帳單類別信件的自動辨識與分揀系統

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姓名

陳昱齊(Yu-Ci Chen) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

帳單類別信件的自動辨識與分揀系統

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摘要(中)

本論文旨在設計一個針對帳單類別信件的自動郵件分揀系統，使用者將多封郵件不分正反面直接放入個別郵件發放裝置，此系統可讓該裝置逐封送出至郵件載台，再透過影像處理定位及辨識該封信件之郵遞區號，隨後使用六自由度機械手臂夾取信件送至對應的郵件分類櫃存放。
本論文之研究項目如下，在硬體部分，透過繪圖軟體及雷射切割機設計並產出零組件而完成以下三點：(1)能逐一發送信封並可暫存多封郵件的個別郵件發放裝置，(2)可供辨識之郵件載台，(3)郵件分類櫃。在郵遞區號辨識演算法的部分，透過郵件載台上下方的兩部網路攝影機完成以下六點：(1)辨識信封正反面，(2)辨識信封傾斜角度並校正，(3)定位及裁切郵遞資訊框，(4)中文字、數字字元分類，(5)利用深度學習網路辨識數字，(6)提取郵遞區號之演算法。另外在機械手臂的運動控制方面，完成以下程序。(1)建置虛擬環境，(2)計算機器手臂運作模型的轉換矩陣，(3)求得從夾取郵件至郵件分類櫃前的各點座標，並以逆運動學控制機械手臂至分類櫃存放信封，(4)最佳化逆運動學移動路徑。綜合上述條件，便可讓系統完成郵遞區號辨識與機械手臂可完成郵件分揀的兩個任務。
本研究在Linux環境下使用機器人作業系統(Robot Operating System, ROS)開發軟體系統，並利用TCP/IP進行資訊傳遞，使多個裝置可進行即時資料傳遞，以實現軟硬體整合協作的設計。本論文在實際實驗中，郵遞區號辨識演算法的實際實驗辨識正確率為95.24%，整體系統的正確分揀率為94.49%，實驗結果顯示本論文確實能成功建立了一套穩定且具有強健性的帳單類別信件自動分揀系統，有助於區域性郵局的日常郵遞業務。

摘要(英)

This thesis aims to design an automatic mail sorting system for bill-type letters. This system allows users to place multiple letters into a mail dispensing device, which then sequentially dispatches them to a mail platform. Through image processing, the system recognizes each letter′s postal code. It utilizes a 6-DoF robotic arm to transform the letters into corresponding mail-sorting cabinets.
The research objectives of this thesis are as follows: In hardware, designing structure diagrams and manufacture them by a laser cutting machine, the following three issues have been achieved: (1) Mail dispensing device capable of sequentially sending envelope and storing multiple letters, (2) Mail platform for recognition, and (3) Mail sorting cabinet. In the postal code recognition algorithm section, utilizing two web cameras above and below the mail platform, the following six topics were achieved: (1) Identify the front/back side of the bill-type envelope, (2)Recognize and correct the tilt angle of the envelope, (3) Locate and crop the postal information box, (4) Classifying Chinese and numeric characters, (5) Use deep learning network to recognize digits, and (6) Extract the postal code algorithm. Additionally, in the motion control of the robotic arm, the following techniques were completed: (1) Establish a virtual environment, (2) Compute the transformation matrix of the robotic arm operation model, (3) Obtain the coordinates of each point from picking up the mail to storing it in the mail sorting cabinet by inverse kinematic control of the robotic arm, and (4) Optimize the inverse kinematic path-planning. Combining the above conditions allows the system to accomplish postal code recognition and mail sorting by the robotic arm.
This study develops a software system using the Robot Operating System (ROS) in the Linux environment. It utilizes TCP/IP for information transmission, enabling real-time data exchange among multiple devices to achieve the design of software-hardware integration and collaboration. In practical experiments, the postal code recognition algorithm performed a correct recognition rate of 95.24%, and the overall sorting accuracy of the system was 94.49%. The experimental results demonstrate that this thesis has successfully established a stable and robust automatic sorting system for bill-type letters.

關鍵字(中)

★ 郵件分揀
★ 自動化
★ 影像辨識
★ 六自由度機械手臂
★ 運動學
★ ROS

關鍵字(英)

★ mail sorting
★ automation
★ image recognition
★ 6 DoF robotic arm
★ robot operating system
★ coordinate transformation

論文目次

摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vi
表目錄 ix
第一章緒論 1
1.1研究背景與動機 1
1.2文獻回顧 3
1.3論文目標 5
1.4論文架構 5
第二章系統架構與軟硬體介紹 6
2.1系統架構 6
2.2硬體裝置 6
2.2.1 個別郵件發放裝置 7
2.2.2 機械手臂端 11
2.2.3 郵件分類櫃 15
2.3系統通訊及軟體 15
2.3.1系統通訊 16
2.3.2 ROS簡介 16
2.3.3 Moveit套件介紹 20
第三章郵遞區號辨識演算法 22
3.1整體演算法流程與架構 22
3.2信封正反面辨識 23
3.3信封傾斜角度偵測與校正 25
3.4郵遞資訊框辨識演算法 26
3.4.1第一階段演算法(Contour Finding-based) 26
3.4.2第二階段演算法(Hough Line Transform-based) 28
3.5郵遞資訊框字元擷取與分類 29
3.6 ResNet-34數字辨識系統 31
3.6.1數字辨識訓練資料集 31
3.6.2數字辨識系統訓練結果與應用 34
3.7郵遞區號提取演算法 35
3.8整體流程總結 37
第四章機械手臂控制 39
4.1機械手臂運動學與其應用 39
4.1.1逆向運動學 39
4.1.2機械手臂姿態幾何描述方式 40
4.1.3達明機械手臂之TM5-700逆運動學 43
4.1.4路徑最佳化運作原理 49
4.2機器人作業系統的應用 49
4.3機械手臂之路徑規劃與任務執行 50
第五章實驗結果 52
5.1字元分類效果實驗 52
5.2郵遞區號辨識演算法實驗結果 53
5.3整體郵件分揀系統之實驗 55
第六章結論與未來展望 60
6.1結論 60
6.2未來展望 60
參考文獻 61

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指導教授

王文俊(Wen-June Wang)

審核日期

2024-8-8

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