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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 蔡進發 | |
dc.contributor.author | Jheng-Hong Li | en |
dc.contributor.author | 李政宏 | zh_TW |
dc.date.accessioned | 2021-06-17T06:00:52Z | - |
dc.date.available | 2024-02-14 | |
dc.date.copyright | 2019-02-14 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-02-11 | |
dc.identifier.citation | 1.Rothblum, A. M. (2002). Keys to successful incident inquiry. In Human Factors in Incident Investigation and Analysis, 2nd International Workshop on Human Factors in Offshore Operations (HFW2002), Houston, TX.
2.Tam, C., Bucknall, R., & Greig, A. (2009). Review of collision avoidance and path planning methods for ships in close range encounters. The Journal of Navigation, 62(3), 455-476. 3.Fujii, Y., & Tanaka, K., Traffic Capacity. Journal of Navigation, 1971. 24(4): p. 543-552. 4.Goodwin, E. M. (1975). A statistical study of ship domains. The Journal of navigation, 28(3), 328-344. 5.Davis, P. V., Dove, M. J., & Stockel, C. T. (1980). A computer simulation of marine traffic using domains and arenas. The journal of Navigation, 33(2), 215-222. 6.Ito, M., Zhnng, F., & Yoshida, N. (1999). Collision avoidance control of ship with genetic algorithm. In Control Applications, 1999. Proceedings of the 1999 IEEE International Conference on (Vol. 2, pp. 1791-1796). IEEE. 7.Liu, Y. H., & Shi, C. J. (2005, August). A fuzzy-neural inference network for ship collision avoidance. In Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on (Vol. 8, pp. 4754-4759). IEEE. 8.Perera, L.P., J.P. Carvalho, and C.G. Soares, Intelligent Ocean Navigation and Fuzzy-Bayesian Decision/Action Formulation. IEEE Journal of Oceanic Engineering, 2012. 37(2): p. 204-219. 9.Perera, Lokukaluge P., et al. 'Experimental evaluations on ship autonomous navigation and collision avoidance by intelligent guidance.' IEEE Journal of Oceanic Engineering 40.2 (2015): 374-387. 10.Nomoto, K. and K. Taguchi, On Steering Qualities of Ships (2). Journal of Zosen Kiokai, 1957. 1957(101): p. 57-66. 11.Statheros, T., Howells, G., & Maier, K. M. (2008). Autonomous ship collision avoidance navigation concepts, technologies and techniques. The Journal of Navigation, 61(1), 129-142. 12.Russell, S.J. and P. Norvig, Artificial Intelligence: A Modern Approach. 2003. pp. 557-583,Sec. 15. 13.Mamdani, E.H., Application of fuzzy algorithms for control of simple dynamic plant. Proceedings of the Institution of Electrical Engineers, 1974. 121(12): p. 1585-1588. 14.Perera, Lokukaluge Prasad, Joao Paulo Carvalho, and C. Guedes Soares. 'Fuzzy-logic based parallel collisions avoidance decision formulation for an ocean navigational system.' Proc. 8th IFAC Conference on Control Applications in Marine Systems. 2010. 15.Zadeh, Lotfi A. 'Fuzzy sets.' Information and control 8.3 (1965): 338-353. 16.廖宗。乙級船員航行當值教材。國立高雄海洋科技大學卓越航輪教育訓練中心,高雄市。 17.Van Amerongen, J., H. R. van Nauta Lemke, and J. C. T. Van der Veen. 'An autopilot for ships designed with fuzzy sets.' Digital computer applications to process control (1977). 18.Bennett, S. (1984). Nicholas Minorsky and the automatic steering of ships. IEEE Control Systems Magazine, 4(4), 10-15. 19.謝文記(2017)。船舶節能自動操船系統之實驗研究。國立臺灣大學工程科學及海洋工程學研究所碩士論文,台北市。 20.游坤達(2009)。不同船型船舶迴旋特性之探討。國立臺灣海洋大學運輸與航海科學系碩士論文,基隆市。 21.廖坤靜,“航海力學”,中華民國海事學會海事出版社,民國八十八年八月。 22.ABS, Guide for vessel maneuverability. 2006. American Bureau of Shipping. 23.Fang, M., Tsai, K., & Fang, C. (2018). A Simplified Simulation Model of Ship Navigation for Safety and Collision Avoidance in Heavy Traffic Areas. Journal of Navigation, 71(4), 837-860. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71446 | - |
dc.description.abstract | 本研究之目的在於建置船舶自動避碰系統並透過實驗進行驗證,所建置的船舶自動避碰系統包含三個子系統,分別為偵測系統、避碰決策系統及控制系統。偵測系統連接各儀器設備並透過雷射掃描儀偵測他船之動態資訊,將此動態資訊透過卡爾曼一維模型進行修正並傳送至避碰決策系統。避碰決策系統透過模糊理論計算於各避碰情況下符合國際海上避碰規則之避碰航向,同時將此航向轉成於每一時刻船舶應達成之航向角速率並傳送至控制系統計算操舵。控制系統分為定航向操縱與變航向操縱兩個部分,定航向操縱於船舶偏離設定航向時藉由模糊理論計算回航時所需之操舵舵角,變航向操縱則透過迴旋圈試驗求得操縱性指數,並透過操縱性指數建置野本一階線性船舶操縱運動方程式,透過此運動方程式可計算出於每一時刻達航向角速率所需之操舵舵角。
