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 路徑規劃在自動車技術領域中佔據重要的地位，而光達（Lidar）因其對於距離量測的準確性及能夠在低光度的環境中正常工作在近年來逐漸被應用其中，其相關技術同樣可以被使用在移動機器人的路徑規劃上。本文以2-D Lidar為主要研究對象，有別於3-D Lidar，2-D Lidar僅提供了對周圍環境的二維探測數據，大大減少了數據處理時間，但相對的對於環境的資訊也少了許多，而如何利用此有限的數據完成路徑規劃便是此次實驗的主要目標。本文分別提出基於2-D Lidar建立地圖環境的方法及套用輪盤式選擇的路徑規劃演算法，整體動作流程從數據處理開始，使用中值過濾法對原始點雲數據進行過濾並藉由座標轉換將其結果呈現在直角座標平面上，之後透過域值劃分法劃分出屬於各個障礙物的點雲群並塑形，完成地圖模型。接著執行路徑規劃演算，最後將演算結果再進行優化產生最終路徑。本文所擬定之路徑規劃演算法將在Google Colaboratory上進行模擬，驗證其可行性，最終將其套用在所規劃的全方位輪式移動機器人上完成實際的移動測試。
 Obstacle avoidance and path planning play an important role in technology field of autonomous vehicles, and the Lidar has been applied to them in recent years because of its accuracy on the measurement of distance and working normally in the low-light environment, some of techniques in it can also be applied to the path planning for the mobile robot. In this thesis, the 2-D Lidar is the main object of study, different from 3-D Lidar, the 2-D Lidar can only supply two-dimensional information about the surrounding, it can help decreasing the time of data processing however the information about the surrounding is also decreased. Therefore, how to use those data to accomplish the path planning is the propose of this study. In this thesis, a way to build the map based on a 2-D Lidar and an algorithm for the path planning using roulette wheel selection method are proposed. The first step of them is data processing, the data decreasing the noise by median filter method is shown on Cartesian coordinate plane. Second, the threshold value method is used for point cloud segmentation, and the obstacles are modeled from the groups. The last, start path planning and then have path optimization. The simulation result of path planning algorithm is verified on Google Colaboratory, and then be applied to the proposed mobile robot. Finally, we successfully proved that the algorithm for the omnidirectional-wheeled mobile robot is feasible.
 誌　謝 i摘　要 iiAbstract iii目　錄 iv圖目錄 vii表目錄 x第一章 緒論 11.1 前言 11.2 文獻回顧 1第二章 硬體配置 42.1 直流減速馬達HN-35GBD-1345T 52.2 麥克納姆輪 62.3 YUASA鉛酸電池 62.4 馬達驅動模組L298N 72.5 USB降壓模組 82.6 GY-273三軸電子羅盤模組 82.7 光學雷達RPLidar A1M8 92.8 Raspberry Pi 4 11第三章 麥克納姆輪運動學分析 12第四章 系統架構 174.1 地圖定位模塊 174.2 路徑規劃模塊 174.3 動作控制模塊 184.4 演算流程圖 21第五章 路徑規劃演算 225.1 點雲過濾 225.2 點雲圖繪製 235.3 點雲分割 235.4 點雲形狀構建 245.5 路徑規劃 265.5.1 目的地選擇 275.5.2 碰撞檢測 285.6 路徑優化 29第六章 模擬測試 306.1 模擬環境 306.2 場地佈置 306.3 數據處理及障礙物定義 306.4 路徑規劃及優化演算 32第七章 實際測試 377.1 測試方法 377.2 實驗一 377.2.1 場地布置 377.2.2 參數設定及實驗結果 387.3 實驗二 417.3.1 場地布置 417.3.2 參數設定及實驗結果 427.4 實驗結果探討 45第八章 結論與未來展望 478.1 結論 478.2 未來展望 47
