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Volume 28 Issue 6
 
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Article Navigation >  Journal of Unmanned Undersea Systems  > 2020  >  28(6): 584-596
XIE Shao-rong, LIU Jian-jian, ZHANG Dan. Current Development of Control Technology for Unmanned Surface Vessel Clusters under Complex Sea Conditions[J]. Journal of Unmanned Undersea Systems, 2020, 28(6): 584-596. doi: 10.11993/j.issn.2096-3920.2020.06.001
Citation: XIE Shao-rong, LIU Jian-jian, ZHANG Dan. Current Development of Control Technology for Unmanned Surface Vessel Clusters under Complex Sea Conditions[J]. Journal of Unmanned Undersea Systems, 2020, 28(6): 584-596. doi: 10.11993/j.issn.2096-3920.2020.06.001
shu

Current Development of Control Technology for Unmanned Surface Vessel Clusters under Complex Sea Conditions

doi: 10.11993/j.issn.2096-3920.2020.06.001
  • 1.

    School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China

  • 2.

    School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China

  • Received Date: 2020-09-27
  • Rev Recd Date: 2020-11-23
  • Publish Date: 2020-12-31
    Abstract
  • As highly autonomous systems, unmanned surface vessels (USVs) are a reliable means of improving working efficiency on water in such areas as hydrology research, scientific exploration, hydrographic surveys, emergency search and rescue, and security patrol. Advancements in exchange information and collaborative decision-making have enabled the development of USV cluster systems. These systems can obtain more complete perception information and have a high execution efficiency and greater operating range, which considerably enhance the capabilities of USVs to complete tasks autonomously. However, because of complex and changeable marine environmental factors such as wind, waves, and sea currents, collaborative control and optimization decision-making of USV cluster systems face challenges related to single USV autonomous and complete perception mechanism, rapid and flexible interactive cognition of multiple USVs, and real-time efficient cluster collaboration. This study summarizes the latest developments of USV cluster systems in the following four respects: 1) the single USV complete autonomous perception mechanism, 2) the multiple-USV real-time interactive cognition mechanism, 3) the intelligent collaborative control decision methodology, and 4) the USV verification platform. Finally, problems that remain to be addressed as well as future research directions in the field are also briefly discussed. Single USV autonomous and complete perception, rapid and flexible interactive cognition among multiple USVs, and real-time efficient cluster collaboration are the research directions of cluster control technology.

     

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  • [1]
    Larson J, Bruch M, Halterman R, et al. Advances in Autonomous Obstacle Avoidance for Unmanned Surface Vehicles[J]. Association for Unmanned Vehicle Systems International-Unmanned Systems North America Conference, 2007, 1: 484-498.
    [2]
    田明辉, 万寿红, 岳丽华. 遥感图像中复杂海面背景下的海上舰船检测[J]. 小型微型计算机系统, 2008, 29(11): 2162-2166.

