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題名 無人機基於智慧空間模型導航應用
UAVs Intelligent Space Modeling and Navigation Applications作者 馬智煒
Ma, Chih-Wei貢獻者 劉吉軒
Liu, Jyi-Shane
馬智煒
Ma, Chih-Wei關鍵詞 無人機
空間模型
自主導航
空間資訊計算應用日期 2023 上傳時間 9-Mar-2023 18:36:37 (UTC+8) 摘要 近年來無人機的發展愈發成熟，從早期皆需要人為遠端操控到現在已逐漸在發展自主無人機，目的是為了取代許多高危險的工作，如救災、高壓電塔檢查、地面動態情境偵查。由此可知無人機應用已普及在我們的周遭。目前無人機的應用有許多，但對於自主無人機來說感測周遭的環境狀況往往都仰賴感測器的即時資訊，有些事前就已知的遠處資訊或是之前有探訪過的資訊無人機卻無從獲得。無人機需要再次抵達該區域才能夠使用感測器進行環境偵測獲得資訊，無人機難以達到較好的效率。因此本研究希望能設計一套空間模型，該空間模型將會紀錄無人機所需的任務與定位資訊。讓無人機可以不用移動就直接調用遠處的資訊，並以此資訊進行決策判斷。如此無人機將可以減少不必要的飛行時間，進而增加無人機任務效率。 參考文獻 [1] International Civil Aviation Organization(ICAO). Global Air Traffic Management Operational Concept;ICAO,2005.[2] Y. Lin et al., "Image Processing Techniques for UAV Vision-Based River Floating Contaminant Detection," 2019 Chinese Automation Congress (CAC), 2019, pp. 89-94.[3] X. Yang, L. Tang, H. Wang and X. He, "Early Detection of Forest Fire Based on Unmaned Aerial Vehicle Platform," 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP), 2019, pp. 1-4.[4] M. Elloumi, R. Dhaou, B. Escrig, H. Idoudi and L. A. Saidane, "Monitoring road traffic with a UAV-based system," 2018 IEEE Wireless Communications and Networking Conference (WCNC), 2018, pp. 1-6.[5] S. Wang, Y. Han, J. Chen, Z. Zhang, G. Wang and N. Du, "A Deep-Learning-Based Sea Search and Rescue Algorithm by UAV Remote Sensing," 2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC), 2018, pp. 1-5.[6] M. Shadman Shafkat Tanjim, S. Ahammad Rafi, S. Barua, A. Nushra Oishi and M. Imran Hossain, "FRIQ 1.0: A Guided Quadcopter to Inject Retardant Fluid or Gas Aerially into the Fire Affected Zone," 2020 2nd International Conference on Sustainable Technologies for Industry 4.0 (STI), 2020, pp. 1-5.[7] L. Deng, Y. He and Q. Liu, "Research on Application of Fire Uumanned Aerial Vehicles in Emergency Rescue," 2019 9th International Conference on Fire Science and Fire Protection Engineering (ICFSFPE), 2019, pp. 1-5.[8] W. Li, D. Hu and Z. Lin, "Indoor Space Dimensional Model Supporting the Barrier-free Path-finding," 2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS), 2018, pp. 1-9.[9] I. Afyouni, C. Ray, and C. Christophe. "Spatial models for context-aware indoor navigation systems: A survey," Journal of Spatial Information Science, 2012, pp. 85-123.[10] M. Worboys, "Modeling indoor space," Proceedings of the 3rd ACM SIGSPATIAL international workshop on indoor spatial awareness, 2011, pp. 1-6.[11] K. Karur et al., "A survey of path planning algorithms for mobile robots," Vehicles, 2021, pp. 448-468.[12] S. Koenig, M. Likhachev, "Incremental a," Advances in neural information processing systems, 2001, pp. 1539-1546.[13] S. Koenig, M. Likhachev, "D^* lite," Association for the Advancement of AI, 2002, pp. 476-483.[14] P. E. Hart, N. J. Nilsson and B. Raphael, "A Formal Basis for the Heuristic Determination of Minimum Cost Paths," in IEEE Transactions on Systems Science and Cybernetics, 1968, vol. 4, no. 2, pp. 100-107.[15] J. Branu et al. "A Comparison of A* and RRT* Algorithms with Dynamic and Real Time Constraint Scenarios for Mobile Robots." SIMULTECH, 2019, pp. 398-405[16] C. Zammit et al. "Comparison between A* and RRT Algorithms for UAV Path Planning,", Proceedings of the 2018 AIAA Guidance, Navigation, and Control Conference, 2018.[17] W. C. Wright, B. E. Wilkinson, W. P. Cropper and C. E. Oxendine, "Classifying Terrestrial Based Forest Photography with Geographic Information Systems to Model Signal Loss," IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018, pp. 6420-6423.[18] A. G. Dubrovin, et al., "Analysis of noise immunity of GLONASS and GPS positioning receivers," IOP Conference Series: Materials Science and Engineering, 2020, vol. 734, no. 1.[19] A. Hameed and H. A. Ahmed, "Survey on indoor positioning applications based on different technologies," 2018 12th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS), 2018, pp. 1-5, pp.