• 中国科技核心期刊
  • JST收录期刊
Volume 31 Issue 1
Feb  2023
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Article Contents
YAN Jing, CHEN Tian-ming, GUAN Xin-ping, YANG Xian, LUO Xiao-yuan. Autonomous Undersea Vehicle Cooperative Control: Current Research Status and Development Trends[J]. Journal of Unmanned Undersea Systems, 2023, 31(1): 108-120. doi: 10.11993/j.issn.2096-3920.2022-0096
Citation: YAN Jing, CHEN Tian-ming, GUAN Xin-ping, YANG Xian, LUO Xiao-yuan. Autonomous Undersea Vehicle Cooperative Control: Current Research Status and Development Trends[J]. Journal of Unmanned Undersea Systems, 2023, 31(1): 108-120. doi: 10.11993/j.issn.2096-3920.2022-0096

Autonomous Undersea Vehicle Cooperative Control: Current Research Status and Development Trends

doi: 10.11993/j.issn.2096-3920.2022-0096
  • Received Date: 2022-12-20
  • Accepted Date: 2023-01-10
  • Rev Recd Date: 2023-01-10
  • Available Online: 2023-01-19
  • In recent decades, as a cross-research field of ocean exploitation and multi-robotic system, the cooperative control of autonomous undersea vehicles(AUVs) has received increasing attention from researchers and engineers. A theoretical framework for cooperative control of AUVs is under development, and relevant research is facing many problems that must be solved urgently. This paper presents a comprehensive survey of formation control, cooperative navigation and localization, cooperative path planning, task assignment, and target capture for multi-AUV cooperative control problems. We also analyzed the network architecture and cooperative strategy for formation control, as well as the constraints they face. Finally, relevant directions for future research are discussed to provide valuable insights into the rational use of AUVs for various underwater tasks in complex marine application scenarios.

     

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  • [1]
    魏博文, 吕文红, 范晓静, 等. AUV导航技术发展现状与展望[J]. 水下无人系统学报, 2019, 27(1): 1-9.

    Wei Bo-wen, Lü Wen-hong, Fan Xiao-jing, et al. Present Situation and Prospect of Autonomous Undersea Vehicle Navigation Technologies[J]. Journal of Unmanned Undersea Systems, 2019, 27(1): 1-9.
    [2]
    郭银景, 鲍建康, 刘琦, 等. AUV实时避障算法研究进展[J]. 水下无人系统学报, 2020, 28(4): 351-358. doi: 10.11993/j.issn.2096-3920.2020.04.001

    Guo Yin-jing, Bao Jian-kang, Liu Qi, et al. Research Progress of Real-Time Obstacle Avoidance Algorithms for Unmanned Undersea Vehicle: A Review[J]. Journal of Unmanned Undersea Systems, 2020, 28(4): 351-358. doi: 10.11993/j.issn.2096-3920.2020.04.001
    [3]
    闫敬, 李文飚, 杨晛, 等. 融合Q学习与PID控制器的AUV跟踪控制[J]. 水下无人系统学报, 2021, 29(5): 565-574.

    Yan Jing, Li Wen-biao, Yang Xian, et al. Tracking Control for AUV by Combining Q Learning and a PID Controller[J]. Journal of Unmanned Undersea Systems, 2021, 29(5): 565-574.
    [4]
    Davis D. Data Compression and Sampling Methodology for Ocean Observing Systems[C]//Proc. Oceans Conf. Rec. Providence, USA: IEEE, 2000.
    [5]
    Thomas C, James B, Josko C, et al. Autonomous Oceanographic Sampling Networks[J]. Oceanography, 1993, 6(3): 86-94. doi: 10.5670/oceanog.1993.03
    [6]
    Singh H, Catipovic J, Eastwood R, et al. Integrated Approach to Multiple AUV Communications, Navigation and Docking[C]//In Proc. Oceans Conf. Rec., Fort Lauderdale, FL, USA: IEEE, 1996: 59-64.
    [7]
    何玉庆, 秦天一, 王楠. 跨域协同: 无人系统技术发展和应用新趋势[J]. 无人系统技术, 2021, 4(4): 1-13.