    Tian Ming-hui, Wan Shou-hong, Yue Li-hua. Ship Detection in Remote Sensing Images with Complex Sea Surface Background[J]. Journal of Chinese Computer Systems, 2008, 29(11): 2162-2166.
    [3]
    Mabel M Z, Jean C, Kostas D, et al. VAIS: A Dataset for Recognizing Maritime Imagery in the Visible and Infrared Spectrums[C]//The IEEE Conference on Computer Vision and Pattern Recognition Workshops. Boston, MA, USA: IEEE, 2015.
    [4]
    Matej K, Vildana S K, Sulic K, et al. Fast Image-Based Obstacle Detection From Unmanned Surface Vehicles[J]. IEEE Transactions on Cybernetics, 2016, 46(3): 641-654.
    [5]
    Ricardo R, Goncalo C, Jorge M, et al. A Dataset for Air-borne Maritime Surveillance Environments[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 29(9): 2720-2732.
    [6]
    Dilip K P, Deepu R, Lily R, et al. Video Processing from Electro-optical Sensors for Object Detection and Tracking in Maritime Environment: A Survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(8): 1997- 2016.
    [7]
    Perez L, Wang J. The Effectiveness of Data Augmentation in Image Classification Using Deep Learning[EB/OL]. arXiv, (2017-12-13)[2020-08-25]. https://arxiv.org/abs/1712.04621v1.
    [8]
    Cubuk E D, Zoph B, Mane D, et al. AutoAugment: Learning Augmentation Policies from Data[EB/OL]. arXiv, (2019-04-11)[2020-08-25]. http://export.arxiv.org/ abs/1805.09501v3.?/div>
    [9]
    Song R, Liu Y, Ralph R M, et al. Mesh Saliency via Spectral Processing[J]. ACM Transactions on Graphics, 2014, 33(1): 6.1-6.17.
    [10]
    Song R, Liu Y, Ralph R M, et al. Saliency-Guided Integration of Multiple Scans[C]//IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA: IEEE, 2012.
    [11]
    Schreck C, Hafner C, Wojtan C. Fundamental Solutions for Water Wave Animation[J]. ACM Transactions on Graphics, 2019, 38(4): 130.1-130.14.
    [12]
    Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative Adversarial Nets[J]. arXiv, (2014-06-10)[2020-08-25]. http://www.andrew.cmu.edu/user/rmorina/papers/GANs.pdf.
    [13]
    Kingma D P, Welling M. Auto-encoding Variational Bayes[J]. arXiv, (2014-05-01)[2020-08-25]. https://arxiv. org/abs/1312.6114.?/div>
    [14]
    Makhzani A, Shlens J, Jaitly N, et al. Adversarial Autoencoders[J]. arXiv, (2016-05-25)[2020-08-25]. https://arxiv.org/abs /1511.05644.?/div>
    [15]
    Zhu J, Taesung P, Phillip I, et al. Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks[C]//The IEEE International Conference on Computer Vision. Venice, Italy: IEEE, 2017: 2223-2232.
    [16]
    Ledig C, Theis L, Huszar F, et al. Photo-realistic Single Image Super-Resolution Using a Generative Adversarial Network[J]. arXiv, (2017-05-25)[2020-08-25]. https:// arxiv.org/abs/1609.04802.
    [17]
    Schwind P, Suri S, Reinartz P, et al. Applicability of the SIFT Operator to Geometric SAR Image Registration[J]. International Journal of Remote Sensing, 2010, 31(8): 1959-1980.
    [18]
    Wang S, Quan D, Liang X, et al. A Deep Learning Framework for Remote Sensing Image Registration[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 145(A): 148-164.
    [19]
    Pastina D, Lombardo P, Farina A, et al. Super-resolution of Polarimetric SAR Images of Ship Targets[J]. Signal Processing, 2013, 83(8): 1737-1748.
    [20]
    Teutsch M, Kruger W. Classification of Small Boats in Infrared Images for Maritime Surveillance[C]//2010 International WaterSide Security Conference. Carrara, Italy: IEEE, 2010: 1-7.
    [21]
    Wessman M. Object Detection Using LIDAR in Maritime Scenarios[D]. Finland: ?o Akademi University, 2018.
    [22]
    Lin T, Dollár P, Girshick R, et al. Feature Pyramid Net-works for Object Detection[C]//The IEEE Conference on Computer Vision and Pattern Recognition, Hawaii, USA: IEEE, 2017: 2117-2125.
    [23]
    Yang S, Deva R. Multi-scale Recognition with DAG-CNNs[C]//The IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015: 1215-1223.
    [24]
    Chen L, Yang Y, Wang J, et al. Attention to Scale: Scale-Aware Semantic Image Segmentation[C]//The IEEE Conference on Computer Vision And Pattern Recognition, Las Vegas, NV, USA: IEEE, 2016: 3640-3649.
    [25]
    Zhang G, Lu S, Zhang W. CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery[J]. IEEE Transactions On Geoscience and Remote Sensing, 2019, 57(12): 10015-10024.
    [26]
    Bloisi D D, Iocchi L, Fiorini M, et al. Camera Based Target Recognition for Maritime Awareness[C]//15th International Conference on Information Fusion. Singapore, Singapore: IEEE, 2012: 1982-1987.
    [27]
    Luan Y, Wadden D, He L, et al. A General Framework for Information Extraction Using Dynamic Span Graphs[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Minneapolis, Minnesota: NAACL, 2019.
    [28]
    郑家恒, 王兴义, 李飞. 信息抽取模式自动生成方法的研究[J]. 中文信息学报, 2004, 18(1): 49-55.

