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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/65802

    Title: 在室內環境中使用ALC-PSO演算法與危險度指標改良RRT路徑規劃;Using ALC-PSO Algorithm to Improve RRT Path Planning in Indoor Environments with Danger Degree
    Authors: 姜俊甫;Jiang,Jyun-Fu
    Contributors: 電機工程學系
    Keywords: 老齡化領導者與挑戰者粒子群演算法;危險度指標;快速搜尋隨機樹;路徑規劃;移動機器人;ALC-PSO;Danger Degree;Rapidly-exploring Random Tree;Path Planning;Mobile Robots
    Date: 2014-08-15
    Issue Date: 2014-10-15 17:10:41 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 在移動式機器人中,要如何在有障礙物的環境中,規劃出一條合適的無障礙物的路徑,讓移動機器人能從起始點移動到達目標點且確保路徑是最短的,是一個很重要議題。
    ;Path planning is an important issue in mobile robotics. In an environment with obstacles, path planning is to find a suitable collision-free path for a mobile robot to move from a start location to a target location along the shortest path.
    This paper proposes an optimal path planning algorithm for mobile robots based on Particle Swam Optimization with an Aging Leader and Challengers (ALC-PSO) to imitate Rapidly-exploring Random Tree (RRT), traditional Particle Swam Optimization (PSO) for path planning is different, In this paper, we propose a branches-grow method based on the ALC-PSO algorithm, and add extend point to particles after we compare.
    This method overcomes the drawback for particle swam optimization is easy to fall into local optimization in robotic path planning. Because the basic Rapidly-exploring Random Tree (RRT) path planning is unstable for every time, so this paper improved algorithm of ALC-PSO to imitate RRT in path planning, and add Danger Degree Map to avoid obstacles. From the results of simulations, we show that this algorithm can improve the stability of RRT path planning in static environment, and ensures that the path is almost optimal.
    Appears in Collections:[電機工程研究所] 博碩士論文

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