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題名 基於視覺導航之多無人機自主巡檢任務協作
Autonomous Multi-UAVs Collaborative Inspection based on Visual Navigation作者 謝鴻偉
Hsieh, Hung-Wei貢獻者 劉吉軒
Liu, Jyi-Shane
謝鴻偉
Hsieh, Hung-Wei關鍵詞 無人機
多無人機系統
視覺導航
編隊控制
協作視覺同時定位與地圖構建
語義分割
UAV
multi-UAVs system
visual navigation
formation control
collaborative visual SLAM
semantic segmentation日期 2023 上傳時間 1-Sep-2023 15:23:19 (UTC+8) 摘要 隨著無人機技術的成熟與成本降低，在許多領域如物資遞送、精準農業、設施巡檢皆有無人機的應用案例。應用一台無人機於地面場域巡檢時，若遇到寬度較大的道路、河川、橋樑時，需以較高高度飛行才能涵蓋完整目標，然而這將擴大與目標的距離，導致目標解析度下降，較難用蒐集到的影像做進一步的分析。若以較低高度飛行，雖能有較清楚的目標影像，但無法涵蓋完整目標，將降低巡檢的效率。應用多無人機執行巡檢能以較低高度飛行，同時擴大視野，保有巡檢效率與清楚目標影像等優點。本研究提出基於視覺導航的多無人機自主巡檢方法，利用無人機鏡頭獲得的影像資訊進行定位與判斷巡檢目標位置，使多無人機自主跟隨巡檢目標及編隊飛行以能同時涵蓋不同側影像。系統架構包含協作視覺定位模組、線條偵測模組與飛行控制模組，以協作視覺同時定位與地圖構建方法估計多無人機位置，以語義分割技術偵測巡檢目標並產生跟隨線條，並利用這些資訊產生飛行控制指令使多無人機進行線條跟隨與編隊飛行。本研究設定的目標巡檢場域為河川，我們在模擬環境與真實環境的河川場域皆進行實驗，以探討模擬環境與真實環境中的差異，驗證本研究提出的系統之可行性，並分析不同情境下於巡檢過程中線條跟隨與編隊飛行的穩定性與效能，展示巡檢過程影像以證實本研究的應用價值。
With the maturity and cost reduction of Unmanned Aerial Vehicles (UAVs) technology, there are applications of UAVs in various fields such as goods delivery, precision agriculture, and facility inspection. Compared to using a single UAV, employing multiple UAVs can further enhance task efficiency and execute more complex missions. When using a single UAV for ground inspection, When using a single drone for ground inspection, if encountering roads, rivers, or bridges with larger widths, it is necessary to fly at a higher altitude in order to cover the entire target. However, this increases the distance from the target, leading to decreased target resolution, making it more challenging to perform further analysis using collected images. Flying at lower altitudes provides clearer target images but may not cover the entire target, thus reducing inspection efficiency. Employing multiple UAVs for inspection enables flying at lower altitudes while expanding the field of view, retaining benefits such as inspection efficiency and clear target images. This study proposes a multi-UAVs autonomous inspection method based on visual navigation. It uses image information captured by UAV cameras for localization and determining the location of inspection targets. This allows multiple UAVs to autonomously follow inspection targets and fly in formation to simultaneously cover different side views. The system architecture includes collaborative visual localization module, line detection module, and flight control module. We utilize collaborative visual SLAM method to estimate the positions of multiple UAVs, and semantic segmentation techniques to detect inspection targets and generate follow lines. Flight control commands are generated using these information to enable multiple UAVs line following and formation flying.The target inspection area set in this study is river. Experiments were conducted in both simulated and real river environments to explore differences between the two and validate the feasibility of the proposed system. Stability and performance of line following and formation flying during inspections under various scenarios were analyzed. The inspection process images were presented to demonstrate the practicality of this research.參考文獻 [1] A. N. Wilson, A. Kumar, A. Jha, and L. R. Cenkeramaddi, “Embedded sensors, communication technologies, computing platforms and machine learning for uavs: A review,” IEEE Sensors Journal, vol. 22, no. 3, pp. 1807–1826, 2022.