AUV 3D path planning based on improved PSO

doi:10.23919/JSEE.2025.000074

Journal of Systems Engineering and Electronics ›› 2025, Vol. 36 ›› Issue (3): 854-866.doi: 10.23919/JSEE.2025.000074

• CONTROL THEORY AND APPLICATION • Previous Articles

AUV 3D path planning based on improved PSO

Hongen LI(), Shilong LI(), Qi WANG(), Xiaoming HUANG()

School of Chemical Engineering, Ocean and Life Sciences, Dalian University of Technology, Panjin 124221, China

Received:2024-03-22 Online:2025-06-18 Published:2025-07-10
Contact: Xiaoming HUANG E-mail:980192418@qq.com;1761704166@qq.com;1623380406@qq.com;huangxm@dlut.edu.cn
About author:
LI Hongen was born in 1999. He received his B.S. degree in vehicle engineering from the College of Automotive and Traffic Engineering, Nanjing Forestry University in 2022. He is currently pursuing his M.S. degree at the School of Chemical Engineering, Ocean and Life Sciences, Dalian University of Technology. His research interests include the lane markings recognition based on deep learning and underwater robot path planning and evaluation. E-mail: 980192418@qq.com

LI Shilong was born in 1997. He received his B.S. degree in marine engineering from the College of Maritime, Ningbo University, China, in 2020, and M.S. degree in engineering mechanics from the School of Ocean Science and Technology, Dalian University of Technology, China, in 2023. His research interests include the design of underwater vehicles, and path planning of autonomous underwater vehicles. E-mail: 1761704166@qq.com

WANG Qi was born in 1999. She received her B.S. degree in vehicle engineering from the College of Traffic and Vehicle Engineering, Shandong University of Technology in 2022. She is currently pursuing her M.S. degree at the School of Chemical Engineering, Ocean and Life Sciences, Dalian University of Technology. Her research interests include path planning and obstacle avoidance processing of intelligent vehicles, underwater robot path planning, and polar sea ice risk management. E-mail: 1623380406@qq.com

HUANG Xiaoming was born in 1987. She received her B.S. degree in electronic science and technology from the School of Physics, Dalian University of Technology, China, in 2010, and Ph.D. degree in condensed matter physics from the same school in 2015. Since 2021, she worked as an associate professor and her research interests include the design of underwater vehicles, path planning of autonomous underwater vehicles, and autonomous motion control. E-mail: huangxm@dlut.edu.cn
Supported by:
This work was supported by the High-tech Ship Projects of the Ministry of Industry and Information Technology of China (2021-342).

Abstract

Abstract:

The influence of ocean environment on navigation of autonomous underwater vehicle (AUV) cannot be ignored. In the marine environment, ocean currents, internal waves, and obstacles are usually considered in AUV path planning. In this paper, an improved particle swarm optimization (PSO) is proposed to solve three problems, traditional PSO algorithm is prone to fall into local optimization, path smoothing is always carried out after all the path planning steps, and the path fitness function is so simple that it cannot adapt to complex marine environment. The adaptive inertia weight and the “active” particle of the fish swarm algorithm are established to improve the global search and local search ability of the algorithm. The cubic spline interpolation method is combined with PSO to smooth the path in real time. The fitness function of the algorithm is optimized. Five evaluation indexes are comprehensively considered to solve the three-demensional (3D) path planning problem of AUV in the ocean currents and internal wave environment. The proposed method improves the safety of the path planning and saves energy.

Key words: autonomous underwater vehicle (AUV), three-dimensional (3D) path planning, particle swarm optimization (PSO), cubic spline interpolation

Hongen LI, Shilong LI, Qi WANG, Xiaoming HUANG. AUV 3D path planning based on improved PSO[J]. Journal of Systems Engineering and Electronics, 2025, 36(3): 854-866.

Figures/Tables 18

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Algorithm	Average time/h	Average length/km	Pass rate
PSO	15.13	33.17	0.74
QPSO	15.12	33.10	0.78
LDW-PSO	14.64	32.28	0.55
Improved PSO	14.05	31.35	0.85

Improved PSO constraint	Sailing time/ h	Average path nodes v/(m/s)	Maximum fluctuation of v/(m/s)	Standard deviation of v/(m/s)
F_ ocean currents	12.74	0.765	0.705	0.247
T_ ocean currents	13.12	0.722	0.172	0.152

Improved PSO constraint	Sailing time/h	Length/ km	Internal wave time/h	Internal wave length/km
F_Internal wave	12.44	30.87	4.47	10.79
T_Internal wave	12.80	31.52	2.03	4.90

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AUV 3D path planning based on improved PSO

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