
Objective Group:Defence
Objective Field:Intelligence
UTAS Author:Lim, HS (Mr Hui Lim)
UTAS Author:Fan, S (Dr Shuangshuang Fan)
UTAS Author:Chin, CKH (Dr Chris Chin)
UTAS Author:Chai, S (Professor Shuhong Chai)
ID Code:129578
Year Published:2018
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2018-12-07
Last Modified:2019-09-05
Downloads:0

Repository Staff Only: item control page