多自主式水下机器人的路径规划和控制技术研究综述
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摘要
自主式水下机器人(AUV)是海洋资源勘探和开发的重要工具,在民用和军用领域都发挥着重要作用。随着AUV技术的逐步成熟,通过构建多自主式水下机器人(MAUV)系统,令多个AUV协作完成水下作业任务已成为当前的发展趋势。MAUV系统对提高水下机器人的智能化水平及发展海洋化装备具有重要的理论研究意义和实用价值。介绍目前MAUV系统的应用现状和科研进展,并对MAUV协同路径规划和集群协同控制技术等研究热点进行系统化梳理,着重分析人工智能优化和编队协同的关键技术。最后,对MAUV系统未来的发展方向进行展望。
The Autonomous Underwater Vehicle(AUV)is an important tool for ocean exploration and the exploitation of underwater resources,which plays important roles in civilian and military fields. Along withthe research progress of AUVs,it has become the current development trend to cooperate on completingunderwater operations by constructing the Multiple Autonomous Underwater Vehicle(MAUV)system. TheMAUV system has important theoretical research significance and practical value for improving theintelligence level of underwater vehicles and developing marine equipment. In this paper,the state of theart of the MAUV is presented from the point of view of practical application and scientific research. Themain methods of path planning and cooperative control for MAUV are illustrated,including artificialintelligence and formation control techniques. Finally,the research trends of MAUV are also discussed and the main topic is highlighted.
The Autonomous Underwater Vehicle(AUV)is an important tool for ocean exploration and the exploitation of underwater resources,which plays important roles in civilian and military fields. Along withthe research progress of AUVs,it has become the current development trend to cooperate on completingunderwater operations by constructing the Multiple Autonomous Underwater Vehicle(MAUV)system. TheMAUV system has important theoretical research significance and practical value for improving theintelligence level of underwater vehicles and developing marine equipment. In this paper,the state of theart of the MAUV is presented from the point of view of practical application and scientific research. Themain methods of path planning and cooperative control for MAUV are illustrated,including artificialintelligence and formation control techniques. Finally,the research trends of MAUV are also discussed and the main topic is highlighted.
引文
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[16] CUI R X,YAN W S,XU D M. Synchronization of multiple autonomous underwater vehicles without velocity measurements[J]. Science China Information Sciences,2012,55(7):1693-1703.
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[25] ZHU D Q,HUANG H,YANG S X. 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.
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[28] HUANG Q,ZHENG G L. Route optimization for autonomous container truck based on rolling window[J]. International Journal of Advanced Robotic Systems,2016,13(3):112.
[29] LIU J H,YANG J G,LIU H P,et al. An improved ant colony algorithm for robot path planning[J]. Soft Computing,2016,1(11):1-11. DOI:10.1007/s00500-016-2161-7.
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[31] FAVARO F,BROLO L,TOSO G,et al. A study on remote data retrieval strategies in underwater acoustic networks[C]//Proceedings of Oceans 2013-San Diego. San Diego,CA:IEEE,2013:1-8.
[32] KARTHIK S. Underwater vehicle for surveillance with navigation and swarm network communication[J]. Indian Journal of Science and Technology,2014,7(6):22-31.
[33] CAO X,ZHU D Q. Multi-AUV underwater cooperative search algorithm based on biological inspired neurodynamics model and velocity synthesis[J]. The Journal of Navigation,2015,68(6):1075-1087.
[34] YING L L,HE B,ZHANG S J,et al. A modified fast SLAM with simple particle swarm optimization and consistent mapping for AUVs[C]//Proceedings of Oceans 2014-Taipei. Taipei,China:IEEE,2014:1-5.
[35]李爱国,覃征,鲍复民,等.粒子群优化算法[J].计算机工程与应用,2002,38(21):1-3,17.LI A G,QIN Z,BAO F M,et al. Particle swarm optimization algorithms[J]. Computer Engineering and Applications,2002,38(21):1-3,17(in Chinese).
[36] ZENG Z,LAMMAS A,SAMMUT K,et al. Path planning for rendezvous of multiple AUVs operating in a variable ocean[C]//Proceedings of the 4th Annual IEEE International Conference on Cyber Technology in Automation,Control,and Intelligent Systems(CYBER). Hong Kong,China:IEEE,2014:451-456.
[37]朱亦峰.多AUV协同作业中的互定位方法研究[D].哈尔滨:哈尔滨工程大学,2009.ZHU Y F. Research of intercommunication method of multi-AUV cooperation[D]. Harbin:Harbin Engineering University,2009(in Chinese).
[38] CONSOLINI L,MORBIDI F,PRATTICHIZZO D,et al. Leader-follower formation control of nonholonomic mobile robots with input constraints[J]. Automatica,2008,44(5):1343-1349.
[39]彭周华.舰船编队的鲁棒自适应控制[D].大连:大连海事大学,2011.PENG Z H. Robust adaptive control for formations of marine surface vessels[D]. Dalian:Dalian Maritime University,2011(in Chinese).
[40] CAO Z Q,XIE L J,ZHANG B,et al. Formation constrained multi-robot system in unknown environments[C]//Proceedings of 2003 IEEE International Conference on Robotics and Automation. Taipei,China:IEEE,2013:735-740.
[41] LIANG Y,LEE H H. Decentralized formation control and obstacle avoidance for multiple robots with nonholonomic constraints[C]//Proceedings of 2006 American Control Conference. Minneapolis,USA:IEEE,2006:6.
[42] LEONARD N E,FIORELLI E. Virtual leaders,artificial potentials and coordinated control of groups[C]//Proceedings of the 40th IEEE Conference on Decision andControl.Orlando,USA:IEEE,2011:2968-2973.
[43] ROUT R,SUBUDHI B. A backstepping approach for the formation control of multiple autonomous underwater vehicles using a leader-follower strategy[J]. Journal of Marine Engineering and Technology,2016,15(1):38-46.
[44]石桂芬,方华京.基于相邻矩阵的多机器人编队容错控制[J].华中科技大学学报(自然科学版),2005,33(3):39-42.SHI G F,FANG H J. Fault tolerance of multi-robot formation based on adjacency matrix[J]. Journal of Huazhong University of Science and Technology(Nature Science Edition),2005,33(3):39-42.
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