應用於雙雷達移動機器人之自動探索建圖系統

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研究生：	涂健軒 Tu, Jian-Syuan
論文名稱：	應用於雙雷達移動機器人之自動探索建圖系統 An Automatic Exploration and Mapping System Applied to a Mobile Robot with Dual Lidars
指導教授：	馬席彬 Ma, Hsi-Pin
口試委員:	黃稚存 Huang, Chih-Tsun 黃元豪 Huang, Yuan-Hao 楊家驤 Yang, Chia-Hsiang
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 電機工程學系 Department of Electrical Engineering
論文出版年：	2022
畢業學年度：	111
語文別：	中文
論文頁數：	53
中文關鍵詞：	機器人作業系統、雙雷達、主動式同時定位與地圖構建、移動機器人、導航、路徑規劃、佔據柵格地圖、麥克納姆輪
外文關鍵詞：	Robot Operating System (ROS), Dual Lidar, Active Simultaneous Localization and Mapping (SLAM), Mobile Robot, Navigation, Path Planning, Occupancy Grid Map, Mecanum Wheel
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在當今ROS 機器人探索的研究當中，自動導航是一個熱門的研究議題，而在實行導航功能之前，機器人必須在未知的環境當中，能夠模擬定位並建立建圖，對於地圖建立有兩種主要的方法，一種是使用手動控制機器人建圖，另一種為加入導航功能包，在RViz 可視化界面下，設定導航路徑讓機器人沿著規劃路徑建起地圖，而兩種方法都屬於需要人力的方法。
在本篇論文中，以使用全向輪的ROS 移動機器人為基礎，搭配兩個掃瞄範圍為270° 的雷達與里程計實行Gmapping 演算法建圖，再透過邊界探索的技術結合ROS 官方導航系統架構，實現A* 與動態窗口的軌跡移動演算法(Dynamic Window Approach，DWA)，完成了不需要花費人力的自動探索建圖系統，整個系統切分成建圖、目標選擇以及探索運動三個階段，每個階段有可以因應場域選擇較有優勢的演算法，最後實驗的地圖結果會根據最近平均距離(Average Distance to the Nearest Neighbor，ADNN) 比較公式作比較，該方法是以基準地圖比較自動探索建出的地圖，最終本論文的最佳ADNN 為5.75 公分，與其他論文以Gmapping 獲得2.65 公分到6.11 公分的成果比較，是介於中間的準確度表現，能建出良好準確度的地圖。
本篇論文的研究也實際應用在工廠的場域當中，其中關於地圖特徵區的劃分，如充電樁、置物架子與木箱等等障礙物，在實行演算法之前，已明確定義各個特徵區的規格，最後製成地圖中也能清楚的匡列工廠內所需標明的特徵區與實際場域相符。

In this paper, we use mobile robot with dual 270° lidars and mecanum wheels in robot operating system (ROS) to construct an autonomous exploration and mapping system. There are three main stages in the system: Mapping, Goal Selection and Exploration.
We use the Gmapping algorithm to locate the robot’s pose and build the 2D map of environment, and combine the frontier detector and ROS navigation stack with A* and dynamic window approach (DWA) algorithm to be the global planner and local planner.
To confirm the system performance, we test the system in a 11 x 10 m2 factory environment. There are chargers, shelves and boxes as obstacles and map features in the experimental environment. Our verification method is to use a ground truth map compare with the map which the autonomous exploration system build and calculate their average distance to the nearest neighbor (ADNN) which represent
the map errors.
According to the experiment result, the map build from our system can get ADNN value of 5.75 cm. Compared to other research that can get 2.65 cm to 6.11 cm ADNN values with Gmapping algorithm. Our system can build a whole map with good accuracy. We can also clearly identify the location of the feature area of factory on the visual map.

摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I
誌謝. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V
第一章緒論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1 研究背景. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2 研究動機. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
3 論文大綱. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
第二章文獻回顧. . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1 機器人作業系統. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.1 ROS 與ROS2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2 ROS 常用工具. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 ROS 移動機器人系統模塊. . . . . . . . . . . . . . . . . . . . . . . 7
1.4 移動機器人使用的輪子. . . . . . . . . . . . . . . . . . . . . . . . . 8
2 主動式同時定位與地圖構建. . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.1 ROS 地圖格式. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2 佔據柵格地圖. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3 應用主動式SLAM 的產品. . . . . . . . . . . . . . . . . . . . . . . 11
3 二維雷達SLAM 演算法比較. . . . . . . . . . . . . . . . . . . . . . . . . . 12
4 目標選擇演算法比較. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.1 基於邊界探索法. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.2 快速隨機探索樹. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
5 路徑規劃演算法比較. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
5.1 全局規劃演算法. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
5.2 局部規劃演算法. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
6 本章小節. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
第三章自動探索建圖系統. . . . . . . . . . . . . . . . . . . . . . . . 19
1 系統功能分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2 平台資訊. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3 總體系統架構與功能模塊. . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.1 雷達資料前處理. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.2 Gmapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.3 邊界探索. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.4 導航模塊. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4 本章小結. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
第四章實驗設計與結果分析. . . . . . . . . . . . . . . . . . . . . . . 33
1 場域配置與物件規格. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2 地圖切割與比較方法. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3 評測效能方法. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.1 評估標準與目標. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.2 實驗中欲討論的參數. . . . . . . . . . . . . . . . . . . . . . . . . . 37
4 實驗成果與比較. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.1 不同參數之地圖比較. . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.2 地圖複雜度比較. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.3 時間花費比較. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
5 結果討論與分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
5.1 地圖比較討論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
5.2 誤差原因分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
5.3 時間比較討論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
6 改善方法分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
7 本章小結. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
第五章結論與未來展望. . . . . . . . . . . . . . . . . . . . . . . . . 49
1 結論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
2 未來應用. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3 未來工作. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
參考文獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
                                

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