    He Yu-qing, Qin Tian-yi, Wang Nan. Cross-domain Collaboration: New Trends in the Development and Application of Unmanned Systems Technology[J]. Unmanned Systems Technology, 2021, 4(4): 1-13.
    [8]
    国家自然科学基金委员会. 国家自然科学基金委员会“十四五”优先发展领域[EB/OL]. [2022-12-14]. https://www.nsfc.gov.cn/publish/portal0/tab1392/info87786.htm.
    [9]
    Yan Z, Jouandeau N, Cherif A A. A Survey and Analysis of Multi-Robot Coordination[J]. International Journal of Advanced Robotic Systems, 2013, 10(12): 345-363.
    [10]
    Yamashita A, Arai T, Ota J, et al. Motion Planning of Multiple Mobile Robots for Cooperative Manipulation and Transportation[J]. IEEE Transactions on Robotics & Automation, 2003, 19(2): 223-237.
    [11]
    Burlutskiy N, Touahmi Y, Lee B. Power Efficient Formation Configuration for Centralized Leader-follower AUVs Control[J]. Journal of Marine Science and Technology, 2012, 17(3): 315-329. doi: 10.1007/s00773-012-0167-0
    [12]
    Wei R, Sorensen N. Distributed Coordination Architecture for Multi-robot Formation Control[J]. Robotics & Autonomous Systems, 2008, 56(4): 324-333.
    [13]
    Ge X, Han Q L. Distributed Formation Control of Networked Multi-Agent Systems Using a Dynamic Event-Triggered Communication Mechanism[J]. IEEE Transactions on Industrial Electronics, 2017, 64(10): 8118-8127. doi: 10.1109/TIE.2017.2701778
    [14]
    Yan Z, Yang Z, Yue L, et al. Discrete-time Coordinated Control of Leader-following Multiple AUVs under Switching Topologies and Communication Delays[J]. Ocean Engineering, 2019, 172: 361-372. doi: 10.1016/j.oceaneng.2018.12.018
    [15]
    Kwon J W, Chwa D. Hierarchical Formation Control Based on a Vector Field Method for Wheeled Mobile Robots[J]. IEEE Transactions on Robotics, 2012, 28(6): 1335-1345. doi: 10.1109/TRO.2012.2206869
    [16]
    Zheng J, Huang Y, Xiao Y. The Effect of Leaders on the Consistency of Group Behaviour[J]. International Journal of Sensor Networks, 2012, 11(2): 126-135. doi: 10.1504/IJSNET.2012.045962
    [17]
    Droge G. Distributed Virtual Leader Moving Formation Control using Behavior-based MPC[C]//American Control Conference. Chicago, America: IEEE, 2015: 2323-2328.
    [18]
    Li T, Zhao R, Chen C, et al. Finite-Time Formation Control of Under-Actuated Ships Using Nonlinear Sliding Mode Control[J]. IEEE Transactions on Cybernetics, 2018, 48(11): 3243-3253. doi: 10.1109/TCYB.2018.2794968
    [19]
    陈伟, 严卫生, 崔荣鑫. 障碍物环境下的多AUV主从式编队控制[J]. 鱼雷技术, 2013, 21(6): 431-435.