[20] X. Xin, J. Jiang, and Y. Zou. "A review of visual-based localization," Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence, 2019.[21] N. Piasco et al., "A survey on visual-based localization: On the benefit of heterogeneous data," Pattern Recognition, 2018,vol.74, pp. 90-109.[22] D. Nister, O. Naroditsky and J. Bergen, "Visual odometry," Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, pp. I-I.[23] G. Klein and D. Murray, "Parallel Tracking and Mapping for Small AR Workspaces," 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, 2007, pp. 225-234.[24] J. Engel, V. Koltun and D. Cremers, "Direct Sparse Odometry," in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, vol. 40, no. 3, pp. 611-625.[25] J. Engel, T. Schöps, and D. Cremers, "LSD-SLAM: Large-scale direct monocular SLAM." European conference on computer vision, 2014, pp. 834-849.[26] R. Mur-Artal, J. M. M. Montiel and J. D. Tardós, "ORB-SLAM: A Versatile and Accurate Monocular SLAM System," in IEEE Transactions on Robotics, 2015, vol. 31, no. 5, pp. 1147-1163.[27] A. Gautam, S. Mahangade, V. I. Gupta, R. Madan and K. Arya, "An experimental comparison of visual SLAM systems," 2021 International Symposium of Asian Control Association on Intelligent Robotics and Industrial Automation (IRIA), 2021, pp. 13-18.[28] M. Filipenko and I. Afanasyev, "Comparison of Various SLAM Systems for Mobile Robot in an Indoor Environment," 2018 International Conference on Intelligent Systems (IS), 2018, pp. 400-407.[29] M. Colledanchise, and P. Ögren, "Behavior Trees in Robotics and AI: An Introduction," 2017, ArXiv:1709.00084.[30] D. Malyuta, C. Brommer, D. Hentzen, T. Stastny, R. Siegwart, and R. Brockers, “Long-duration fully autonomous operation of rotorcraft unmanned aerial systems for remote-sensing data acquisition,” Journal of Field Robotics, 2020, pp. 137-157.[31] C. Brommer, D. Malyuta, D. Hentzen and R. Brockers, "Long-Duration Autonomy for Small Rotorcraft UAS Including Recharging," 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018, pp. 7252-7258.[32] J. Wang and E. Olson, "AprilTag 2: Efficient and robust fiducial detection," 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016, pp. 4193-4198.[33] R. Ranftl, K. Lasinger, D. Hafner, K. Schindler and V. Koltun, "Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross-Dataset Transfer," in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, vol. 44, no. 3, pp. 1623-1637. 描述 碩士
國立政治大學
資訊科學系
108753129資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108753129 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) 馬智煒 zh_TW dc.contributor.author (Authors) Ma, Chih-Wei en_US dc.creator (作者) 馬智煒 zh_TW dc.creator (作者) Ma, Chih-Wei en_US dc.date (日期) 2023 en_US dc.date.accessioned 9-Mar-2023 18:36:37 (UTC+8) - dc.date.available 9-Mar-2023 18:36:37 (UTC+8) - dc.date.issued (上傳時間) 9-Mar-2023 18:36:37 (UTC+8) - dc.identifier (Other Identifiers) G0108753129 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/143832 - 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) 108753129 zh_TW dc.description.abstract (摘要) 近年來無人機的發展愈發成熟，從早期皆需要人為遠端操控到現在已逐漸在發展自主無人機，目的是為了取代許多高危險的工作，如救災、高壓電塔檢查、地面動態情境偵查。由此可知無人機應用已普及在我們的周遭。目前無人機的應用有許多，但對於自主無人機來說感測周遭的環境狀況往往都仰賴感測器的即時資訊，有些事前就已知的遠處資訊或是之前有探訪過的資訊無人機卻無從獲得。無人機需要再次抵達該區域才能夠使用感測器進行環境偵測獲得資訊，無人機難以達到較好的效率。因此本研究希望能設計一套空間模型，該空間模型將會紀錄無人機所需的任務與定位資訊。讓無人機可以不用移動就直接調用遠處的資訊，並以此資訊進行決策判斷。如此無人機將可以減少不必要的飛行時間，進而增加無人機任務效率。 