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研究生: 王思涵
Wang, Sih-Han
論文名稱: 可適應無人搬運車彈性化設計之學習式導航策略及強健式路徑跟隨控制
Adaptation Design of Learning-Based Navigation Maneuver and Robust Path Following Control for Flexible Automated Guided Vehicles
指導教授: 蔣欣翰
Chiang, Hsin-Han
口試委員: 林志哲
Lin, Chih-Che
李慶鴻
Lee, Ching-Hung
王偉彥
Wang, Wei-Yen
蔣欣翰
Chiang, Hsin-Han
口試日期: 2022/01/06
學位類別: 碩士
Master
系所名稱: 電機工程學系
Department of Electrical Engineering
論文出版年: 2022
畢業學年度: 110
語文別: 中文
論文頁數: 115
中文關鍵詞: 無人搬運車機器人作業系統基於光達之同步定位與地圖建置導航策略反光柱輔助定位模糊類神經網路
英文關鍵詞: Automated guided vehicles, robot operating system, Lidar SLAM, navigation maneuver, reflector-assisted indoor localization, fuzzy neural network
研究方法: 實驗設計法
DOI URL: http://doi.org/10.6345/NTNU202200892
論文種類: 學術論文
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  • 現今無人搬運車(Automated Guided Vehicle,AGV)引入製造工廠和自動化倉儲是邁向工業4.0的必備條件,由於實際工廠生產線環境中高度動態與不確定性,本論文開發一套強化AGV定位精確性與導航策略。首先提出具有低成本效益之反光柱輔助定位技術,利用反光點作為環境中的分離特徵進行重新定位,能有效改善自適應蒙地卡羅定位定位(Adaptive Monte Carlo Localization, AMCL) 演算法在環境特徵不明顯或環境地圖邊界過於破碎,所導致的迷航或定位失效的問題。接著,本論文提出可適應AGV動作的路徑跟隨控制設計,並整合至機器人作業系統(Robot Operating System, ROS)的軟體環境,此種設計除了可延伸應用於相關自主式無人搬運車軌跡追蹤控制策略之外,基於模糊神經網路架構並提出新的誤差計算方式,可以在模擬環境搭配AGV運動模型來預先進行控制參數自動調整。本論文開發的AGV導航控制先使用MATLAB模擬環境來實現所提出的用於導航控制的模糊神經網絡(Fuzzy Neural Network, FNN)策略,對軌跡跟踪中的模擬結果評估,以驗證所提出的AGV控制策略的有效性。由實驗測試結果說明,本論文提出的反光柱輔助定位搭配AMCL定位演算法能有效克服累積定位誤差之外,進一步整合強健式路徑跟隨控制與學習式導航策略,能展現本論文所開發AGV技術在實際工廠生產線環境中之高度應用價值。

    Nowadays, automated guided vehicles (AGVs) have become a bridge for manufacturing factories and warehousing industries to enter Industry 4.0. This study focuses on the development of sophisticated localization algorithms and navigation algorithms based on AGVs. First, a reflector-assisted localization method is proposed, which is different from the Adaptive Monte Carlo Localization (AMCL) algorithm used by most mobile robots. With the installed reflectors, this method improves the problem of AMCL localization when the environment features are not obvious or the boundaries of map are broken, which may lead to the problem of getting lost its position or localization failure. The current position of the AGV is calculated and localized using reflection points as the second feature of the environment, especially while the AMCL localization fails and the supported localization information from the reflectors can be supplemented in time. In addition, a path following algorithm using intelligent control design is proposed and integrated into the robot operating system (ROS). Based on the fuzzy neural network (FNN) approach, such a proposed intelligent controller can be used in the pre-built virtual environments and the required parameters of FNN can then be determined. The developed control system uses a MATLAB simulation environment to implement the proposed FNN approach in the navigation task of AGVs. The simulation results with the real experiments are evaluated to verify the effectiveness of the proposed navigation and control strategy. To sum up, the demonstration results depict our approach, including the reflector-assisted localization method and path-following control strategy, with the high potential for the production lines of factories.

    誌 謝 i 摘 要 ii ABSTRACT iii 目 錄 v 表 目 錄 viii 圖 目 錄 ix 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機與目的 3 1.3 文獻回顧 3 1.3.1 無人搬運車(AGV) 3 1.3.2 輔助定位法 7 1.3.3 導航演算法 22 1.3.4 模糊神經網路 24 1.4 論文架構 29 第二章 無人搬運車架構及設備 30 2.1 無人搬運車機構 30 2.2 運算核心 31 2.3 觸控螢幕 32 2.4 電力系統 33 2.4.1 電池規格 33 2.4.2 充電設備 34 2.5 馬達系統 35 2.6 感測器系統 36 2.7 其它 37 第三章 軟體架構設計 39 3.1 ROS機器人作業系統 39 3.1.1 ROS基本概念 39 3.1.2 ROS使用版本 40 3.2 無人搬運車系統架構 41 3.2.1 模組架構設計 41 3.2.2 ROS節點架構&消息列表 43 第四章 定位功能設計 46 4.1 環境建圖 46 4.1.1 GMapping演算法 46 4.1.2 Cartographer演算法 49 4.2 AMCL定位演算法 51 4.3 反光柱定位演算法 55 4.3.1 反光柱資料集和資料庫建立 55 4.3.2 反光柱資料庫匹配 58 4.3.3 旋轉矩陣與梯度下降法 59 第五章 導航控制設計 64 5.1 無人搬運車運動模型 64 5.2 遙控與路徑規劃 67 5.2.1 遙控模組 67 5.2.2 路徑規劃設計 68 5.3 模糊神經網路跟隨控制器設計 69 5.3.1 跟隨控制器介紹 69 5.3.2 模糊神經網路介紹 71 5.3.3 學習函數推導 73 5.3.4 B-spline歸屬函數介紹 75 第六章 實驗結果 80 6.1 實驗環境介紹 80 6.2 反光柱輔助定位測試與效能分析 81 6.3 模擬環境訓練模擬神經網路的參數 88 6.4 路徑跟隨實車測試 93 6.5 無人搬運車評估指標 97 6.6 廠區實測功能 104 第七章 結論與未來展望 112 7.1 結論 112 7.2 未來展望 112 參考資料 113

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