    Chen Wei, Yan Wei-sheng, Cui Rong-xin. Multi-AUV Leader-Follower Formation Control in Obstacle Environment[J]. Torpedo Technology, 2013, 21(6): 431-435.
    [20]
    Balch T, Arkin R C. Behavior-based Formation Control for Multirobot Teams[J]. IEEE Transactions on Robotics & Automation, 1998, 14(6): 926-939.
    [21]
    Ren W, Beard R W. A Decentralized Scheme for Spacecraft Formation Flying via the Virtual Structure Approach[J]. Journal of Guidance, Control, and Dynamics, 2004, 27(1): 1746-1751.
    [22]
    Krick L. Application of Graph Rigidity in Formation Control of Multi-Robot Networks[D]. Canada: University of Toronto, 2007.
    [23]
    Xiao Y, Peng M, Gibson J, et al. Tight Performance Bounds of Multihop Fair Access for MAC Protocols in Wireless Sensor Networks and Underwater Sensor Networks[J]. IEEE Transactions on Mobile Computing, 2012, 11(10): 1538-1554. doi: 10.1109/TMC.2011.190
    [24]
    Xiao Y, Zhang Y, Gibson J H, et al. Performance Analysis of ALOHA and P-persistent ALOHA for Multi-hop Underwater Acoustic Sensor Networks[J]. Cluster Computing, 2011, 14: 65-80. doi: 10.1007/s10586-009-0093-z
    [25]
    Lurton, X. An Introduction to Underwater Acoustics: Principles and Applications[M]. Berlin: Springer, 2010.
    [26]
    Yan J, Gao J, Yang X, et al. Position Tracking Control of Remotely Operated Underwater Vehicles with Communication Delay[J]. IEEE Transactions on Control Systems Technology, 2020, 28(6): 2503-2514.
    [27]
    Suryendu C, Subudhi B. Modified Constrained Adaptive Formation Control Scheme for Autonomous Underwater Vehicles Under Communication Delays[J]. IET Cyber-Systems and Robotics, 2021, 2(1): 22-30.
    [28]
    Wei H, Shen C, Shi Y. Distributed Lyapunov-based Model Predictive Formation Tracking Control for Autonomous Underwater Vehicles Subject to Disturbances[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(8): 5198-5208. doi: 10.1109/TSMC.2019.2946127
    [29]
    Gao Z, Guo G. Fixed-time Sliding Mode Formation Control of AUVs Based on a Disturbance Observer[J]. IEEE/CAA Journal of Automatica Sinica, 2020, 7(2): 228-234.
    [30]
    Liang X, Qu X, Wang N, et al. A Novel Distributed and Self-organized Swarm Control Framework for Underactuated Unmanned Marine Vehicles[J]. IEEE Access, 2019, 7: 112703-112712. doi: 10.1109/ACCESS.2019.2934190
    [31]
    Jian C, Lin Z, Yu J, et al. Distributed Finite-time Adaptive Consensus Tracking Control for Multiple AUVs with State Constraints[J]. Journal of the Franklin Institute, 2021, 358(17): 9158-9177. doi: 10.1016/j.jfranklin.2021.09.022
    [32]
    Tabuada P. Event-Triggered Real-Time Scheduling of Stabilizing Control Tasks[J]. IEEE Transactions on Automatic Control, 2007, 52(9): 1680-1685. doi: 10.1109/TAC.2007.904277
    [33]
    Yoo S J, Kim J H. Distributed Event-driven Adaptive Three-dimensional Formation Tracking of Networked Autonomous Underwater Vehicles with Unknown Nonlinearities[J]. Ocean engineering, 2021, 233: 109069. doi: 10.1016/j.oceaneng.2021.109069
    [34]
    Gao Z, Guo G. Fixed-time Leader-Follower Formation Control of Autonomous Underwater Vehicles with Event-triggered Intermittent Communications[J]. IEEE Access, 2018, 6: 27902-27911. doi: 10.1109/ACCESS.2018.2838121
    [35]
    Xu Y, Li T, Tong S. Event-triggered Adaptive Fuzzy Bipartite Consensus Control of Multiple Autonomous Underwater Vehicles[J]. IET Control Theory & Applications, 2020, 14(20): 3632-3642.
    [36]
    Qin J, Li M, Shi L, et al. Optimal Denial-of-Service Attack Scheduling With Energy Constraint Over Packet-Dropping Networks[J]. IEEE Transactions on Automatic Control, 2018, 63(6): 1648-1663. doi: 10.1109/TAC.2017.2756259
    [37]
    Liu S, Song Y, Wei G, et al. RMPC-based Cecurity Problem for Polytopic Uncertain System Subject to Deception Attacks and Persistent Disturbances[J]. IET Control Theory and Applications, 2017, 11(10): 1611-18. doi: 10.1049/iet-cta.2017.0153
    [38]
    Persis C D, Tesi P. Input-to-State Stabilizing Control Under Denial-of-Service[J]. IEEE Transactions on Automatic Control, 2015, 60(11): 2930-2944. doi: 10.1109/TAC.2015.2416924
    [39]
    Zhang D, Tang Y, Ding Z, et al. Event-Based Resilient Formation Control of Multiagent Systems[J]. IEEE Transactions on Cybernetics, 2021, 51(5): 2490-2503. doi: 10.1109/TCYB.2019.2910614
    [40]
    Tang Y, Zhang D, Shi P, et al. Event-Based Formation Control for Nonlinear Multiagent Systems Under DoS Attacks[J]. IEEE Transactions on Automatic Control, 2021, 66(1): 452-459. doi: 10.1109/TAC.2020.2979936
    [41]
    Wu Y, Ta X, Xiao R, et al. Survey of Underwater Robot Positioning Navigation[J]. Applied Ocean Research, 2019, 90: 101845. doi: 10.1016/j.apor.2019.06.002
    [42]
    李佳橦, 张臣, 张宏欣. 基于单信标纯方位测量的AUV水下定位方法[J]. 水下无人系统学报, 2018, 26(4): 310-315.

