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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: | 9 |
| Deposited By: | Mathematics |
| Deposited On: | 2020-05-28 |
| Last Modified: | 2021-06-04 |
| Downloads: | 11 View Download Statistics |
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