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    Please use this identifier to cite or link to this item: http://ccur.lib.ccu.edu.tw/handle/A095B0000Q/436

    Title: 基於快速探索隨機樹之自主移動機器人室內未知環境探索;Autonomous Mobile Robot Exploration in Unknown Indoor Environment Based on Rapidly-exploring Random Tree
    Authors: 吳承諺;WU, CHENG-YAN
    Contributors: 電機工程研究所
    Keywords: 快速搜索隨機樹;自主探索;即時定位與地圖建構;Autonomous exploration;SLAM;Rapidly-exploring random tree
    Date: 2018
    Issue Date: 2019-07-17
    Publisher: 電機工程研究所
    Abstract: 近年來機器人迅速地融入人類的日常生活中,為了讓機器人能夠自主導航,必須提供準確的地圖,因此為了讓機器人能夠自動獲得地圖以及提升自主探索的效率,本篇論文提出了一套基於RRT與邊界的2D-SLAM探索系統,系統架構可分為三個部分,首先以雷射資訊建構出初始地圖,使用RRT與邊界探測器檢測初始地圖的邊界點。在第二部分我們將得到的所有邊界點經過過濾與聚類,減少邊界點的數量降低計算的需求。最後計算每個邊界點的效益,引導機器人前往未知區域,直到地圖建構完成。在實驗結果中,我們分別測試機器人在虛擬與實際環境中自主探索未知的室內場景,並評估了我們的系統在各種虛擬和實際的室內環境中的性能,實驗結果顯示,我們的系統能夠成功地找到未知區域,並且在合理的時間內完成自主探索。
    In recent years, robots have quickly integrated into the daily life of people. In order to allow robots to navigate autonomously, accurate maps must be provided. Therefore, in order to enable robots to automatically obtain maps and improve the efficiency of autonomous exploration. This paper proposes a method based on RRT and frontier 2D-SLAM exploration system. The system architecture can be divided into three parts, firstly constructing the initial map with laser information. Using RRT and frontier detector to detect the boundary point of the initial map. In the second part, we will filter and cluster all the boundary points to reduce the number of boundary points and the computational. Finally, calculate the score of each boundary point and guide the robot to the unknown areas until the map is constructed. In the experimental results, we separately test the robots to explore unknown indoor environments in simulation and real environments. We evaluate the performance of our system in various simulation and real indoor environments. The experimental results show that our system can successfully find unknown areas and complete autonomous exploration in a reasonable time.
    Appears in Collections:[電機工程研究所] 學位論文

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