    Li Jia-tong, Zhang Chen, Zhang Hong-xin. Underwater Localization Method of AUV Based on Single Beacon Bearing-Only Measurement[J]. Journal of Unmanned Undersea Systems, 2018, 26(4): 310-315.
    [43]
    Allotta B, Caiti A, Costanzi R, et al. Development and Online Validation of an UKF-based Navigation Algorithm for AUVs[J]. IFAC-Papers on Line, 2016, 49(15): 69-74. doi: 10.1016/j.ifacol.2016.07.711
    [44]
    Yan J, Zhao H, Luo X, et al. Asynchronous Localization of Underwater Target Using Consensus-based Unscented Kalman Filtering[J]. IEEE Journal of Oceanic Engineering, 45(4), 1466-1481.
    [45]
    Yan J, Xu Z, Luo X, et al. Feedback-based Target Localization in Underwater Sensor Networks: A Multisensor Fusion Approach[J]. IEEE Transactions on Signal and Information Processing over Network, 2019, 5(1): 168-180. doi: 10.1109/TSIPN.2018.2866335
    [46]
    Yan J, Zhang X, Luo X, et al. Asynchronous Localization With Mobility Prediction for Underwater Acoustic Sensor Networks[J]. IEEE Transactions on Vehicular Technology, 2018, 67(3): 2543-2556. doi: 10.1109/TVT.2017.2764265
    [47]
    Huang Y, Zhang Y, Zhao Y, et al. Robust Rauch-Tung-Striebel Smoothing Framework for Heavy-Tailed and/or Skew Noises[J]. IEEE Transactions on Aerospace and Electronic Systems, 2020, 56(1): 415-441. doi: 10.1109/TAES.2019.2914520
    [48]
    Huang Y, Zhang Y, Shi P, et al. Robust Kalman Fifilters Based on Gaussian Scale Mixture Distributions with Application to Target Tracking[J]. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2019, 49(10): 2082-2096. doi: 10.1109/TSMC.2017.2778269
    [49]
    Karlgaard C D, Schaub H. Huber-based Divided Difference Fifiltering[J]. Journal of Guidance, Control, and Dynamics, 2007, 30(3): 885-891. doi: 10.2514/1.27968
    [50]
    Karlgaard C D. Nonlinear Regression Huber-Kalman Fifiltering and Fifixed-interval Smoothing[J]. Journal of Guidance, Control, and Dynamics, 2014, 38(2): 322-330.
    [51]
    Chen B, Liu X, Zhao H, et al. Maximum Correntropy Kalman Filter[J]. Automatica, 2017, 76: 70-77. doi: 10.1016/j.automatica.2016.10.004
    [52]
    Huang Y, Zhang Y, Wu Z, et al. A Novel Robust Student’s t-based Kalman Fifilter[J]. IEEE Transactions on Aerospace Wlectronic System, 2017, 53(3): 1545-1554. doi: 10.1109/TAES.2017.2651684
    [53]
    Zhou Z, Peng Z, Cui J, et al. Scalable Localization with Mobility Prediction for Underwater Sensor Networks[J]. IEEE Transactions on Mobile Computing, 2011, 10(3): 335-348. doi: 10.1109/TMC.2010.158
    [54]
    Luo H, Wu K, Gong Y, et al. Localization for Drifting Restricted Floating Ocean Sensor Networks[J]. IEEE Transactions on Vehicular Technology, 2016, 65(12): 9968-9981. doi: 10.1109/TVT.2016.2530145
    [55]
    Liu J, Wang Z, Cui J, et al. A Joint Time Synchronization and Localization Design for Mobile Underwater Sensor Networks[J]. IEEE Transactions on Mobile Computing, 2016, 15(3): 530-543. doi: 10.1109/TMC.2015.2410777
    [56]
    Mortazavi E, Javidan R, Dehghani M, et al. A Robust Method for Underwater Wireless Sensor Joint Localization and Synchronization[J]. Ocean Engineering, 2017, 137(1): 276-286.
    [57]
    Yan J, Gong Y, Chen C, et al. AUV-aided Localization for Internet of Underwater Things: A Reinforcement Learning-based Method[J]. IEEE Internet of Things Journal, 2020, 7(10): 9728-9746. doi: 10.1109/JIOT.2020.2993012
    [58]