    Zheng Jia-heng, Wang Xing-yi, Li Fei. Research on Automatic Generation of Extraction Patterns[J]. Journal of Chinese Information Processing, 2004, 18(1): 49-55.
    [29]
    Pingle A, Piplai A, Mittal S, et al. RelExt: Relation Extraction Using Deep Learning Approaches for Cybersecurity Knowledge Graph Improvement[J]. arXiv, (2019- 05-16)[2020-08-25]. https://arxiv.org/abs/1905.02497?context=cs.AI.
    [30]
    Bordes A, Usunier N, Garcia-Duran A, et al. Translating Embeddings for Modeling Multi-Relational Data[C]//Proc of NIPS. Cambridge, MA: MIT Press, 2013: 2787-2795.
    [31]
    Lin Y, Liu Z, Sun M, et al. Learning Entity and Relation Embedding for Knowledge Graph Completion[C]//Proc of AAAI. Menlo Park, CA: AAAI, 2015.
    [32]
    Ji G, He S, Xu L, et al. Knowledge Graph Embedding Via Dynamic Mapping Matrix[C]//Pro. of ACL. Stroudsburg, PA: ACL, 2015: 687-696.
    [33]
    Hu Z, Huang P, Deng Y, et al. Entity hierarchy embedding[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. Beijing, China: ACL, 2015.
    [34]
    Nickel M, Volker T, Kriegel H P. A Three-Way Model for Collective Learning on Multi-Relational Data[C]//Proceedings of the 28th International Conference on International Conference on Machine Learning. Washington, USA: ICML, 2011.
    [35]
    Chang K W, Yih W, Yang B, et al. Typed Tensor Decom-position of Knowledge Bases for Relation Extraction[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Doha, Qatar: EMNLP, 2014.
    [36]
    He W, Feng Y, Zou L, et al. Knowledge Base Completion Using Matrix Factorization[C]//Asia-Pacific Web Conference. Guangzhou, China: APWeb, 2015.
    [37]
    Xiao H, Huang M, Hao Y, et al. TransG: A Generative Mixture Model for Knowledge Graph Embedding[J]. arXiv, (2017-09-08)[2020-08-25]. https://arxiv.org/abs/ 1509.05488.?/div>
    [38]
    He S, Liu K, Ji G, et al. Learning to Represent Knowledge Graphs with Gaussian Embedding[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. Melbourne, Australia: ACM, 2015.
    [39]
    巴宏欣, 赵宗贵, 杨飞. 态势估计——概念、内容与方法[J]. 解放军理工大学学报, 2004, 5(6): 10-16.

    Ba Hong-xin, Zhao Zong-gui, Yang Fei. Concepts, Contents and Methods of Situation Assessment[J]. Journal of PLA University of Science and Technology(Natural Sci-ence Edition), 2004, 5(6): 10-16.
    [40]
    赵宗贵, 李君灵, 王珂. 战场态势估计概念、结构与效能[J]. 中国电子科学研究院学报, 2010, 5(3): 226-230.

    Zhao Zong-Gui, Li Jun-Ling, Wang Ke. The Concept, Structure and Efficiency of Battlefield Situation Assessment[J]. Journal of China Academy of Electronics and Information Technology, 2010, 5(3): 226-230.
    [41]
    邵勇, 马丰文. 战场态势评估关键技术综述[C]//2007系统仿真技术及其应用学术会议, 珠海, 中国: 中国系统仿真学会, 2007: 913-915.
    [42]
    Wen H, Song J. Using Big Data to Enhance the Capability of the Situational Awareness of Battlefield[C]//Proceedings of 2018 Chinese Intelligent Systems Conference. Singapore, Singapore: Springer, 2019: 311-318.
    [43]
    Noreils F R. Toward a Robot Architecture Integrating Cooperation Between Mobile Robots: Application to In-door Environment[J]. Int. J. Robot. Res., 1993, 12(1): 79-98.
    [44]
    Luo C, Yang S X, Li X, et al. Neural-Dynamics-Driven Complete Area Coverage Navigation Through Cooperation of Multiple Mobile Robots[J]. IEEE Trans. Ind. Electron., 2017, 64(1): 750-760.
    [45]
    Saska M, Vonásek V, Krajník T, et al. Coordination and Navigation of Heterogeneous MAV–UGV Formations Localized by a “Hawk-Eye”-Like Approach Under a Model Predictive Control Scheme[J]. Int. J. Robot. Res., 2014, 33(10): 1393-1412.
    [46]
    Wu M H, Ogawa S, Konno A. Symmetry Position/Force Hybrid Control for Cooperative Object Transportation Using Multiple Humanoid Robots[J]. Adv. Robot., 2016, 30(2): 131-149.
    [47]
    王越超, 谈大龙, 黄闪, 等. 一个多智能体机器人协作装配系统[J]. 高技术通讯, 1998, 8(7): 6-10.

