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研究生:陳威翔
研究生(外文):CHEN, WEI-HSIUNG
論文名稱:使用輪盤式選擇法及2-D Lidar之改進型避障及路徑優化演算法於全方位輪式移動機器人
論文名稱(外文):Improved Algorithm of Obstacle Avoidance and Path Optimization Using Roulette Wheel Selection Method and 2-D Lidar for an Omnidirectional-Wheeled Mobile Robot
指導教授:林南州
指導教授(外文):Nanjou Lin
口試委員:林仕亭陳孝武
口試委員(外文):Shih-Tin LinShiaw-Wu Chen
口試日期:2022-07-13
學位類別:碩士
校院名稱:逢甲大學
系所名稱:自動控制工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2022
畢業學年度:110
語文別:中文
論文頁數:51
中文關鍵詞:路徑規劃路徑最佳化障礙物迴避光達全方位輪式移動機器人輪盤式選擇
外文關鍵詞:Path PlanningPath OptimizationObstacle AvoidanceLidarOmnidirectional-Wheeled Mobile RobotRoulette Wheel Selection
相關次數:
  • 被引用被引用:0
  • 點閱點閱:191
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  • 下載下載:46
  • 收藏至我的研究室書目清單書目收藏:2
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 ii
Abstract iii
目 錄 iv
圖目錄 vii
表目錄 x
第一章 緒論 1
1.1 前言 1
1.2 文獻回顧 1
第二章 硬體配置 4
2.1 直流減速馬達HN-35GBD-1345T 5
2.2 麥克納姆輪 6
2.3 YUASA鉛酸電池 6
2.4 馬達驅動模組L298N 7
2.5 USB降壓模組 8
2.6 GY-273三軸電子羅盤模組 8
2.7 光學雷達RPLidar A1M8 9
2.8 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 17
4.1 地圖定位模塊 17
4.2 路徑規劃模塊 17
4.3 動作控制模塊 18
4.4 演算流程圖 21
第五章 路徑規劃演算 22
5.1 點雲過濾 22
5.2 點雲圖繪製 23
5.3 點雲分割 23
5.4 點雲形狀構建 24
5.5 路徑規劃 26
5.5.1 目的地選擇 27
5.5.2 碰撞檢測 28
5.6 路徑優化 29
第六章 模擬測試 30
6.1 模擬環境 30
6.2 場地佈置 30
6.3 數據處理及障礙物定義 30
6.4 路徑規劃及優化演算 32
第七章 實際測試 37
7.1 測試方法 37
7.2 實驗一 37
7.2.1 場地布置 37
7.2.2 參數設定及實驗結果 38
7.3 實驗二 41
7.3.1 場地布置 41
7.3.2 參數設定及實驗結果 42
7.4 實驗結果探討 45
第八章 結論與未來展望 47
8.1 結論 47
8.2 未來展望 47

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