    Yan J, Meng Y, Yang X, et al. Privacy-preserving Localization for Underwater Sensor Networks via Deep Reinforcement Learning[J]. IEEE Transactions on Information Forensics and Security, 2021, 16(1): 1880-1895.
    [59]
    Yan J, Li X, Yang X, et al. Integrated Localization and Tracking for AUV with Model Uncertainties via Scalable Sampling-based Reinforcement Learning Approach[J/OL]. IEEE Transactions on Systems, Man, and Cybernetics: Systems. [2022-12-08]. https://www.scholarmate.com/A/gM9oxK
    [60]
    Yang M, Li C Z. Path Planing and Tracking for Multi-robot System Based on Improved PSO Algorithm[C]//In International Conference on Mechatronic Science, Electric Engineering and Computer(MEC). Jilin, China: IEEE, 2011.
    [61]
    Yan X, Gu F, Song C, et al. Dynamic Formation Control for Autonomous Underwater Vehicles[J]. Journal of Central South University, 2014, 21(1): 113-123. doi: 10.1007/s11771-014-1922-7
    [62]
    Li J, Zhang R, Yang Y. Research on Route Obstacle Avoidance Task Planning Based on Differential Evolution Algorithm for AUV[C]// International Conference in Swarm Intelligence. [S. l.]: Springer International Publishing, 2014.
    [63]
    Zhu D, Huang Z. A Cooperative Hunting Algorithm of Multi-AUV in 3-D Dynamic Environment[C]// Control & Decision Conference. Qingdao, China: IEEE, 2015.
    [64]
    Zhu D, Lv R, Cao X, et al. Multi-AUV Hunting Algorithm Based on Bio-inspired Neural Network in Unknown Environments[J]. International Journal of Advanced Robotic Systems, 2015, 12(11): 166-166. doi: 10.5772/61555
    [65]
    Zhu D, Cao X, Sun B, et al. Biologically Inspired Self-organizing Map Applied to Task Assignment and Path Planning of an AUV System[J]. IEEE Trans. Cognitive and Developmental Systems, 2018, 10(2): 304-313. doi: 10.1109/TCDS.2017.2727678
    [66]
    Cao X, Zhu D. Multi-AUV Underwater Cooperative Search Algorithm Based on Biological Inspired Neurodynamics Model and Velocity Synthesis[J]. Journal of Navigation, 2015, 68(6): 1075-1087. doi: 10.1017/S0373463315000351
    [67]
    Cao X, Zhu D. Multi-AUV Task Assignment and Path Planning with Ocean Current Based on Biological Inspired Self-organizing Map and Velocity Synthesis Algorithm[J]. Intelligent automation and soft computing, 2017, 23(1): 31-39. doi: 10.1080/10798587.2015.1118277
    [68]
    Li J, Zhang R. Multi-AUV Distributed Task Allocation Based on the Differential Evolution Quantum Bee Colony Optimization Algorithm[J]. Polish Maritime Research, 2017, 24(s3): 65-71. doi: 10.1515/pomr-2017-0106
    [69]
    Lee J, Lee S, Chen H, et al. Composing Web Services Enacted by Autonomous Agents Through Agent-centric Contract Net Protocol[J]. Information & Software Technology, 2012, 54(9): 951-967.
    [70]
    Juan L, Zhang K, University H. Heterogeneous Multi-AUV Cooperative Task Allocation Based on Improved Contract Net Algorithm[J]. Journal of Unmanned Undersea Systems, 2017, 25: 418-423.
    [71]
    Bertsekas D. Auction Algorithms for Network Flow Problems: A Tutorial Introduction[J]. Computational Optimization & Applications, 1992, 1(1): 7-66.
    [72]
    Zavlanos M, Spesivtsev L, Pappas G. A Distributed Auction Algorithm for the Assignment Problem[C]//IEEE Conference on Decision & Control. Cancun, Mexico: IEEE, 2008.
    [73]
    Otte M, Kuhlman M, Sofge D. Auctions for Multi-robot Task Allocation in Communication Limited Environments[J]. Autonomous Robots, 2020, 44(3): 547-584.
    [74]
    李鑫滨, 郭力争, 韩松. 一种分布式异构多AUV任务分配鲁棒拍卖算法[J]. 北京航空航天大学学报, 2022, 48(5): 736-746. doi: 10.13700/j.bh.1001-5965.2020.0655