    Wang Yue-chao, Tan Da-long, Huang Shan, et al. A Multi-agent Robot Cooperative Assembly System[J]. Chinese High Technology Letters, 1998, 8(7): 6-10.
    [48]
    Andre T, Bettstetter C. Collaboration in Multi-robot Exploration: to Meet or Not to Meet?[J]. J. Intell. Robot. Syst., 2016, 82(2): 325-337.
    [49]
    Paliotta C, Belleter D J, Pettersen K Y. Adaptive Source Seeking with Leader-Follower Formation Control[J]. IFAC-Pap., 2015, 48(16): 285-290.
    [50]
    Tolba S, Ammar R. Virtual Tether Search: A Self-Constraining Search Algorithm for Swarms in an Open Ocean[C]//2017 IEEE Symposium on Computers and Communications(ISCC). Heraklion, Greece: IEEE, 2017: 1128-1135.
    [51]
    Matignon L, Jeanpierre L, Mouaddib A I. Coordinated Multi-Robot Exploration Under Communication Constraints Using Decentralized Markov Decision Processes[C]//Proceeding of the Twenty-sixth AAAI Conference on Artificial Intelligence. Toronto, Ontario, Canada: AAAI, 2012.
    [52]
    Qin J, Li M, Shi Y, et al. Optimal Synchronization Control of Multiagent Systems with Input Saturation via Off-Policy Reinforcement Learning[J]. IEEE Trans. Neural Netw. Learn. Syst., 2018, 30(1): 85-96.
    [53]
    Wang S, Huang J. Adaptive Leader-Following Consensus for Multiple Euler-Lagrange Systems with an Uncertain Leader System[J]. IEEE Trans. Neural Netw. Learn. Syst., 2019, 30(7): 2188-2196.
    [54]
    Mavrovouniotis M, Li C, Yang S. A Survey of Swarm Intelligence for Dynamic Optimization: Algorithms and Applications[J]. Swarm Evol. Comput., 2017, 33: 1-17.
    [55]
    Hinzmann T, Stastny T, Conte G, et al. Collaborative 3D Reconstruction Using Heterogeneous UAVs: System and Experiments[C]//2016 International Symposium on Experimental Robotics. Tokyo, Japan: Springer, 2017: 43-56.
    [56]
    Martins A, Dias A, Almeida J. Field Experiments for Marine Casualty Detection with Autonomous Surface Vehicles[C]//2013 OCEANS-San Diego. San Diego, CA, USA: IEEE, 2013: 1-5.
    [57]
    Sullivan A, York A M, An L, et al. How Does Perception at Multiple Levels Influence Collective Action in The Commons? The Case of Mikania Micrantha in Chitwan, Nepal[J]. Forest Policy and Economics, 2017, 80: 1-10.
    [58]
    Hussain Qureshi A, Ayaz Y. Intelligent Bidirectional Rapidly-Exploring Random Trees for Optimal Motion Planning in Complex Cluttered Environments[J]. Robotics and Autonomous Systems, 2015, 68: 1-11.
    [59]
    Hinostroza M A, Xu H, Guedes Soares C. Cooperative Operation of Autonomous Surface Vehicles for Maintaining Formation in Complex Marine Environment[J]. Ocean Eng., 2019, 183: 132-154.
    [60]
    Zhang Z, Cui P, Zhu W. Deep Learning on Graphs: A Survey[J/OL]. IEEE Transactions on Knowledge and Data Engineering, (2018-03-17)[2020-08-25]. https://ieeexplore.ieee.org/document/9039675.
    [61]
    Yao T, Pan Y, Li Y, et al. Exploring Visual Relationship for Image Captioning[C]//Proceedings of 15th European Conference. Munich, Germany: Springer, 2018.
    [62]
    Neamatollahi P, Abrishami S, Naghibzadeh M, et al. Hierarchical Clustering-Task Scheduling Policy in Cluster-Based Wireless Sensor Networks[J]. IEEE Transactions on Industrial Informatics, 2018, 14(5): 1876-1886.
    [63]
    张鹏, 崔勇. 移动自组织网络路由选择算法研究进展[J]. 计算机科学, 2010, 37(1): 10-22, 38.