[2] H. Shakhatreh, A. H. Sawalmeh, A. Al-Fuqaha, Z. Dou, E. Almaita, I. Khalil, N. S. Othman, A. Khreishah, and M. Guizani, “Unmanned aerial vehicles (uavs): A survey on civil applications and key research challenges,” IEEE Access, vol. 7, pp. 48572–48634, 2019.[3] S. Jordan, J. Moore, S. E. Hovet, jonny box, J. Perry, K. Kirsche, D. Lewis, and Z. T. H. Tse, “State-of-the-art technologies for uav inspections,” Iet Radar Sonar and Navigation, vol. 12, pp. 151–164, 2018.[4] K. Máthé and L. Buşoniu, “Vision and control for uavs: A survey of general methods and of inexpensive platforms for infrastructure inspection,” Sensors, vol. 15, no. 7, pp. 14887–14916, 2015.[5] G. Skorobogatov, C. Barrado, and E. Salamí, “Multiple uav systems: A survey,” Unmanned Systems, vol. 8, no. 02, pp. 149–169, 2020.[6] J. Han, Y. Xu, L. Di, and Y. Chen, “Low-cost multi-uav technologies for con- tour mapping of nuclear radiation field,” Journal of Intelligent & Robotic Systems, vol. 70, pp. 401 410, 2013.[7] J. Scherer, S. Yahyanejad, S. Hayat, E. Yanmaz, T. Andre, A. Khan, V. Vukadinovic, C. Bettstetter, H. 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Skinnemoen, “River segmentation for flood monitoring,” in 2017 IEEE International Conference on Big Data (Big Data), pp. 3746–3749, 2017.[48] Parrot, “Parrot anafi | professional drone camera 4k hdr.” Available at https://www.parrot.com/us/drones/anafi.[49] Parrot, “Parrot anafi | professional thermal drones - 4k camera.” Available at https://www.parrot.com/en/drones/anafi-thermal.[50] Parrot-Developers, “Olympe - python controller library for parrot drones.” Availableat https://github.com/Parrot-Developers/olympe.[51] Parrot, “What is parrot sphinx.” Available at https://developer.parrot.com/docs/sphinx/index.html. 描述 碩士
國立政治大學
資訊科學系
109753155資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109753155 資料類型 thesis dc.contributor.advisor 劉吉軒 zh_TW dc.contributor.advisor Liu, Jyi-Shane en_US dc.contributor.author (Authors) 謝鴻偉 zh_TW dc.contributor.author (Authors) Hsieh, Hung-Wei en_US dc.creator (作者) 謝鴻偉 zh_TW dc.creator (作者) Hsieh, Hung-Wei en_US dc.date (日期) 2023 en_US dc.date.accessioned 1-Sep-2023 15:23:19 (UTC+8) - dc.date.available 1-Sep-2023 15:23:19 (UTC+8) - dc.date.issued (上傳時間) 1-Sep-2023 15:23:19 (UTC+8) - dc.identifier (Other Identifiers) G0109753155 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/147027 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學系 zh_TW dc.description (描述) 109753155 zh_TW dc.description.abstract (摘要) 隨著無人機技術的成熟與成本降低，在許多領域如物資遞送、精準農業、設施巡檢皆有無人機的應用案例。應用一台無人機於地面場域巡檢時，若遇到寬度較大的道路、河川、橋樑時，需以較高高度飛行才能涵蓋完整目標，然而這將擴大與目標的距離，導致目標解析度下降，較難用蒐集到的影像做進一步的分析。若以較低高度飛行，雖能有較清楚的目標影像，但無法涵蓋完整目標，將降低巡檢的效率。應用多無人機執行巡檢能以較低高度飛行，同時擴大視野，保有巡檢效率與清楚目標影像等優點。本研究提出基於視覺導航的多無人機自主巡檢方法，利用無人機鏡頭獲得的影像資訊進行定位與判斷巡檢目標位置，使多無人機自主跟隨巡檢目標及編隊飛行以能同時涵蓋不同側影像。系統架構包含協作視覺定位模組、線條偵測模組與飛行控制模組，以協作視覺同時定位與地圖構建方法估計多無人機位置，以語義分割技術偵測巡檢目標並產生跟隨線條，並利用這些資訊產生飛行控制指令使多無人機進行線條跟隨與編隊飛行。本研究設定的目標巡檢場域為河川，我們在模擬環境與真實環境的河川場域皆進行實驗，以探討模擬環境與真實環境中的差異，驗證本研究提出的系統之可行性，並分析不同情境下於巡檢過程中線條跟隨與編隊飛行的穩定性與效能，展示巡檢過程影像以證實本研究的應用價值。 