    Li Xin-bin, Guo Li-zheng, Han Song. A Robust Auction Algorithm for Distributed Heterogeneous Multi-AUV Task Assignment[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(5): 736-746. doi: 10.13700/j.bh.1001-5965.2020.0655
    [75]
    Di B, Zhou R, Ding Q. Distributed Coordinated Heterogeneous Task Allocation for Unmanned Aerial Vehicles[J]. Control and Decision, 2013, 28(2): 274-278.
    [76]
    Kohonen T. Self-organized Formation of Topologically Correct Feature Maps[J]. Biological Cybernetics, 1982, 43(1): 59-69. doi: 10.1007/BF00337288
    [77]
    Han M, Okada N, Kondo E. Coordination of an Uncalibrated 3-D Visuo-motor System Based on Multiple Self-organizing Maps[J]. JSME International Journal, Series C. Mechanical Systems, Machine Elements and Manufacturing, 2006, 49(1): 230-239.
    [78]
    朱大奇, 李欣, 颜明重. 多自治水下机器人多任务分配的自组织算法[J]. 控制与决策, 2012, 27(8): 1201-1205.

    Zhu Da-qi, Li Xin, Yan Ming-zhong. Task Assignment Algorithm of Multi-AUV Based on Self-organizing Map[J]. Control and Decision, 2012, 27(8): 1201-1205.
    [79]
    Zhu D, Huang H, Yang S. Dynamic Task Assignment and Path Planning of Multi-AUV System Based on an Improved Self-Organizing Map and Velocity Synthesis Method in Three-Dimensional Underwater Workspace[J]. IEEE Transactions on Cybernetics, 2013, 43(2): 504-514. doi: 10.1109/TSMCB.2012.2210212
    [80]
    朱大奇, 曹翔. 多个水下机器人动态任务分配和路径规划的信度自组织算法[J]. 控制理论与应用, 2015, 32(6): 762-769. doi: 10.7641/CTA.2015.40996

