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 >

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 >

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 >