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Constrained path planning of autonomous underwater vehicle using selectively-hybridized particle swarm optimization algorithms

Citation

Lim, HS and Fan, S and Chin, CKH and Chai, S and Bose, N and Kim, E, Constrained path planning of autonomous underwater vehicle using selectively-hybridized particle swarm optimization algorithms, IFAC-PapersOnLine, 52 (21): Proceedings of the 12th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles (CAMS 2019), 18-20 September 2019, Daejeon, South Korea, pp. 315-322. ISSN 2405-8963 (2019) [Refereed Conference Paper]

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Copyright Statement

Copyright 2019 IFAC (International Federation of Automatic Control)

DOI: doi:10.1016/j.ifacol.2019.12.326

Abstract

This paper presents an autonomous underwater vehicle (AUV) path planning scenario as an optimization problem constrained by the combination of hard constraints and soft constraints. The path planner aims to generate the optimum path that safely guides an AUV through an ocean environment with priori known obstacles and non-uniform currents in both 2D and 3D. The path planner uses 2 variants of particle swarm optimization (PSO) algorithms, which are the selectively Differential Evolution (DE)-hybridized Quantum PSO (SDEQPSO) and Adaptive PSO (SDEAPSO). The performances of the path planners using different constraints are analyzed in a series of extensive Monte Carlo simulations and ANOVA (analysis of variance) procedures based on their respective solution qualities, stabilities and computational efficiencies. Based on the simulation results, the SDEQPSO path planner with the setting of hard constraint for boundary condition and soft constraint for obstacle avoidance was found to be able to generate smooth and feasible AUV path with higher efficiency than other algorithms, as indicated by its relatively low computational requirement and excellent solution quality.

Item Details

Item Type:Refereed Conference Paper
Keywords:path planning, optimization problems, constraints, Monte Carlo simulation, autonomous vehicle
Research Division:Engineering
Research Group:Maritime engineering
Research Field:Special vehicles
Objective Division:Defence
Objective Group:Defence
Objective Field:Intelligence, surveillance and space
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)
UTAS Author:Bose, N (Professor Neil Bose)
UTAS Author:Kim, E (Miss Eonjoo Kim)
ID Code:139153
Year Published:2019
Web of Science® Times Cited:6
Deposited By:Mathematics
Deposited On:2020-05-28
Last Modified:2021-06-04
Downloads:5 View Download Statistics

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