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    政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 >  Item 140.119/29688
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/29688


    Title: 主幹導引式最短路徑搜尋演算法
    A Heuristics shortest path algorithm by backbone orientation
    Authors: 林啟榮
    Lin, Chi Jung
    Contributors: 連耀南
    Lien, Yao Nan
    林啟榮
    Lin, Chi Jung
    Keywords: 主幹導引
    路徑規劃
    最短路徑演算法
    導航系統
    backbone orientation
    route planning
    shortest path algorithm
    Navigation system
    Date: 2008
    Issue Date: 2009-09-11 16:03:53 (UTC+8)
    Abstract: A*(A-Star)演算法透過啟發函式,以減少路徑搜尋過程中所需要計算的網路數量,SMA*(Simplified Memory Bounded A-Star)為A*之變形,目前最廣泛被應用於GPS導航系統之路徑規劃的演算法。尋找路徑的計算過程中,A*與SMA*演算法利用中間節點與目的地的方向(直線距離)作為啟發函式,以預估中間節點到目的地之路徑長度挑選優先搜尋的路段,而SMA*則因記憶體的限制會排除預估長度較長的路段,以減少搜尋的路段數量與記憶體之使用量。當起點與終點中存在障礙地形時或路段較崎嶇時,以方向導引路徑搜尋之準確度便大幅降低,導致A*與SMA*之搜尋數量增加,SMA*甚至會得到較差的路徑。

    主幹導引式最短路徑搜尋演算法(Backbone Orientation)以骨幹路徑導引路徑之搜尋,在障礙地形或道路崎嶇的情況下,可有效避免SMA*之缺點,效能較佳。主幹導引式最短路徑搜尋演算法分為二階段,先由原始路網中提取骨幹路網,並計算出最佳骨幹路徑,再利用骨幹路徑引導路徑的搜尋,在骨幹路徑的一定範圍內搜尋最短路徑。

    本研究以台灣地區2007年之平面路網圖進行實驗,以三種不同的實驗方式進行實驗,以驗證主幹導引式最短路徑搜尋演算法之效能,證明在SMA*演算法之啟發函式效能低落時,使用主幹導引式最短路徑搜尋演算法可以有效的改善SMA*在障礙地形之效能不彰的問題。
    Reference: [1] G. Ausiello, P. Crescenzi, G. Gambosi, V. Kann, A. Marchetti-Spaccamela, and M. Protasi, “Complexity and Approximation”, Springer, ISBN-13 978-3540654315, 2003.
    [2] Richard Bellman, “On a Routing Problem”, Quarterly of Applied Mathematics, 16(1), 1958, p.p. 87-90.
    [3] Thomas H. Cormen, Charle E. Leiserson, Ronald L. Rivest and Clifford Stein, Introduction to Algorithms (Second Edition), ISBN 0-262-03293-7, 2001, p.p. 525-681.
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    [12] Judea Pearl, Heuristics, Addison-Wesley Pub. ISBN 0-20-105594-5, April-1984.
    [13] Stuart Russell and Peter Norvig,” Upper Saddle River, NJ: Prentice Hall”, Artificial Intelligence:A Modern Approach (2nd ed.) , ISBN 0-13-790395-2, 2003, p.p. 111-114.
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    [15] Anthony Stentz, “Optimal and Efficient Path Planning for Partially-Known Environments”, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA `94), May-1994, p.p. 3310-3317.
    [16] Anthony Stentz, “The Focussed D* Algorithm for Real-Time Replanning”, In Proceedings of the International Joint Conference on Artificial Intelligence, 1995.
    [17] Anthony Stentz, “Optimal and efficient path planning for partially-known environments”, In Proceedings of IEEE International Conference on Robotics and Automation, May-1994, ISBN 0-8186-5330-2, 8-13-May-1994, p.p. 3310-3317 vol.4.
    [18] Steven R. Strom, “Charting a Course Toward Global Navigation”, http://www.aerospace.org/publications/crosslink/summer2002/01.html, Retrive at 12-28-2008.
    [19] Nathan Sturtevant and Michael Buro, “Partial Pathfinding Using Map Abstraction and Refinement”, In Proceedings of AAAI, 2005, p.p. 47–52.
    [20] George Taylor, “Line Simplification Algorithm”, http://www.comp.glam.ac.uk/pages/staff/getaylor/papers/lcwin.PDF, 1995. Retrive at 01-21-2009.
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    [24] 吳泰熙, 張欽智, “以禁忌搜尋法則求解推銷員旅行問題”, Journal of Da-Yeh University, Vol.6, No.1, 1997, p.p. 87-99.
    [25] 張文贏, 蕭飛賓, 許棟龍, “多目標巡視之飛行路徑規劃研究”, 國立成功大學航太系-96年度博士畢業論文, 2007.
    [26] 賴建銘, 尹邦嚴, 徐熊健, “以禁制搜尋演算法建構最大概率條件演化樹”, 國立暨南大學資管所, 2003.
    [27] “Bellman-Ford algorithm”, http://en.wikipedia.org/wiki/Bellman-Ford_algorithm, Retrieved at 12-25-2008.
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    [29] “Depth-first search”, http://en.wikipedia.org/wiki/Depth-first_search, Retrieved at 12-25-2008.
    [30] “Dijkstra`s algorithm”, http://en.wikipedia.org/wiki/Dijkstra%27s_algorithm, Retrieved at 12-25-2008.
    [31] “Simulated annealing”, http://en.wikipedia.org/wiki/Simulated_annealing, Retrieved at 12-25-2008.
    Description: 碩士
    國立政治大學
    資訊科學學系
    94971011
    97
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0094971011
    Data Type: thesis
    Appears in Collections:[資訊科學系] 學位論文

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