Vol.27, No.2, 2021, pp.551-564, doi:10.32604/iasc.2021.01008
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ARTICLE
A Fuzzy-Based Bio-Inspired Neural Network Approach for Target Search by Multiple Autonomous Underwater Vehicles in Underwater Environments
  • Aolin Sun, Xiang Cao*, Xu Xiao, Liwen Xu
School of Physics and Electronic Electrical Engineering, Huaiyin Normal University, Huaian, 223300, China
* Corresponding Author: Xiang Cao. Email:
Received 14 October 2020; Accepted 06 November 2020; Issue published 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 proposed approach obtains all the required parameters that do not require learning and training. And the model parameters are not sensitive. The simulation and experiment results show that the proposed algorithm can quickly and security search targets in the complex obstacle environments. Compared with the PSO (Particle Swarm Optimization) algorithm, the simulation results show that the proposed algorithm can control a multi-AUV to complete multi-target search tasks with higher search efficiency and adaptability. At the same time, the fuzzy obstacle-avoidance improves the search trajectory smoothness.
Keywords
Target search; multi-AUV; fuzzy approach; bio-inspired neural network
Cite This Article
A. Sun, X. Cao, X. Xiao and L. Xu, "A fuzzy-based bio-inspired neural network approach for target search by multiple autonomous underwater vehicles in underwater environments," Intelligent Automation & Soft Computing, vol. 27, no.2, pp. 551–564, 2021.
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