KL Vasudev¹, Manish Pandey², Jaan H. Pu^3,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.7, pp. 1545-1569, 2025, DOI:10.32604/fdmp.2025.065289 - 31 July 2025

Abstract This study presents a comparative analysis of optimisation strategies for designing hull shapes of Autonomous Underwater Vehicles (AUVs), paying special attention to drag, lift-to-drag ratio, and delivered power. A fully integrated optimisation framework is developed accordingly, combining a single-objective Genetic Algorithm (GA) for design parameter generation, Computer-Aided Geometric Design (CAGD) for the creation of hull geometries and associated fluid domains, and a Reynolds-Averaged Navier–Stokes (RANS) solver for evaluating hydrodynamic performance metrics. This unified approach eliminates manual intervention, enabling automated determination of optimal hull configurations. Three distinct optimisation problems are addressed using the proposed methodology. First,… More >

Yu-Hsien Lin^*, Po-Cheng Chuang, Joyce Yi-Tzu Huang

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4907-4948, 2025, DOI:10.32604/cmc.2025.065995 - 30 July 2025

Abstract This study proposes an automatic control system for Autonomous Underwater Vehicle (AUV) docking, utilizing a digital twin (DT) environment based on the HoloOcean platform, which integrates six-degree-of-freedom (6-DOF) motion equations and hydrodynamic coefficients to create a realistic simulation. Although conventional model-based and visual servoing approaches often struggle in dynamic underwater environments due to limited adaptability and extensive parameter tuning requirements, deep reinforcement learning (DRL) offers a promising alternative. In the positioning stage, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is employed for synchronized depth and heading control, which offers stable training, reduced overestimation… More >

Guojun Chen^*, Danguo Cheng, Wei Chen, Xue Yang, Tiezheng Guo

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3721-3741, 2024, DOI:10.32604/cmc.2024.047325 - 26 March 2024

Abstract With the increase in ocean exploration activities and underwater development, the autonomous underwater vehicle (AUV) has been widely used as a type of underwater automation equipment in the detection of underwater environments. However, nowadays AUVs generally have drawbacks such as weak endurance, low intelligence, and poor detection ability. The research and implementation of path-planning methods are the premise of AUVs to achieve actual tasks. To improve the underwater operation ability of the AUV, this paper studies the typical problems of path-planning for the ant colony algorithm and the artificial potential field algorithm. In response to… More >

Haolin Wang¹, Lihui Dong¹, Wei Song^1,2,3,*, Xiaobin Zhao^1,3, Jianxin Xia⁴, Tongmu Liu⁵

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1573-1586, 2022, DOI:10.32604/iasc.2022.023994 - 09 December 2021

Abstract Autonomous underwater vehicle (AUV) has many intelligent optical system, which can collect underwater signal information to make the system decision. One of them is the intelligent vision system, and it can capture the images to analyze. The performance of the particle image segmentation plays an important role in the monitoring of underwater mineral resources. In order to improve the underwater mineral image segmentation performance, some novel segmentation algorithm architectures are proposed. In this paper, an improved mineral image segmentation is proposed based on the modified U-Net. The pyramid upsampling module and residual module are bring More >

Aolin Sun, Xiang Cao^*, Xu Xiao, Liwen Xu

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 551-564, 2021, DOI:10.32604/iasc.2021.01008 - 18 January 2021

Abstract An essential issue in a target search is safe navigation while quickly finding targets. In order to improve the efficiency of a target search and the smoothness of AUV’s (Autonomous Underwater Vehicle) trajectory, a fuzzy-based bio-inspired neural network approach is proposed in this paper. A bio-inspired neural network is applied to a multi-AUV target search, which can effectively plan search paths. In the meantime, a fuzzy algorithm is introduced into the bio-inspired neural network to make the trajectory of AUV obstacle avoidance smoother. Unlike other algorithms that need repeated training in the parameters selection, the More >

Xiang Cao^1,2,*, Ling Chen¹, Liqiang Guo³, Wei Han⁴

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 391-407, 2021, DOI:10.32604/iasc.2021.01002 - 18 January 2021

Abstract As one of the classical problems in autonomous underwater vehicle (AUV) research, path planning has obtained a lot of research results. Many studies have focused on planning an optimal path for AUVs. These optimal paths are sometimes too close to obstacles. In the real environment, it is difficult for AUVs to avoid obstacles according to such an optimal path. To solve the safety problem of AUV path planning in a dynamic uncertain environment, an algorithm combining a bio-inspired neural network and potential field is proposed. Based on the environmental information, the bio-inspired neural network plans More >

Hui Sheng Lim^1,*, Christopher K. H. Chin¹, Shuhong Chai¹, Neil Bose^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 33-50, 2020, DOI:10.32604/cmes.2020.011648 - 18 September 2020

Abstract This paper presents an online AUV (autonomous underwater vehicle) path planner that employs path replanning approach and the SDEQPSO (selective differential evolution-hybridized quantum-behaved particle swarm optimization) algorithm to optimize an AUV mission conducted in an unknown, dynamic and cluttered ocean environment. The proposed path replanner considered the effect of ocean currents in path optimization to generate a Pareto-optimal path that guides the AUV to its target within minimum time. The optimization was based on the onboard sensor data measured from the environment, which consists of a priori unknown dynamic obstacles and spatiotemporal currents. Different sensor… More >

Xiang Cao^1,2,3, Haichun Yu^1,3, Hongbing Sun^1,3

Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 25-34, 2019, DOI:10.31209/2018.100000038

Abstract For cooperative hunting by a multi-AUV (multiple autonomous underwater vehicles) team, not only basic problems such as path planning and collision avoidance should be considered but also task assignments in a dynamic way. In this paper, an integrated algorithm is proposed by combining the self-organizing map (SOM) neural network and the Glasius Bio-Inspired Neural Network (GBNN) approach to improve the efficiency of multi-AUV cooperative hunting. With this integrated algorithm, the SOM neural network is adopted for dynamic allocation, while the GBNN is employed for path planning. It deals with various situations for single/multiple target(s) hunting More >

Qi Wang^1,2,*, Changsong Yang^1,2, Shaoen Wu³, Yuxiang Wang^1,2

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 105-118, 2019, DOI:10.32604/cmc.2019.03739

Abstract Transfer alignment is used to initialize SINS (Strapdown Inertial Navigation System) in motion. Lever-arm effect compensation is studied existing in an AUV (Autonomous Underwater Vehicle) before launched from the mother ship. The AUV is equipped with SINS, Doppler Velocity Log, depth sensor and other navigation sensors. The lever arm will cause large error on the transfer alignment between master inertial navigation system and slave inertial navigation system, especially in big ship situations. This paper presents a novel method that can effectively estimate and compensate the flexural lever arm between the main inertial navigation system mounted… More >