综合船桥系统航迹规划技术研究
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摘要
为了减少对船舶误操作引起的各种损失、降低工作人员的工作压力及提高船舶经济效益等,综合船桥系统逐渐发展起来。该系统包括众多的研究内容,航迹规划技术作为其中的一项关键技术,主要是指航线自动生成和优化的过程。先前航行规划技术需要通过工作人员查阅相关的航海图书和推荐航线资料,通过分析潮流等情况手动地将信息输入到系统中,这样并没有发挥出综合船桥系统的智能性。本文的研究考虑到传统航迹规划研究方法的限制和制约,通过获知航海区的信息,利用计算机技术和现代控制理论技术实现船舶的航迹规划设计。
针对纯数学规划方法在求解环境复杂时存在维数爆炸、计算时间长等问题,本文中采用两种智能算法——蚁群算法和遗传算法展开航迹规划设计的研究。
在文章中首先对蚁群算法和遗传算法的基本理论、算法结构、算法特点进行了比较深入的研究;其次介绍了船舶航行空间的建模,通过建模对航行环境中固定位置的禁航区(等深线区或碍航区)进行了必要的处理。并根据IHO S-57(第三版)的规定将禁航区的位置进行扩充,扩充后的区域形成一个环,环内部为禁航区不可行,环外部为可以自由航行的区域;再次在空间建模的基础上,根据船舶的航行特点分别对蚁群算法和遗传算法进行了算法内部结构参数的一些必要改进,并将两种算法应用到船舶的航迹规划中,实现综合船桥系统基本的航迹规划设计;最后根据航迹规划最佳航线计划设计的要求,综合偏航极限和安全离岸距离等概念进行了最佳航迹规划的设计,保障船舶的安全行驶。另外还综合考虑气象对航线的各种影响因素,并重点对船舶在不同方向顺流影响下的航迹规划进行了仿真,为船舶节能、增效起到一定的作用。
文章中航迹规划技术的基本航线设计和最佳航线设计均通过Matlab软件进行仿真实现,较之传统手工绘制航迹的方法提高了设计的效率和设计的准确性;较之查阅已有航线图的方法提高了设计的灵活性。通过各种仿真结果,证明了运用智能算法规划航迹速度快、方便灵活,能够较好的运用于不同的航行环境。
In order to reduce all kinds of heavy losses caused by incorrect operation、decrease workers’work pressure and increase the economic benefits, the Integrated Bridge System(IBS) develops gradually. This system includes many kinds of research contents, the ship path planning as a key technology refers the selfshipping and optimizing. Previously the ship path planning needed workers consulting the relevant maritime books and the path materials, analysising the circumstance like ocean currents, manually entering into the system. By this way it can not reflect the intelligence of the IBS. Considing the restriction and restrict of the traditional methods in the methods of ship path planning, this research through the known information of the sailing area, finishs the ship path planning by computer and control theory.
Because when solves the complex problem, the dimension will be exploded and the time for caculating will be such long by using the pure mathematics method, this paper studies two intelligent algorithms——ant colony algorithm and genetic algorithm.
Firstly, this paper introduces the basic theory of ant colony algorithm and genetic algorithm. Secondly, this paper introduces the modeling of ship sailing space, and makes some necessary processing of the fixed location obstacle zones. Obeying the IHO S-57 (the third edition) regulation, this paper extends the obstacle zones. The obstacle zones become ring after the extending, and inside the ring is not feasible, out of the ring is feasible. Based on the modeling of the ship sailing space, the paper improves some parameters of the two algorithms according to ship path planning. Finally, it realizes the optimal ship path planning by computer simulation. It puts forward the concept of yaw limit area and safe distance from the shore reef, ensuring the safe voyage. It also introduces the various factors affecting the weather route, and simulates different direction of the ocean current on ship path planning. It plays a part in the ship on energy saving and the economic efficiency improving.
The basic path planning and the optimal path planning are all simulated by the software of Matlab. It can greatly improve the efficiency and accuracy comparing with the traditional manual method. And it is also flexible comparing with method by refering to the existing route chart. Through various simulation results it proves that using the intelligent algorithms, the speed is fast , it is convenient and flexible when realizing. The design can applied to different navigation environments.
