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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 李志中(Jyh-Jone Lee) | |
dc.contributor.author | Jian-Lun Chen | en |
dc.contributor.author | 陳健倫 | zh_TW |
dc.date.accessioned | 2021-05-20T00:57:21Z | - |
dc.date.available | 2025-02-01 | |
dc.date.available | 2021-05-20T00:57:21Z | - |
dc.date.copyright | 2021-02-20 | |
dc.date.issued | 2021 | |
dc.date.submitted | 2021-01-28 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/8554 | - |
dc.description.abstract | 本文提出一套改善的隨機堆疊物件分類之夾取流程,藉由物件切割、姿態估測以及夾取點判定,完成在隨機堆疊場景當中夾取與擺放物件的任務。整個流程首先使用深度感知相機獲得隨機堆疊場景,接著將平面影像搭配物件切割系統把場景中的目標物件逐一取出,將物件從堆疊場景取出後便放入姿態估測模型做姿態的預測,同時找尋該姿態對應的夾取資訊,最後搭配深度影像經過夾取點的判定完成本文的夾取流程。 本文提出並改善的地方分別為針對目標物件以虛擬相機自動獲得物件各個視角的照片成為姿態資料集,節省對現實資料處理的時間成本;以領域隨機化(Domain Randomization)的方式訓練自動編碼網路(Autoencoder)成為增強式自動編碼網路(Augmented Autoencoder),避免虛擬與現實環境產生的領域差異(Domain Gap)並作為姿態估測系統;將對應的姿態經過修正的夾取經驗轉移預測夾取資訊,再經過干涉篩選與穩定度的排序,獲得最終的夾取點預測。 為了驗證本夾取流程的效果,本文架設實驗環境與機械手臂系統實際運行夾取流程,統計不同流程的夾取成功率與速度並探討本流程的特色與優點。最終本文的夾取流程針對兩樣金屬物件在隨機堆疊場景當中的夾取成功率為89.285%,整體運算的時間為1.128秒。 | zh_TW |
dc.description.abstract | A process is proposed to improve the robotic grasping and classification system in this thesis, in which the system first uses instance segmentation technique to segment the image of the object in clutter, then proceeded by pose estimation and finally applies collision detection process to output the optimal grasping position for the robot. This thesis focuses on the establishment of the pose estimation process by using the augmented autoencoder which uses a virtual camera to automatically crop the poses of the target object for dataset and contains domain randomization to avoid domain gap between real and synthetic data. In order to verify the effectiveness of the process, a robotic system is set up to perform the random bin picking. It is shown that the success rate of grasping two metal objects in clutter can be up to 89.285% and the computation time is 1.128 seconds. | en |
dc.description.provenance | Made available in DSpace on 2021-05-20T00:57:21Z (GMT). No. of bitstreams: 1 U0001-2201202120220500.pdf: 8402215 bytes, checksum: 8af91fa03ab30dc7296b7cb4bd3e261d (MD5) Previous issue date: 2021 | en |
dc.description.tableofcontents | 誌謝 i 摘要 ii ABSTRACT iii 目錄 iv 圖目錄 vii 表目錄 xii 第一章 緒論 1 1.1 前言 1 1.2 文獻回顧 2 1.2.1 物件偵測 3 1.2.2 物件姿態估測 4 1.2.3 夾取點估測 6 1.2.4 夾取系統 9 1.3 研究動機與目的 14 1.4 論文架構 16 第二章 物件切割 17 2.1 前言 17 2.2 場景認知 17 2.2.1 物件偵測 18 2.2.2 影像切割 20 2.3 Mask R-CNN 22 2.3.1 特徵擷取網路 22 2.3.2 區域提案網路 24 2.3.3 RoIAlign 25 2.3.4 遮罩、邊界框與類別預測 25 2.4 資料集 26 2.4.1 資料收集 27 2.4.2 資料標註 27 2.4.3 資料增強 28 2.5 模型訓練 29 2.5.1 模型參數 29 2.5.2 損失函數 30 2.5.3 訓練參數 30 2.6 模型預測 31 2.6.1 特徵擷取網路預測 31 2.6.2 區域提案網路預測 31 2.6.3 物件切割結果 32 第三章 姿態估測與夾取點判定 33 3.1 前言 33 3.2 姿態估測評比 33 3.2.1 BOP基準簡介 34 3.2.2 BOP基準資料集 34 3.2.3 BOP基準評比結果 35 3.3 增強式自動編碼網路 36 3.3.1 自動編碼網路 36 3.3.2 領域隨機化 38 3.3.3 姿態資料庫 40 3.3.4 模型貢獻統整 41 3.4 姿態估測 43 3.4.1 資料集建立 44 3.4.2 模型訓練 45 3.4.3 姿態資料庫建立 46 3.4.4 模型驗證 46 3.4.5 模型預測 47 3.4.6 模型比較 47 3.5 夾取點判定 48 3.5.1 夾取經驗轉移 48 3.5.2 夾取候選擴增 50 3.5.3 夾取干涉篩選 50 3.5.4 最終夾取判定 52 第四章 系統與驗證 53 4.1 前言 53 4.2 系統架構 53 4.2.1 控制系統 54 4.2.2 實驗環境 55 4.2.3 夾取流程 56 4.3 流程驗證 57 4.3.1 實驗架設與流程 57 4.3.2 夾取結果與討論 58 第五章 結論與未來展望 62 5.1 結論 62 5.2 未來展望 63 參考文獻 64 | |
dc.language.iso | zh-TW | |
dc.title | 增強式自動編碼網路應用於隨機堆疊物件之分類夾取 | zh_TW |
dc.title | Robotic Random Bin Picking and Classification System Using Augmented Autoencoder | en |
dc.type | Thesis | |
dc.date.schoolyear | 109-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳亮嘉(Liang-Chia Chen),林峻永(Chun-Yeon Lin) | |
dc.subject.keyword | 堆疊夾取,實例切割,姿態估測,自動編碼網路,領域隨機化, | zh_TW |
dc.subject.keyword | Clutter Grasping,Instance Segmentation,Pose Estimation,Autoencoder,Domain Randomization, | en |
dc.relation.page | 71 | |
dc.identifier.doi | 10.6342/NTU202100129 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2021-01-29 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 機械工程學研究所 | zh_TW |
dc.date.embargo-lift | 2025-02-01 | - |
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