    Zhu Da-qi, Cao Xiang. An Improved Self-organizing Map Method for Multiple Autonomous Underwater Vehicle Teams in Dynamic Task Assignment and Path Planning[J]. Control Theory & Applications, 2015, 32(6): 762-769. doi: 10.7641/CTA.2015.40996
    [81]
    张子迎, 宫思远, 徐东, 等. 多机器人任务分配与路径规划算法[J]. 哈尔滨工程大学学报, 2019, 40(10): 1753-1759. doi: 10.11990/jheu.201811054

    Zhang Zi-ying, Gong Si-yuan, Xu Dong, et al. Research on Multi-robot Task Assignment and Path Planning Algorithm[J]. Journal of Harbin Engineering University, 2019, 40(10): 1753-1759. doi: 10.11990/jheu.201811054
    [82]
    Zhu D, Liu Y, Sun B. Task Assignment and Path Planning of a Multi-AUV System Based on a Glasius Bio-Inspired Self-Organising Map Algorithm[J]. Journal of Navigation, 2018, 71(2): 482-496. doi: 10.1017/S0373463317000728
    [83]
    Zhu D, Zhou B, Yang S. A Novel Algorithm of Multi-AUVs Task Assignment and Path Planning Based on Biologically Inspired Neural Network Map[J]. IEEE Transactions on Intelligent Vehicles, 2021, 6(2): 333-342. doi: 10.1109/TIV.2020.3029369
    [84]
    Zhu D, Lv R, Cao X, et al. Multi-AUV Hunting Algorithm Based on Bio-inspired Neural Network in Unknown Environments[J]. International Journal of Advanced Robotic Systems, 2015, 12(11): 1-12.
    [85]
    Cao X, Yu H, Sun H. Dynamic Task Assignment for Multi-AUV Cooperative Hunting[J]. Intelligent Automation & Soft Computing, 2018, 25(1): 1-11.
    [86]
    Ni J, Yang L, Wu L, et al. An Improved Spinal Neural System-Based Approach for Heterogeneous AUVs Cooperative Hunting[J]. International Journal of Fuzzy Systems, 2018, 20(2): 672-686. doi: 10.1007/s40815-017-0395-x
    [87]
    Chen M, Zhu D. A Novel Cooperative Hunting Algorithm for Inhomogeneous Multiple Autonomous Underwater Vehicles[J]. IEEE Access, 2018, 6: 7818-7828. doi: 10.1109/ACCESS.2018.2801857
    [88]
    Cao X, Xu X. Hunting Algorithm for Multi-AUV Based on Dynamic Prediction of Target Trajectory in 3D Underwater Environment[J]. IEEE Access, 2020, 8: 138529-138538. doi: 10.1109/ACCESS.2020.3013032
    [89]
    Cao W, Yan J, Yang X, et al. Communication-Aware Formation Control of AUVs with Model Uncertainty and Fading Channel via Integral Reinforcement Learning[C]// IEEE/CAA Journal of Automatica Sinica. [S. l. ]: IEEE, 2021.
    [90]
    Yan J, Cao W, Yang X, et al. Communication-Efficient and Collision-Free Motion Planning of Underwater Vehicles via Integral Reinforcement Learning[J/OL]. IEEE Transactions on Neural Networks and Learning Systems, (2022-12-13)[2022-12-15]. https://ieeexplore.ieee.org/document/9983989. DOI: 10.1109/TNNLS.2022.3226776.
    [91]
    Guérin É, Digne J, Galin É, et al. Interactive Example-Based Terrain Authoring with Conditional Generative Adversarial Networks[J]. ACM Transactions on Graphics, 2017, 36(6): 1-13.
    [92]
    Kai W, Savva M, Chang A, et al. Deep Convolutional Priors for Indoor Scene Synthesis[J]. ACM Transactions on Graphics, 2018, 37(4): 1-14.
    [93]
    Yan J, Zhao H, Meng Y, et al. Localization in Underwater Sensor Networks[M]. Beijing: Springer, 2021.
    [94]
    Yan J, Yang X, Zhao H, et al. Autonomous Underwater Vehicles: Localization, Tracking, and Formation[M]. Beijing: Springer, 2021.
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