• 中国科技核心期刊
  • JST收录期刊
  • Scopus收录期刊

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

自主水下航行器协同控制研究现状与发展趋势

闫敬 陈天明 关新平 杨晛 罗小元

闫敬, 陈天明, 关新平, 等. 自主水下航行器协同控制研究现状与发展趋势[J]. 水下无人系统学报, 2023, 31(1): 108-120 doi: 10.11993/j.issn.2096-3920.2022-0096
引用本文: 闫敬, 陈天明, 关新平, 等. 自主水下航行器协同控制研究现状与发展趋势[J]. 水下无人系统学报, 2023, 31(1): 108-120 doi: 10.11993/j.issn.2096-3920.2022-0096
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

自主水下航行器协同控制研究现状与发展趋势

doi: 10.11993/j.issn.2096-3920.2022-0096
基金项目: 国家自然科学基金优青项目资助(62222314)
详细信息
    作者简介:

    闫敬:闫 敬(1985−), 男, 博士生导师, 教授, 主要研究方向为水下机器人/传感网协同监测

  • 中图分类号: TJ630.33; U664.82; TP273.2

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

  • 摘要: 自主水下航行器(AUV)的协同控制作为海洋开发和多机器人系统之间的交叉领域, 近几十年来越来越受到研究人员和工程师的关注。目前, AUV协同控制理论体系尚处于构建之中, 相关研究正面临诸多亟待解决的难题。文中对多 AUV 协同控制问题中的编队控制、协同导航和定位、协同路径规划、任务分配以及目标围捕等研究进行了全面调研, 同时分析了编队控制的网络架构、协同策略以及其面临的约束等问题。最后讨论了未来可能研究的相关方向, 为在复杂的海洋应用场景中合理利用AUV来完成各种水下任务提供相关参考。

     

  • 图  1  多AUV协同控制场景

    Figure  1.  Scenario of the cooperation control for multi-AUVs

    图  2  美国防部无人系统发展路线图部分封面

    Figure  2.  Partial covers of the unmanned system development roadmaps of US department of defense

    图  3  AUV编队控制架构图

    Figure  3.  Architecture diagram of AUV formation control

    图  4  合同网算法原理图

    Figure  4.  Schematic diagram of contract network algorithm

    图  5  多AUV探测−通信−控制一体化设计

    Figure  5.  Co-design of detection, communication and control for multi-AUVs

  • [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.
  • 加载中
图(5)
计量
  • 文章访问数:  2976
  • HTML全文浏览量:  617
  • PDF下载量:  242
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-12-20
  • 修回日期:  2023-01-10
  • 录用日期:  2023-01-10
  • 网络出版日期:  2023-01-19

目录

    /

    返回文章
    返回
    服务号
    订阅号