针对纯数学规划方法在求解环境复杂时存在维数爆炸、计算时间长等问题,本文中采用两种智能算法——蚁群算法和遗传算法展开航迹规划设计的研究。
在文章中首先对蚁群算法和遗传算法的基本理论、算法结构、算法特点进行了比较深入的研究;其次介绍了船舶航行空间的建模,通过建模对航行环境中固定位置的禁航区(等深线区或碍航区)进行了必要的处理。并根据IHO S-57(第三版)的规定将禁航区的位置进行扩充,扩充后的区域形成一个环,环内部为禁航区不可行,环外部为可以自由航行的区域;再次在空间建模的基础上,根据船舶的航行特点分别对蚁群算法和遗传算法进行了算法内部结构参数的一些必要改进,并将两种算法应用到船舶的航迹规划中,实现综合船桥系统基本的航迹规划设计;最后根据航迹规划最佳航线计划设计的要求,综合偏航极限和安全离岸距离等概念进行了最佳航迹规划的设计,保障船舶的安全行驶。另外还综合考虑气象对航线的各种影响因素,并重点对船舶在不同方向顺流影响下的航迹规划进行了仿真,为船舶节能、增效起到一定的作用。
文章中航迹规划技术的基本航线设计和最佳航线设计均通过Matlab软件进行仿真实现,较之传统手工绘制航迹的方法提高了设计的效率和设计的准确性;较之查阅已有航线图的方法提高了设计的灵活性。通过各种仿真结果,证明了运用智能算法规划航迹速度快、方便灵活,能够较好的运用于不同的航行环境。
In order to reduce all kinds of heavy losses caused by incorrect operation、decrease workers’work pressure and increase the economic benefits, the Integrated Bridge System(IBS) develops gradually. This system includes many kinds of research contents, the ship path planning as a key technology refers the selfshipping and optimizing. Previously the ship path planning needed workers consulting the relevant maritime books and the path materials, analysising the circumstance like ocean currents, manually entering into the system. By this way it can not reflect the intelligence of the IBS. Considing the restriction and restrict of the traditional methods in the methods of ship path planning, this research through the known information of the sailing area, finishs the ship path planning by computer and control theory.
Because when solves the complex problem, the dimension will be exploded and the time for caculating will be such long by using the pure mathematics method, this paper studies two intelligent algorithms——ant colony algorithm and genetic algorithm.
Firstly, this paper introduces the basic theory of ant colony algorithm and genetic algorithm. Secondly, this paper introduces the modeling of ship sailing space, and makes some necessary processing of the fixed location obstacle zones. Obeying the IHO S-57 (the third edition) regulation, this paper extends the obstacle zones. The obstacle zones become ring after the extending, and inside the ring is not feasible, out of the ring is feasible. Based on the modeling of the ship sailing space, the paper improves some parameters of the two algorithms according to ship path planning. Finally, it realizes the optimal ship path planning by computer simulation. It puts forward the concept of yaw limit area and safe distance from the shore reef, ensuring the safe voyage. It also introduces the various factors affecting the weather route, and simulates different direction of the ocean current on ship path planning. It plays a part in the ship on energy saving and the economic efficiency improving.
The basic path planning and the optimal path planning are all simulated by the software of Matlab. It can greatly improve the efficiency and accuracy comparing with the traditional manual method. And it is also flexible comparing with method by refering to the existing route chart. Through various simulation results it proves that using the intelligent algorithms, the speed is fast , it is convenient and flexible when realizing. The design can applied to different navigation environments.
引文
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[3]郑荣才,杨功流,李滋刚,汪顺亭.综合船桥系统综述[J].中国惯性技术学报.2009,17(6):661-665.
[4] Blagovest chanev belev. Information Capabilities of Integrated Bridge Systems[J]. The Journal of Navigation.2004,57(1):34-39.
[5] Geoff tayolr.Integrated bridge systems: The pilot's view[J]. Seaways.2003,0(11):37-41.
[6]王淑瑛.综合船桥系统的应用与发展[J].中外船舶科技.2007,(3):25-31,43.
[7] Denham E. Advanced bridge automation[J]. Marine Technology.1993,30(4):276-285.
