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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81980完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林達德(Ta-Te Lin) | |
| dc.contributor.author | Jing-Heng Lin | en |
| dc.contributor.author | 林敬恆 | zh_TW |
| dc.date.accessioned | 2022-11-25T05:33:31Z | - |
| dc.date.available | 2024-09-01 | |
| dc.date.copyright | 2021-11-12 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-09-08 | |
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In 2016 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 649-656. Pieruschka, R., Schurr, U., 2019. Plant phenotyping: past, present, and future. Plant Phenomics, 2019. Redmon, J., Farhadi, A., 2018. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767. Rose, J., Paulus, S. and Kuhlmann, H., 2015. Accuracy Analysis of a Multi-View Stereo Approach for Phenotyping of Tomato Plants at the Organ Level. Sensors, 15(5), pp.9651-9665. Roldán, J., Garcia-Aunon, P., Garzón, M., de León, J., del Cerro, J. and Barrientos, A., 2016. Heterogeneous Multi-Robot System for Mapping Environmental Variables of Greenhouses. Sensors, 16(7), p.1018. Segura, M., Auat Cheein, F., Toibero, J., Mut, V. and Carelli, R., 2011. Ultra Wide-Band Localization and SLAM: A Comparative Study for Mobile Robot Navigation. Sensors, 11(2), pp.2035-2055. Smith, R., Self, M., Cheeseman, P., 1990. Estimating uncertain spatial relationships in robotics. In Autonomous robot vehicles, pp. 167-193. Springer, New York, NY. Song, Y., Guan, M., Tay, W. P., Law, C. L., Wen, C., 2019. UWB/LiDAR Fusion for cooperative range-only SLAM. 2019 IEEE International Conference on Robotics and Automation (ICRA), pp. 6568-6574. Shakhatreh, H., Sawalmeh, A., Al-Fuqaha, A., Dou, Z., Almaita, E., Khalil, I., Othman, N., Khreishah, A. and Guizani, M., 2019. Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges. IEEE Access, 7, pp.48572-48634. Sullivan, J., 2006. Evolution or revolution? the rise of UAVs. IEEE Technology and Society Magazine, 25(3), pp.43-49. Tangarife, H. I., Díaz, A. E. (2017, October). Robotic applications in the automation of agricultural production under greenhouse: A review. In 2017 IEEE 3rd Colombian Conference on Automatic Control (CCAC) (pp. 1-6). Tiemann, J., Ramsey, A., Wietfeld, C., 2018. Enhanced UAV indoor navigation through SLAM-augmented UWB localization. 2018 IEEE International Conference on Communications Workshops (ICC Workshops), pp. 1-6. Tiemann, J., Schweikowski, F., Wietfeld, C., 2015, October. Design of an UWB indoor-positioning system for UAV navigation in GNSS-denied environments. 2015 IEEE International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1-7. Terejanu, G. A., 2008. Extended kalman filter tutorial. University at Buffalo. Vakilian, K. A., Massah, J. A. F. A. R., 2012. Design, development and performance evaluation of a robot to early detection of nitrogen deficiency in greenhouse cucumber (Cucumis sativus) with machine vision. Int. J. Agric. Res. Rev, 2, 448-454. Yagfarov, R., Ivanou, M., Afanasyev, I., 2018. Map comparison of lidar-based 2d slam algorithms using precise ground truth. In 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 1979-1983. Zhao, Y., Gong, L., Liu, C., Huang, Y., 2016. Dual-arm robot design and testing for harvesting tomato in greenhouse. IFAC-PapersOnLine, 49(16),pp 161-165. Zhou, Y., Guo, X., Zhou, M., and Wang, L., 2007. A Design of Greenhouse Monitoring Control System Based on ZigBee Wireless Sensor Network. 2007 International Conference on Wireless Communications, Networking and Mobile Computing, Shanghai. pp. 2563-2567. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81980 | - |
