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研究生: 徐培恩
Hsu, Pei-En
論文名稱: 融合雷射掃描及視覺資訊之TEB演算法應用於無人搬運車防碰撞策略開發
Collision Avoidance Strategy for AGV Using TEB Algorithm with Fusion of Laser Scan and Visual Data
指導教授: 林政宏
Lin, Cheng-Hung
蔣欣翰
Chiang, Hsin-Han
口試委員: 林政宏
Lin, Cheng-Hung
蔣欣翰
Chiang, Hsin-Han
陳瑄易
Chen, Syuan-Yi
口試日期: 2023/10/12
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: 2023
畢業學年度: 112
語文別: 中文
論文頁數: 95
中文關鍵詞: 無人搬運車空間障礙物件偵測雷射視覺融合路徑規劃防碰撞
英文關鍵詞: AGV, spatial object detection, laser-visual fusion, path planning, collision avoidance.
研究方法: 實驗設計法
DOI URL: http://doi.org/10.6345/NTNU202301775
論文種類: 學術論文
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  • 隨著無人搬運車(AGV)在倉儲、物流和製造業等領域的普及,使用機器人在運輸和操作物品方面的效率和安全性受到越來越多的關注。然而,當AGV操作場域複雜或不確定環境時,其運動控制和防碰撞設計仍然存在挑戰。為了實現避障,本論文採用Timed-Elastic-Band (TEB)演算法,在多個選擇路徑中選擇最佳路徑,並使用動態控制策略實現平順移動。此外,針對雷射掃描無法有效偵測之空間障礙物,本論文整合影像辨識來輔助TEB演算法的防碰撞策略,以增強無人搬運車進行導航任務的運動控制和避障能力。透過攝影機即時偵測AGV前方的環境影像,並利用機器學習技術識別空間障礙物的相對位置資訊,透過座標轉換將空間障礙物座標投影在代價地圖上,使TEB局部路徑規劃器可以將其納入計算避障路徑。本論文所開發的防撞策略先於ROS Stage模擬驗證後再將其實現於AGV平台進行實車驗證,透過融合影像偵測資訊與雷射掃描資訊的TEB避障演算法,經由實驗結果驗證能在導航過程中安全完成障礙物閃避。本論文採用之AGV平台及測試場域與業界緊密合作,顯示所提出防撞策略已成功整合於導航軟體架構與實際產業上之需求潛力。

    As automated guided vehicles (AGV) become more prevalent in fields such as warehousing, logistics, and manufacturing, their efficiency and safety in handling and transporting goods are becoming increasingly important. However, challenges in terms of motion control and collision avoidance capabilities still exist for AGV while operating in complex or uncertain environments. As such, to enhance the motion control and collision avoidance capabilities of AGV, this thesis proposes an effective strategy based on the Timed-Elastic-Band (TEB) algorithm with the aid of visual recognition. To avoid possible collision, the TEB algorithm is to choose the optimal path from multiple candidate paths and then the dynamic control can be utilized for smooth motion control of AGV. In addition to the obstacles detected from 2D laser scanning, the proposed strategy uses a camera to capture the front environment of the AGV and uses machine learning techniques to recognize the camera coordinate of the spatial obstacle in real time. Thus, such a spatial obstacle can be projected onto a cost map for the TEB path planner to determine the collision-free path. With consideration of the kinematics and dynamics constraints of our AGV, the collision avoidance strategy presented in this study is firstly verified in Ros Stage simulation. Further, the experimental evaluation for automatic navigation process combined with global path planning and TEB local path planner is carried out. Through the TEB obstacle avoidance algorithm fusing visual information and laser scanning information, experimental results verified that obstacles can be safely avoided during the navigation process. The AGV platform and test field used in this thesis are in close cooperation with the industry, showing that the proposed collision avoidance strategy has feasible potential integrated into the navigation software architecture and the demand of actual industry.

    誌謝 i 摘要 ii ABSTRACT iii 目錄 v 表目錄 viii 圖目錄 x 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機與目的 2 1.3 文獻回顧 2 1.3.1 動態閃避策略 3 1.3.2 障礙物偵測 12 1.4 論文架構 16 第二章 無人搬運車的硬體與軟體架構 17 2.1 無人搬運車機構 17 2.2 運算核心 18 2.3 可視化介面 19 2.4 電力系統 19 2.4.1 電池規格 19 2.4.2 充電設備 20 2.5 馬達系統 21 2.6 感測器系統 22 2.6.1 雷射測距儀 22 2.6.2 RGB攝影機 24 2.7 其他 24 2.8 ROS機器人作業系統 25 2.8.1 ROS基本概念 25 2.8.2 ROS使用版本 27 2.9 無人搬運車系統架構 27 2.9.1 模組架構設計 27 2.9.2 ROS節點架構&消息列表 30 2.9.3 Gmapping 建置地圖 33 2.9.4 路徑規劃 33 2.9.5 AMCL 定位 34 2.9.6 路徑追蹤 - Path Tracking Package 35 2.9.7 路徑追蹤 - Move Base Package 36 第三章 避障功能設計 38 3.1 代價地圖(Costmap) 38 3.2 TEB演算法介紹 39 3.3 動態避障參數調整 41 3.3.1 move base 參數 41 3.3.2 common costmap 參數 42 3.3.3 local costmap 參數 43 3.3.4 global costmap 參數 44 3.3.5 base global planner 參數 45 3.3.6 teb local planner 參數 46 3.4 影像輔助動態避障策略 48 3.4.1 Mediapipe 3D Objectron 48 3.4.2 多輸出回歸模型 51 3.4.3 視覺輔助動態避障之整合 52 第四章 無影像輔助避障功能模擬與實驗結果 54 4.1 ROS Stage 54 4.1.1 ROS Stage--world 54 4.1.2 ROS Stage--sensor 55 4.1.3 ROS Stage--robot 55 4.2 使用ROS Stage模擬TEB算法的效果 56 4.3 TEB算法在真實環境之效果 58 4.4 TEB算法在任務執行之效果 61 4.4.1 使用者介面 62 4.4.2 Task Manager 64 第五章 視覺輔助與避障整合實驗結果 75 5.1 實驗一: 純使用雷射測距儀資訊之避障策略 75 5.2 實驗二: 純使用影像資訊之避障策略 77 5.3 實驗三: 雷射測距儀資訊與影像資訊整合之避障策略 80 5.4 實驗一與實驗二之局部路徑分析 82 5.5 實驗三之局部路徑分析 84 5.5.1 路徑解析度 84 5.5.2 直線速度與角速度組合 85 5.6 實驗四: 障礙物分開擺放之避障策略測試 87 第六章 結論與未來展望 91 6.1 結論 91 6.2 未來展望 91 參考資料 92 自傳 94 學術成就 95

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