Multi-autonomous underwater vehicle formation control and cluster search using a fusion control strategy at complex underwater environment. (15th November 2020)

• Record Type: Journal Article
• Authors: Chen, Yan-Li; Ma, Xi-Wen; Bai, Gui-Qiang; Sha, Yongbai; Liu, Jun
• Is Part Of: Ocean engineering. Volume 216(2020)
• Publication Date: 2020-11-15
• Subjects: Multi-AUVs -- Format control and cluster search -- Redistribution mechanism -- Path planning and task assignment -- Obstacle avoidance; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2020.108048
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: We introduce a multiple autonomous underwater vehicle (multi-AUV) control strategy to improve the cooperative operation ability in formation control and cluster search operations and to minimize the likelihood of collisions and interference with teammates and obstacles under various complex working conditions. Our approach uses a fusion control strategy with a redistribution mechanism (RM) based on virtual structure and leader–follower formation characteristics to improve the rapidity, stability, and accuracy of the multi-AUVs. The RM improved the rapidity and uniqueness of the random distribution formation by addressing over- and under-allocation. The combination of two (26-point and 8-point) path updating methods based on a hybrid bio-inspired self-organizing map algorithm considerably enhanced the performance of the multi-AUV system. Simulation tests and performance analyses indicated that the 8-point path updating method performed better in terms of rapidity; however, the 26-point path updating method was superior in integrity. The hunting performance of the fusion-controlled multi-AUV formation, was then analyzed to verify the feasibility of real-time formations under dynamic and static mixing of multi-obstacles by considering shape irregularities and the random distribution of multi-obstacles in three-dimensional underwater environments. Highlights: A tabu search mechanism was used to simulate multi-AUV deep-sea exploration and capture tasks. Proposed the hybrid bio-inspired self-organizing map (HBSOM) algorithm for hunting intelligenttargets. The RM improved the rapidity and uniqueness of the random distribution formation by addressingover- and under-allocation. Proposed a fusion formation control strategy improve the rapidity, stability, and accuracy of the multi-AUVs formation.