Multi-AUV cooperative target search and tracking in unknown underwater environment Xiang Cao a, b, * , Hongbing Sun a , Gene Eu Jan c a School of Physics and Electronic Electrical Engineering, Huaiyin Normal University, Huaian 223300, China b School of Automation, Southeast University, Nanjin 210096, China c Graduate Institute of Animation and Film Art, Tainan National University of the Arts and Dept. of Electrical Engineering, National Taipei University, China ARTICLE INFO Keywords: Multiple autonomous underwater vehicles (multi-AUV) Cooperative target search Tracking control Glasius bio-inspired neural network (GBNN) Bio-inspired cascaded ABSTRACT For target search and tracking in unknown underwater environment, an integrated algorithm for a cooperative team of multiple autonomous underwater vehicles (Multi-AUV) is proposed by combining the Glasius bio-inspired neural network (GBNN) and bio-inspired cascaded tracking control approach to improve search efficiency and reduce tracking errors. Among them, the GBNN is mainly used to control a multi-AUV team in search of each targets. Once any target is found, the bio-inspired cascaded tracking control approach is applied to track it in case that it may escape. This integrated algorithm deals with various situations such as search for static or dynamic targets, and tracking of different trajectory in underwater environments with obstacles. The simulation results show that this integrated algorithm is of high efficiency and adaptability. 1. Introduction Autonomous underwater vehicle (AUV) is an important tool for ma- rine resource exploitation and marine scientific research (Paull et al., 2014; Chu and Zhang, 2014; Kulkarni and Pompili, 2010). Due to the limited energy, communication range/bandwidth, and sensing range of the AUVs, many applications has outgrown single AUV's capability. Therefore, multi-AUV systems with high parallelism, robustness and collaboration have gradually become a new research field (Millan et al., 2014; Cai, 2013; Yang and Li, 2011). The fundamental research of a multi-AUV system is how to search and track target in unknown under- water environments. (Cai, 2013; Yang and Li, 2011; Masehian and Nejad, 2010). As more and more research interests turn to the cooperative target search, some significant achievements have been obtained in this field. For example, Yoon and Qiao (2011) proposed a synchronization-based survey algorithm to conduct a multi-AUV team with limited energy and communication capabilities. This approach enables the work team to search large areas even with mechanical failures of some team members. In order to improve the search efficiency, Couillard, et al. (Couillard et al., 2012). developed a local sequential path planning algorithm and a more global simulated annealing algorithm allowing a multi-AUV team to search for more than one targets while minimizing the total distance covered (Li and Landa-Silva, 2011). For target search tasks, particle swarm optimization (PSO) algorithm is also demonstrated to be effective. Cao et al. (Cao et al., 2015). proposed an improved PSO algorithm for path planning and search tasks. This algorithm is expected to show fast convergence and global search characters. In addition, Furukawa, et al. (Lanillos et al., 2014). presented a coordinated control technique that allows heterogeneous vehicles to autonomously search for and track multiple targets using recursive Bayesian filtering. A unified sensor model and a unified objective function are proposed to enable search-and-tracking within the recursive Bayesian filter framework. Cai et al. (2011). proposed a hierarchical reinforcement learning based approach to fulfil target search tasks in complex environments. Though these algorithms with some cooperation rules can handle the target search tasks, the learning process is essential before the work. Cao et al. (Cao et al., 2016). presented a decentralized search algorithm based on bio-inspired neurodynamics model in 3-D underwater environments with obstacles. This neuron network algorithm not only enables the multi-AUV team to fulfill its search task when there isn't any mechanical breakdown with its team members, but also ensures a successful search if one or several AUVs fail. The multi-AUV system, however, has a problem of optimization. That is, it is expected to accomplish the task in an optimal or nearly optimal way. Although some algorithms have been developed for optimization with current researches, there are some common problems with them as follows. * Corresponding author. School of Physics and Electronic Electrical Engineering, Huaiyin Normal University, Huaian 223300, China. E-mail addresses: cxeffort@126.com (X. Cao), gejan@mail.ntpu.edu.tw (G.E. Jan). Contents lists available at ScienceDirect Ocean Engineering journal homepage: www.elsevier.com/locate/oceaneng https://doi.org/10.1016/j.oceaneng.2017.12.037 Received 12 March 2017; Received in revised form 13 December 2017; Accepted 15 December 2017 0029-8018/© 2017 Elsevier Ltd. All rights reserved. Ocean Engineering 150 (2018) 1–11