實驗之方式為使本船沿一航向航行,於途中令他船從各方向行駛而來並造成碰撞危機,此時本船會以設計船速轉向至避碰航向直到碰撞危機解除,當碰撞危機解除時再使本船行駛於原航向,根據避碰實驗結果顯示,迎艏正遇避碰情況之兩船最接近點距離為2.4倍船長、交叉相遇(左)之兩船最接近點距離為2.0倍船長,表示皆可安全通過且最終皆回到原航向航行。 | zh_TW |
dc.description.abstract | An autonomous collision avoidance system was built and validated by the ship model collision experiments in this study. The autonomous collision avoidance system consists of three subsystems which are detection system, collision avoidance decision-making system(CADMS) and control system. The detection system consists of the various sensors and detects the dynamic information of the target ship by the laser scanner. The dynamic information of the target ship was smoothed by the Kalman one-dimensional model and was transmitted to the CADMS. The CADMS makes the collision avoidance decision by the Convention on the International Regulations for Preventing Collisions at Sea with the dynamic information and fuzzy logic theory. The decisions were then transmitted to the control system. The control system has two control logics which are fixed heading and variable heading. The fixed heading logic uses the fuzzy control steering system to calculate rudder angle to keep the ship sailing in a straight line. The variable heading logic adopts the first-order Nomoto equation of motion model with the coefficients calculated from the ship model turning cycle test. The rudder angle which makes the ship to reach the yaw rate was then determined by the CADMS. The scenario of the autonomous collision avoidance experiments is to make a target ship driving to an own ship which is sailed along a course and cause a collision crisis. Then, the own ship will turn to the collision avoidance course at the design speed until the collision crisis is averted. After the collision crisis, the own ship will return to its direction of original course.
The distance at closest point of approach was about 2.4 ship length in the head-on situation experiment, and the distance at closest point of approach was about 2.0 ship length in the crossing situation(left) experiment. The own ship can pass the target ship safely and return to original course from a collision crisis. The autonomous collision avoidance system built in this study is successful. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T06:00:52Z (GMT). No. of bitstreams: 1 ntu-108-R05525081-1.pdf: 7567519 bytes, checksum: c4c4b498d9da72b3fcd28fae2cdaa7a8 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 誌謝 I
摘要 II ABSTRACT III 目錄 V 圖目錄 VII 表目錄 XI 符號說明 XII 第一章 緒論 1 1-1 前言 1 1-2 文獻回顧 1 1-3 研究動機 3 1-4 研究方法與目的 3 1-5 論文結構 3 第二章 載具設備及實驗環境 5 2-1 載具介紹 5 2-2 設備介紹及校正流程 6 2-2-1 全球定位系統(Global Positioning System, GPS) 6 2-2-2 電子羅盤(Compass) 7 2-2-3 雷射掃描儀(Laser Range Finder) 7 2-2-4 雷射測距儀 8 2-3 實驗環境說明 8 第三章 系統與理論介紹 9 3-1 偵測系統 9 3-2 避碰決策系統 12 3-2-1 國際海上避碰規則 12 3-2-2 模糊避碰決策 14 3-2-3 操舵時間點 16 3-3 控制系統 20 3-3-1 定航向操縱 20 3-3-2 變航向操縱 24 第四章 試驗結果與討論 28 4-1 定航向操縱試驗 28 4-2 變航向操縱試驗 29 4-2-1 迴旋圈試驗(Turning Circle Test) 29 4-2-2 操縱性指數T、K值之迴歸分析 30 4-2-3 反舵角修正試驗 31 4-3 避碰實驗 32 4-3-1 避碰情況計算 32 4-3-2 避碰實驗結果 33 第五章 結論與建議 36 5-1 結論 36 5-2 建議 36 文獻參考 38 附圖 40 附表 81 | |
dc.language.iso | zh-TW | |
dc.title | 船舶自動避碰系統之實驗研究 | zh_TW |
dc.title | Experimental Study on the Ship Autonomous Collision Avoidance System | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 邱逢琛,黃正利,方志中,沈聖智 | |
dc.subject.keyword | 船舶自動避碰系統,卡爾曼模型,模糊理論,迴旋圈試驗,野本操縱運動式,船舶避碰實驗, | zh_TW |
dc.subject.keyword | Ship Autonomous Collision Avoidance System,Kalman Model,Fuzzy Logic Theory,Turning Cycle Test,First-order Nomoto Equation,Experiment On Ship Autonomous Collision Avoidance System, | en |
dc.relation.page | 84 | |
dc.identifier.doi | 10.6342/NTU201900413 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-02-12 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
顯示於系所單位: | 工程科學及海洋工程學系 |
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