zh_TW dc.description.abstract (摘要) With the maturity and cost reduction of Unmanned Aerial Vehicles (UAVs) technology, there are applications of UAVs in various fields such as goods delivery, precision agriculture, and facility inspection. Compared to using a single UAV, employing multiple UAVs can further enhance task efficiency and execute more complex missions. When using a single UAV for ground inspection, When using a single drone for ground inspection, if encountering roads, rivers, or bridges with larger widths, it is necessary to fly at a higher altitude in order to cover the entire target. However, this increases the distance from the target, leading to decreased target resolution, making it more challenging to perform further analysis using collected images. Flying at lower altitudes provides clearer target images but may not cover the entire target, thus reducing inspection efficiency. Employing multiple UAVs for inspection enables flying at lower altitudes while expanding the field of view, retaining benefits such as inspection efficiency and clear target images. This study proposes a multi-UAVs autonomous inspection method based on visual navigation. It uses image information captured by UAV cameras for localization and determining the location of inspection targets. This allows multiple UAVs to autonomously follow inspection targets and fly in formation to simultaneously cover different side views. The system architecture includes collaborative visual localization module, line detection module, and flight control module. We utilize collaborative visual SLAM method to estimate the positions of multiple UAVs, and semantic segmentation techniques to detect inspection targets and generate follow lines. Flight control commands are generated using these information to enable multiple UAVs line following and formation flying.The target inspection area set in this study is river. Experiments were conducted in both simulated and real river environments to explore differences between the two and validate the feasibility of the proposed system. Stability and performance of line following and formation flying during inspections under various scenarios were analyzed. The inspection process images were presented to demonstrate the practicality of this research. en_US dc.description.tableofcontents 第一章 緒論 11.1 研究背景 11.2 研究動機與目的 21.3 研究成果與貢獻 51.4 論文架構 5第二章 文獻探討 62.1 同時定位與地圖構建 (SLAM) 62.1.1 視覺 SLAM (VSLAM) 62.1.2 協作視覺 SLAM (Collaborative VSLAM) 72.2 多無人機編隊控制 82.2.1 無人機控制 82.2.2 編隊控制策略 10第三章 多無人機自主巡檢方法 123.1 系統架構 123.1.1 無人機 133.1.2 地面站 133.2 協作視覺定位模組 143.2.1 CCM-SLAM 153.2.2 地圖合併策略 153.2.3 真實尺度估計 163.3 線條偵測模組 173.3.1 語義分割模型 183.4 飛行控制模組 203.4.1 跟隨控制 213.4.2 編隊控制 27第四章 實驗結果與分析 314.1 實驗設置 314.1.1 硬體設置 314.1.2 軟體設置 334.2 實驗流程與評估方法 354.2.1 實驗流程 354.2.2 實驗評估方法 364.3 模擬環境實驗 374.3.1 實驗場域 374.3.2 實驗結果分析 394.4 真實環境實驗 564.4.1 實驗場域 564.4.2 實驗結果分析 574.5 小結 71第五章 結論與未來展望 735.1 研究結論 735.2 未來展望 74參考文獻 76 zh_TW dc.format.extent 42143371 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109753155 en_US dc.subject (關鍵詞) 無人機 zh_TW dc.subject (關鍵詞) 多無人機系統 zh_TW dc.subject (關鍵詞) 視覺導航 zh_TW dc.subject (關鍵詞) 編隊控制 zh_TW dc.subject (關鍵詞) 協作視覺同時定位與地圖構建 zh_TW dc.subject (關鍵詞) 語義分割 zh_TW dc.subject (關鍵詞) UAV en_US dc.subject (關鍵詞) multi-UAVs system en_US dc.subject (關鍵詞) visual navigation en_US dc.subject (關鍵詞) formation control en_US dc.subject (關鍵詞) collaborative visual SLAM en_US dc.subject (關鍵詞) semantic segmentation en_US dc.title (題名) 基於視覺導航之多無人機自主巡檢任務協作 zh_TW dc.title (題名) Autonomous Multi-UAVs Collaborative Inspection based on Visual Navigation en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] A. 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