| dc.description.abstract | "數據導向的育種技術和溫室環境控制方法可以提高作物的生產效率。在植物表型分析中，監測系統對優化作物生長和產量起著關鍵作用。無人飛行機（Unmanned Aerial Vehicle, UAV）與定點傳感器不同，由於其機動性，可以作為溫室中精確數據採集的整體解決方案。本研究開發了一種可以在GPS訊號無法有效利用之溫室環境，具備自主導航能力之番茄表型數據採集無人機。該四旋翼無人機安裝有嵌入式電腦、深度相機、測距儀、光流感測器、二維光達與深度相機。深度相機用於表型數據收集，用來提取距離資訊以估計番茄大小並在偵測階段忽略超出範圍的番茄。無人機使用開源的Pixhawk 4飛行控制器並包含低雜訊機載慣性測量單元。導航方法利用嵌入式電腦接收光達掃描距離點，以ROS (Robot Operating System)作為軟體控制系統，同步運行Cartographer SLAM即時建圖定位節點、自適應蒙特卡羅定位( Adaptive Monte Carlo Localization, AMCL)節點與路規劃節點。室內飛行透過SLAM即時定位與光流補償控制達到穩定飛行之目標，水平飄移量之均方根誤差為9.6 cm。利用測距儀與氣壓計融合以預估無人機飛行姿態，達到定高目的，固定高度控制之均方根誤差為2.6 cm。此系統使用YOLOv3物件辨識模型從影片中檢測出番茄，對靜止圖像進行測試時，命中率為91%。並採用SFM (Structure From Motion）水果辨識演算法進行水果計數；手持式裝置獲取番茄影片的最佳平均辨識相對誤差小於15%，而無人機約為17%。果實表型分析將可以為水果產量評估提供定量指標，以優化農作物產量，所提出的方法不僅可用於溫室監測和表型分析，還可用於其他應用，如農場管理和無人倉庫自動化。" | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T05:33:31Z (GMT). No. of bitstreams: 1 U0001-0809202101223600.pdf: 9227422 bytes, checksum: 9646116ecdb3289578a7f452b8f95b55 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 誌謝 i 摘要 ii Abstract iii 目錄 v 圖目錄 viii 表目錄 xii 第一章 緒論 1 1.1 前言 1 1.2 研究目的 4 第二章 文獻探討 5 2.1 無人機與智慧溫室農業 5 2.2無人機控制與飛行 7 2.2.1 飛行控制系統 9 2.2.2 四旋翼無人機 10 2.3 無人機巡航 11 2.3.1 定位與被動式路徑規劃 12 2.3.2 主動與混和式巡航 14 2.4即時定位與地圖構建 15 2.5高通量果實表型分析 18 第三章 研究方法 19 3.1系統架構 19 3.1.1巡航運作架構 20 3.1.2硬體架構 22 3.1.2軟體架構與飛行控制系統 29 3.2無人機飛行控制 33 3.3無人機室內自穩飛行 34 3.4無人機室內巡航 37 3.4.1 SLAM即時定位與建圖 38 3.4.2即時定位與飛行控制 40 3.4.3地圖定位與路徑規劃 41 3.4.4巡航模擬 44 3.5高通量表型數據蒐集 46 3.5.1影像蒐集 46 3.5.2影像蒐集有效性實驗 47 3.6高通量表型分析 48 第四章 結果與討論 50 4.1飛行系統建立 50 4.1.1飛行系統運作負載 50 4.1.2飛行穩定性測試 53 4.2室內飛行 58 4.1.4定高懸停飛行 58 4.1.5光流自穩飛行 60 4.3室內巡航 63 4.1.1感測器性能測試 63 4.2.1 SLAM即時建圖 65 4.2.1定位結果 72 4.4番茄影像收集與高通量表型分析 74 4.5場域驗證 79 4.4.1 溫室場域建圖與定位驗證 79 4.4.2 番茄表型特徵分析驗證 85 4.4.3 巡航演算法驗證 87 4.4.3 室內飛行驗證 91 第五章 結論與建議 95 參考文獻 97 | |
| dc.language.iso | zh-TW | |
| dc.subject | 感測器融合 | zh_TW |
| dc.subject | 導航 | zh_TW |
| dc.subject | 表型分析 | zh_TW |
| dc.subject | 自動化 | zh_TW |
| dc.subject | SLAM | zh_TW |
| dc.subject | 無人機 | zh_TW |
| dc.subject | Localization and Mapping | en |
| dc.subject | Navigation | en |
| dc.subject | Unmanned Aerial Vehicle | en |
| dc.subject | Phenotyping | en |
| dc.subject | Sensor Fusion | en |
| dc.subject | Automation | en |
| dc.title | 溫室巡航無人機應用於高通量番茄果實表型分析研究 | zh_TW |
| dc.title | Greenhouse UAV Navigation System Applies to High Throughput Phenotyping for Tomato Fruits | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 郭彥甫(Hsin-Tsai Liu),楊江益(Chih-Yang Tseng) | |
| dc.subject.keyword | 自動化,無人機,導航,感測器融合,SLAM,表型分析, | zh_TW |
| dc.subject.keyword | Automation,Sensor Fusion,Phenotyping,Localization and Mapping,Unmanned Aerial Vehicle,Navigation, | en |
| dc.relation.page | 101 | |
| dc.identifier.doi | 10.6342/NTU202103051 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-09-10 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 生物機電工程學系 | zh_TW |
| dc.date.embargo-lift | 2024-09-01 | - |
| 顯示於系所單位： | 生物機電工程學系 | |
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| U0001-0809202101223600.pdf 授權僅限NTU校內IP使用（校園外請利用VPN校外連線服務） | 9.01 MB | Adobe PDF |
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