混凝土缺陷自動修補機器人之研發

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姓名

楊鉑洪(Bo-Hong Yang) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

混凝土缺陷自動修補機器人之研發
(Research and development of automatic repairing robot for concrete defects)

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至系統瀏覽論文 (2027-8-1以後開放)

摘要(中)

現今的噴射混凝土的噴塗工程，包含建築牆面、隧道工程的噴塗作業，大部分都是由人力完成，在作業的過程中會因為噪音、粉塵、嚴峻的施工環境等對施工人員造成不可逆的傷害；建築物以及隧道工程亦有檢修年限的問題，依舊需要人力檢修以及補強。
　　本研究預計研發一款混凝土缺陷自動修補機器人，此機器人經由微控制器(Micro Control Unit ,MCU)控制各機構的行為，在手機或電腦端以無線網路(Wi-Fi)連接微控制器進行遠端控制，互動介面則是客製化網頁，使用HTML以及JavaScript編寫溝通伺服器端以及客戶端；在噴射混凝土噴槍上方搭載視覺辨識模組，基於機器學習 (Machine Learning)以及影像辨識(Visual Recognition)技術蒐集圖片進行訓練替代人力檢測，視覺辨識訓練使用卷積神經網路(Convolutional Neural Network, CNN)演算法，多次調整訓練模型訓練後正確率達到94.2%、隨機圖片預測正確率97.28%，F1-Score在訓練模型達到0.9、隨機圖片預測達到0.96，並設計演算法完成牆面自動掃描，微控制器配合視覺辨視模組回報牆面缺陷狀況，當缺陷被辨識到，微控制器將遠端控制噴射混凝土幫浦控制盒的微控制器扳動開關，即可自動化完成缺陷自動噴塗修補作業。
　　本研究整合機器人自動控制、噴射混凝土、視覺辨識系統、物聯網系統以及網頁遠端控制，經由實驗證實可以自動化完成缺陷辨識以及噴射混凝土的噴塗作業，開發出未來自動化缺陷辨識補強機器人的雛形。

摘要(英)

Nowadays, shotcrete operation, including the spraying of building walls and tunnels, is done mainly by human resources. During the operation, it will cause irreversible damage to the construction personnel due to noise, dust, and a severe construction environment. Buildings and tunnel projects also have the problem of the maintenance period, which still requires manual maintenance and reinforcement.
This research is expected to develop an automatic repairing robot for concrete defects. This robot controls the behavior of each device through a microcontroller and connects the microcontroller with a wireless network (Wi-Fi) on the mobile phone or computer. Remote control, the user interface is a customized web page using HTML and JavaScript to contact the communication server and client. A visual recognition module is carried on the shotcrete spray gun, based on Machine Learning and Visual Recognition technology to collect pictures for visual recognition to replace manual work. Visual recognition training uses Convolutional Neural Network (CNN) algorithm. Then designed, an algorithm automatically scans the target wall, and the microcontroller cooperates with the visual recognition module to report the defect status of the wall. When the defect is identified, the microcontroller will remotely control the microcontroller of the shotcrete pump control box and flip the switch to automate complete the defect automatic spray repair operation. After adjusting the training model many times, the accuracy rate reached 94.2%, the random image prediction accuracy rate was 97.28%, and the F1-Score reached 0.9 in the training model and 0.96 in random image prediction.
This research develops the prototype of the future automated defect identification reinforcement robot. This research integrates automatic robot control, shotcrete, visual recognition system, Internet of Things system, and web remote control. It has been confirmed through experiments that defect identification and shotcrete spraying can be automatically completed.

關鍵字(中)

★ 噴射混凝土
★ 機器學習
★ 視覺辨識
★ 物聯網應用
★ 機器人
★ 自動控制

關鍵字(英)

★ shotcrete
★ machine learning
★ visual recognition
★ IoT applications
★ robotics
★ automatic control

論文目次

摘要 vii
Abstract viii
致謝 x
目錄 xi
表目錄 xiii
圖目錄 xiii
一、緒論 1
1-1研究背景及動機 1
1-2研究目的 2
1-3論文架構 2
二、文獻回顧 3
三、研究方法 7
3-1 混凝土缺陷自動修補機器人系統架構 7
3-2機器人架構設計與實作 9
3-2-1混凝土缺陷自動修補機器人設計概念 9
3-2-2混凝土缺陷自動修補機器人機構設計 10
3-2-3噴射混凝土供料系統 19
3-2-4控制系統與機器視覺系統設計 23
3-3軟體設計與實作 30
3-3-1機器人遠端通訊 30
3-3-2機器人自動控制 34
3-4基於機器視覺辨識混凝土缺陷辨識方法 35
3-4-1訓練資料蒐集以及分類 35
3-4-2 視覺訓練模型 39
3-4-3視覺模型成果檢測 43
3-5演算法規畫以及虛擬碼(Pseudo Code) 49
四、實驗設計及規劃 53
4-1牆面設計與建置 53
4-2牆面噴塗實驗 54
4-3混凝土缺陷自動修補實驗 60
五、實驗結果與討論 62
5-1自動噴塗實驗 62
5-2 視覺辨識現地測試 66
5-3混凝土缺陷自動修補實驗成果 71
六、結論以及未來展望 79
6-1 結論 79
6-2未來展望 79
七、參考文獻 80

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指導教授

林子軒(Tzu-Hsuan Lin)

審核日期

2022-9-20

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