[8]刘强,邱太琪,许江宁.国外综合船桥系统研究与发展[J].舰船电子工程.2007,(1):37-40.
[9]李丽.基于遗传算法的舰船航行路径规划技术研究[D].哈尔滨工程大学硕士学位论文.2006.03
[10]杨慧.基于ECDIS的最优航线设计与研究[D].江苏科技大学硕士学位论文.2009.03.
[11] Lee Alexander, Joseph F Ryan, Michael J Casey. Integrated Navigation System:not a sum of its parts[A]. Canadian Hydrographic Conference[C],Ottawa, 2004,05: 24-27.
[12] Komrakov E.Integrated Information System[J]. Safety at Sea International,1997,(10):19-20.
[13]周红进,许江宁.综合舰桥系统研究综述[J].中国惯性技术学报.2005,13(5):87-91.
[14]林伟国,王立新.综合船桥系统综述[J].船舶.2007,(4):15.
[15]郑昌文,严平,丁明跃,苏康.飞行器航迹规划研究现状与趋势[J].宇航学报.2007,28(6):1441-1446.
[16]朱世立.电子海图应用系统设计[M].北京:国防工业出版社,1997:1-41.
[17]巴海涛.无人机航迹规划研究[D].西北工业大学硕士学位论文.2006.03.
[18]薛彦卓.船舶操纵模拟与航迹规划研究[D].哈尔滨工程大学硕士学位论文.2006.05.
[19]段海滨.蚁群算法原理及其应用[M].北京:科学出版社,2005:1-42.
[20] Dorigo M. Optimization,learning and natural algorithms[M]. Ph.D.Thesis,Department of Electronics,Politecnico,Italy,1992.
[21] M.Dorigo,C.Blum. Ant Colony Optimization Theory:A Survey[J]. Theoretical Computer Science. 2005,(344):243-278.
[22] Bonabeau E , Dorigo M , Theraulaz G. Inspiration for optimization from social insect behavior[J] .Nature.2000,406(6):39-42.
[23]陈岩,苏菲,沈林成.概率地图UAV航线规划改进蚁群算法[J].系统仿真学报.2009,6(21):1658-1666.
[24] M.Dorigo , L.M. Gambardella. Ant colonies for the traveling salesman problem[J]. Biosystems.1997,43(2):738.
[25] Dorigo M,Gambardella L M. Ant Colony System: A Copperative Learning Approach to the Traveling Salesman Problem[J]. IEEE Trans. on Evolutionary computing.1997,1 (1):50-60.
[26] B Bullnheimer,R F Hartl,C Strauss. Applying the ant System to the Vehicle Routing Problem[J]. MetaHeuristics:Advances and Trends in Local Search Paradigms for Optimization.Kluwer. 1998:109-120.
[27] B Bullnheimer,R F Hartl,C Strauss. A New Rankbased Version of The ant System:A Computational Study[D]. Technical Report POM03/97,Institute of Management Science,University of Vienna,1997.
[28]李士勇,陈永强,李研.蚁群算法及其应用[M].哈尔滨:哈尔滨工业大学出版社,2004:1-58.
[29] V maniezzo,M Dorigo,A Colorni.The ant System Applied to the Quadratic Assignment Problem[D]. University de Bruxelles,1994.
[30] Stuetzle T.,Hoos H. The MAXMIN Ant System and Local Search for the Traveling Salesman Problem [J]. Proceedings of ICE’971997 IEEE 4th International Conference on Evolutionary Computation. IEEE Press. 1997,(21):308-313.
[31] M dorigo,L M Gambardella. Ant Colony System:A Cooperative Learning Apporach to the Traveling Salesman Problem[J]. IEEE Trans.On Evolutionary Computation.1997,1(1):53-66.
[32] Holland J H. Adaptation in Natural an Artificial Systems[D]. The University of Michigan Press,1989.
[33] Goldberg D. Genetic Algorithms in Search[J], Optimization and Machine Learning,AddisonWesley,Reading,MA.1989.
[34]李丽.基于遗传算法的舰船航行路径规划技术研究[D].哈尔滨工程大学硕士学位论文.2006.02.
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