    Zhang Peng, Cui Yong. Survey on Routing Algorithms of Mobile Ad Hoc Networks[J]. Computer Science, 2010, 37(1): 10-22, 38.
    [64]
    Yang W. Research on Ad Hoc Clustering Algorithm[J]. Computer Engineering & Applications, 2010, 46(21): 111-115.?/div>
    [65]
    王振. 无线自组织网络自适应算法研究[D]. 北京: 北京邮电大学, 2019.
    [66]
    张艳双. 基于免疫算法的无线传感器网络路由算法的研究[D]. 哈尔滨: 哈尔滨工程大学, 2009.
    [67]
    You B, Chen G, Guo W. A Discrete PSO-based Fault-Tolerant Topology Control Scheme in Wireless Sensor Networks[C]//Proceedings of 5th International Symposium. Wuhan, China: Springer, 2010: 1-12.
    [68]
    Basu P, Redi J. Movement Control Algorithms for Realization of Fault-Tolerant Ad Hoc Robot Networks[J]. IEEE Network, 2004, 18(4): 36-44.
    [69]
    Ahmadi M, Stone P. Keeping in Touch: Maintaining Bi-connected Structure by Homogeneous Robots[C]//Proceedings of the National Conference on Artificial Intelligence. Boston, Massachusetts: USA, 2006.
    [70]
    马巧云. 基于多系统的动态任务分配研究[D]. 武汉: 华中科技大学, 2006.
    [71]
    张荣国, 刘静, 张继福, 等. 产品并行设计多Agent系统中任务分解和协调模型的研究[J]. 太原重型机械学院学报, 2002, 23(2): 166-170.

    Zhang Rong-guo, Liu Jing, Zhang Ji-fu, et al. Study on Task Decomposition and Coordination Modeling in Product Concurrent Design Based on Multi-Agent System[J]. Journal of Taiyuan Heavy Machinery Institute, 2002, 23(2): 166-170.
    [72]
    龚卓君. 卫星应用任务分解技术研究[D]. 长沙: 国防科学技术大学, 2009.
    [73]
    宋言伟. 基于网络节点上下文的任务分解和调度方法研究[D]. 济南: 山东大学, 2012.
    [74]
    Robert Z, Tony S A, Bernardine D M, et al. Multi-robot Exploration Controlled by a Market Economy[C]//Proceedings of the IEEE International Conference on Robotica and Automation. Piscataway, USA: IEEE, 2002: 3016- 3023.
    [75]
    Bethauh M, Huang H, Keskinecad P. Robot Exploration with Combinatorial Auctions[C]//Proceedings of IEEE/ RSJ International Conference on Soft Computing for Problem Solving. Las Vegas, Nevada, USA: IEEE, 2003: 1957-1962.
    [76]
    Aguiar A P, Kaminer I, Ghabcheloo R, et al. Coordinated Path Following of Multiple UAVs for Time-Critical Missions in the Presence of Time-Varying Communication Topologies[J]. IFAC Proceedings Volumes, 2008, 41(2): 16015-16020.
    [77]
    B?haug E, Pavlov A, Panteley E, et al. Straight Line Path Following for Formations of Underactuated Marine Surface Vessels[J]. IEEE Transactions on Control Systems Technology, 2011, 19 (3): 493-506.
    [78]
    Li D, Li Q, Cheng N, et al. Sampling-Based Real-Time Motion Planning under State Uncertainty for Autonomous Micro-Aerial Vehicles in GPS-Denied Environments[J]. Sensors, 2014, 14(11): 21791-21825.
    [79]
    李明. 基于改进遗传算法的移动机器人路径规划研究[D]. 芜湖: 安徽工程大学, 2017.
    [80]
    Nikolos I K, Valavanis K P, Tsourveloudis N C, et al. Evolutionary Algorithm Based Offline/Online Path Planner for UAV Navigation[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 2003, 33(6): 898-912.
    [81]
    胡建章, 唐国元, 王建军, 等. 水面无人艇集群系统研究[J]. 舰船科学技术, 2019, 41(7): 83-88.

