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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/68936

    Title: 應用快速擴展隨機樹和人工魚群演算法及危險度於路徑規劃;Application of the RRT&AFS algorithms with danger degree in path planning
    Authors: 沈昱廷;Shen,Yu-Ting
    Contributors: 電機工程學系
    Keywords: 人工魚群演算法;自走車;危險度地圖;快速擴展隨機樹;路徑規劃;AFSA;Danger Degree Map;Rapidly-exploring Random Tree (RRT);Path Planning;Self-propelled Vehicle
    Date: 2015-08-17
    Issue Date: 2015-09-23 14:46:58 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 對於自走車來說,要如何從一個佈滿各種障礙物的環境中,找到一條合適的路徑,並確保自己能成功的從起始點到達目標點,且行經的路程要是最短的,是個值得研究的方向。
    ;In terms of self-propelled vehicles, how to find a suitable route from the environment filled with varied obstacles and how to ensure the target point from the starting point with the minimum passing away can be successfully reached is a direction worthy of research
    In this thesis, the new algorithm in a combination of Artificial Fish Swarm Algorithm (AFSA) and Rapidly-Exploring Random Tree (RRT) is proposed. Compared with the traditional AFSA, the difference of the new algorithm in path planning is it is by similar branch growing up to increase extension points, and after the increase points are compared with each other, the best extension point is chosen as the point of the next moving fish.
    The new algorithm improves the shortcomings of the later-period AFSA in slow convergence time and being hard to be converged in the optimal solution. In addition, the basic RRT algorithm is not so stable on each path planning, so in this paper, the simulation of the RRT algorithm is used to be included in AFSA, with the concept of danger degree map to effectively dodge obstacles.
    After testing, it is shown that the data of the new algorithm on path planning are better than those of either AFSA or RRT and the path is the shortest.
    Appears in Collections:[電機工程研究所] 博碩士論文

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