 [1]Y. Peng, D. Qu, Y. Zhong, S. Xie, J. Luo, and J. Gu, “The Obstacle Detection and Obstacle Avoidance Algorithm Based on 2-D Lidar,” in 2015 IEEE International Conference on Information and Automation, pp. 1648–1653, Aug. 2015.[2]P. Denysyuk, V. Teslyuk, and I. Chorna, “Development of Mobile Robot Using LIDAR Technology Based on Arduino Controller,” in 2018 XIV-th International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH), pp. 240–244, Apr. 2018.[3]Z. Ma, O. Postolache, and Y. Yang, “Obstacle Avoidance for Unmanned Vehicle based on a 2D LIDAR,” in 2019 International Conference on Sensing and Instrumentation in IoT Era (ISSI), pp. 1–6, Aug. 2019.[4]S. T. Padgett and A. F. Browne, “Vector-Based Robot Obstacle Avoidance Using LIDAR and Mecanum Drive,” in SoutheastCon 2017, pp. 1–5, Apr. 2017.[5]L. Kurnianggoro and K. Jo, “Object Classification for LIDAR Data Using Encoded Features,” in 2017 10th International Conference on Human System Interactions (HSI), pp. 49–53, Jul. 2017.[6]D. Lee, J. Jeong, Y. H. Kim, and J. B. Park, “An Improved Artificial Potential Field Method with a New Point of Attractive Force for a Mobile Robot,” in 2017 2nd International Conference on Robotics and Automation Engineering (ICRAE), pp. 63–67, Dec. 2017.[7]Q. Su, W. Yu, and J. Liu, “Mobile Robot Path Planning Based on Improved Ant Colony Algorithm,” in 2021 Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS), pp. 220–224, Jan. 2021.[8]N. ALPKIRAY, Y. TORUN, and O. KAYNAR, “Probabilistic Roadmap and Artificial Bee Colony Algorithm Cooperation for Path Planning,” in 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), pp. 1–6, Sep. 2018.[9]J. Bai, S. Lian, Z. Liu, K. Wang, and D. Liu, “Deep Learning Based Robot for Automatically Picking Up Garbage on the Grass,” IEEE Transactions on Consumer Electronics, vol. 64, no. 3, pp. 382–389, Aug. 2018.[10]A. Alyasin, E. I. Abbas, and S. D. Hasan, “An Efficient Optimal Path Finding for Mobile Robot Based on Dijkstra Method,” in 2019 4th Scientific International Conference Najaf (SICN), pp. 11–14, Apr. 2019.[11]J. Zhou et al., “Improved Path Planning for Mobile Robot Based on Firefly Algorithm,” in 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 2885–2889, Dec. 2019.[12]J. Ou and M. Wang, “Path Planning for Omnidirectional Wheeled Mobile Robot by Improved Ant Colony Optimization,” in 2019 Chinese Control Conference (CCC), pp. 2668–2673, Jul. 2019.[13]F. Yu, X. Fu, H. Li, and G. Dong, “Improved Roulette Wheel Selection-Based Genetic Algorithm for TSP,” in 2016 International Conference on Network and Information Systems for Computers (ICNISC), pp. 151–154, Apr. 2016.[14]X. Zhang and W. Li, “Feature Extraction Method of Indoor Structured Environment Based on Two-Dimensional LiDAR,” in 2019 3rd International Conference on Robotics and Automation Sciences (ICRAS), pp. 69–73, Jun. 2019.[15]T. R. Madhavan and M. Adharsh, “Obstacle Detection and Obstacle Avoidance Algorithm based on 2-D RPLiDAR,” in 2019 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–4, Jan. 2019.[16]O. Yalcin, A. Sayar, O. F. Arar, S. Apinar, and S. Kosunalp, “Detection of Road Boundaries and Obstacles Using LIDAR,” in 2014 6th Computer Science and Electronic Engineering Conference (CEEC), pp. 6–10, Sep. 2014.
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