    Hu Jian-zhang, Tang Guo-yuan, Wang Jian-jun, et al. Research on Unmanned Surface Vehicle Cluster System[J]. Ship Science and Technology, 2019, 41(7): 83-88.
    [82]
    秦梓荷. 水面无人艇运动控制及集群协调规划方法研究[D]. 哈尔滨: 哈尔滨工程大学, 2018.
    [83]
    Breivik M, Hovstein V E, Fossen T I. Straight-line Target Tracking for Unmanned Surface Vehicles[J]. Modeling, Identification and control, 2008, 29(4): 13l-149．
    [84]
    Kjerstad K, Breivik M. Weather Optimal Positioning Control For Marine Surface Vessels[J]. IFAC Conference on Control Applications in Marine Systems, 2010, 43(20): 114-119．
    [85]
    Breivik M, Strand J P, Fossen T I. Guided Dynamic Positioning For Fully Actuated Marine Surface Vessels[C]//Proceedings of the 7th IFAC MCMC．Lisbon, Portugal: Elsevier, 2006: 1-6．
    [86]
    Breivik M, Fossen T I. Guidance Laws for Planar Motion Control[J]. IEEE Conference on Decision and Control, 2008, 16(5): 570-577．
    [87]
    Breivik M, Nonlinear Maneuvering Control of Underactuated Ships[D]. Trondheim: Norwegian University of Science and Technology, 2003: 75-79．
    [88]
    于海军. 基于抗倾覆性与浮态恢复性无人艇仿真研究[D]. 哈尔滨: 哈尔滨工程大学, 2012.
    [89]
    Larrazabal J M, Penas M S．Intelligent Rudder Control of an Unmanned Surface Vessel[J]．Expert Systems with Applications, 2016, 55: 106-117．
    [90]
    吴恭兴. 水面智能高速无人艇的控制与仿真[D]. 哈尔滨: 哈尔滨工程大学, 2008.
    [91]
    刘鹏. 基于Vega Prime的无人艇运动控制视景仿真[D]. 大连: 大连海事大学, 2018.
    [92]
    王卓, 冯晓宁, 万磊, 等. 水面无人艇协同仿真平台设计方法[J]. 哈尔滨工程大学学报, 2012, 33(3):275-282.

    Wang Zhuo, Feng Xiao-ning, Wan Lei, et al. Research on the Platform Design Method of a USV Collaborative Simulation[J]. Journal of Harbin Engineering University, 2012, 33(3):275-282.
    [93]
    伍文晖. 水面无人艇试验平台设计[D]. 大连: 大连海事大学, 2013.
    [94]
    胡辛明, 张鑫, 钟雨轩, 等. 无人水面艇仿真系统设计与实现[J]. 上海大学学报(自然科学版), 2017, 23(1): 56-67.

    Hu Xin-ming, Zhang Xin, Zhong Yu-xuan, et al. Design and Implementation of USV Simulation System[J]. Journal of Shanghai University(Natural Science Edition), 2017, 23(1): 56-67.
    [95]
    徐博, 肖永平, 高伟, 等. 主从式无人水面艇协同定位滤波方法与实验验证[J]. 兵工学报, 2014, 35(11): 1836-1845.

    Xu Bo, Xiao Yong-ping, Gao Wei, et al. A Cooperative Navigation Approach and Its Verification of USVs with Leader-fellower Structure[J]. Acta Armamentarii, 2014, 35(11): 1836-1845.
    [96]
    范云生, 李长飞, 王国峰, 等. 无人水面艇航向跟踪控制器的设计与验证[J]. 大连海事大学学报, 2017, 43(1): 1-7.

    Fan Yun-sheng, Li Chang-fei, Wang Guo-feng, et al. Design and Validation of Course Tracking Controller for Unmanned Surface Vehicle[J]. Journal of Dalian Maritime University, 2017, 43(1): 1-7.
    [97]
    文元桥, 杨吉, 王亚周, 等. 无人艇自适应路径跟踪控制器的设计与验证[J]. 哈尔滨工程大学学报, 2019, 40(3): 482-488.

    Wen Yuan-qiao, Yang Ji, Wang Ya-zhou, et al. Design and Validation of Adaptive Path Following Controller for USV[J]. Journal of Harbin Engineering University, 2019, 40(3): 482-488.
    [98]
    Shin J, Kwak D J, Lee Y I. Adaptive Path Following Control for an Unmanned Surface Vessel Using an Identified Dynamic Model[J]. IEEE/ASME Transactions on Mechatronics, 2017, 22(3): 1143-1153.
    [99]
    蒲华燕, 丁峰, 李小毛, 等. 基于椭圆碰撞锥的无人艇动态避障方法[J]. 仪器仪表学报, 2017, 38(7): 1756-1762.

    Pu Hua-yan, Ding Feng, Li Xiao-mao, et al. Maritime Autonomous Obstacle Avoidance in a Dynamic Environment Based on Collision Cone of Ellipse[J]. Chinese Journal of Scientific Instrument, 2017, 38(7):1756-1762.
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      沈阳化工大学材料科学与工程学院 沈阳 110142

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