zh_TW dc.description.tableofcontents 第一章 緒論 11.1 研究背景 11.2 研究動機與目的 31.3 論文架構 51.4 研究成果與貢獻 5第二章文獻探討 62.1 空間模型 62.2 路徑規劃 92.3 視覺定位 112.4 行為樹 12第三章 空間模型與空間資訊計算 153.1 物件類別 163.2 空間類別 173.3 資料模型 203.4 空間資訊計算 213.4.1 空間模型定位 213.4.2 路徑規劃與執行 233.4.3 障礙物偵測與避障 253.4.4 空間模型更新 283.5 基於空間模型的任務行為樹 29第四章 實驗與評估設計 324.1 實驗設計 324.2.1 理想路徑之誤差值 374.2.2 理想網格路徑之正確率與網格正確率 384.3 實驗數據分析 394.3.1 實驗一（多障礙狹窄空間） 404.3.1 實驗二（少障礙寬闊空間） 454.4 無人機路徑俯視圖與搜索 494.5 實驗結果 53第五章 結論與未來展望 565.1 研究結論 565.2 未來展望 57參考文獻 59 zh_TW dc.format.extent 918915 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108753129 en_US dc.subject (關鍵詞) 無人機 zh_TW dc.subject (關鍵詞) 空間模型 zh_TW dc.subject (關鍵詞) 自主導航 zh_TW dc.subject (關鍵詞) 空間資訊計算應用 zh_TW dc.title (題名) 無人機基於智慧空間模型導航應用 zh_TW dc.title (題名) UAVs Intelligent Space Modeling and Navigation Applications en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] International Civil Aviation Organization(ICAO). Global Air Traffic Management Operational Concept;ICAO,2005.[2] Y. Lin et al., "Image Processing Techniques for UAV Vision-Based River Floating Contaminant Detection," 2019 Chinese Automation Congress (CAC), 2019, pp. 89-94.[3] X. Yang, L. Tang, H. Wang and X. He, "Early Detection of Forest Fire Based on Unmaned Aerial Vehicle Platform," 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP), 2019, pp. 1-4.[4] M. Elloumi, R. Dhaou, B. Escrig, H. Idoudi and L. A. Saidane, "Monitoring road traffic with a UAV-based system," 2018 IEEE Wireless Communications and Networking Conference (WCNC), 2018, pp. 1-6.[5] S. Wang, Y. Han, J. Chen, Z. Zhang, G. Wang and N. Du, "A Deep-Learning-Based Sea Search and Rescue Algorithm by UAV Remote Sensing," 2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC), 2018, pp. 1-5.[6] M. Shadman Shafkat Tanjim, S. Ahammad Rafi, S. Barua, A. Nushra Oishi and M. Imran Hossain, "FRIQ 1.0: A Guided Quadcopter to Inject Retardant Fluid or Gas Aerially into the Fire Affected Zone," 2020 2nd International Conference on Sustainable Technologies for Industry 4.0 (STI), 2020, pp. 1-5.[7] L. Deng, Y. He and Q. Liu, "Research on Application of Fire Uumanned Aerial Vehicles in Emergency Rescue," 2019 9th International Conference on Fire Science and Fire Protection Engineering (ICFSFPE), 2019, pp. 1-5.[8] W. Li, D. Hu and Z. Lin, "Indoor Space Dimensional Model Supporting the Barrier-free Path-finding," 2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS), 2018, pp. 1-9.[9] I. Afyouni, C. Ray, and C. Christophe. "Spatial models for context-aware indoor navigation systems: A survey," Journal of Spatial Information Science, 2012, pp. 85-123.[10] M. Worboys, "Modeling indoor space," Proceedings of the 3rd ACM SIGSPATIAL international workshop on indoor spatial awareness, 2011, pp. 1-6.[11] K. Karur et al., "A survey of path planning algorithms for mobile robots," Vehicles, 2021, pp. 448-468.[12] S. Koenig, M. Likhachev, "Incremental a," Advances in neural information processing systems, 2001, pp. 1539-1546.[13] S. Koenig, M. Likhachev, "D^* lite," Association for the Advancement of AI, 2002, pp. 476-483.[14] P. E. Hart, N. J. Nilsson and B. Raphael, "A Formal Basis for the Heuristic Determination of Minimum Cost Paths," in IEEE Transactions on Systems Science and Cybernetics, 1968, vol. 4, no. 2, pp. 100-107.[15] J. Branu et al. "A Comparison of A* and RRT* Algorithms with Dynamic and Real Time Constraint Scenarios for Mobile Robots." SIMULTECH, 2019, pp. 398-405[16] C. Zammit et al. "Comparison between A* and RRT Algorithms for UAV Path Planning,", Proceedings of the 2018 AIAA Guidance, Navigation, and Control Conference, 2018.[17] W. C. Wright, B. E. Wilkinson, W. P. Cropper and C. E. Oxendine, "Classifying Terrestrial Based Forest Photography with Geographic Information Systems to Model Signal Loss," IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018, pp. 6420-6423.[18] A. G. Dubrovin, et al., "Analysis of noise immunity of GLONASS and GPS positioning receivers," IOP Conference Series: Materials Science and Engineering, 2020, vol. 734, no. 1.[19] A. Hameed and H. A. Ahmed, "Survey on indoor positioning applications based on different technologies," 2018 12th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS), 2018, pp. 1-5, pp.[20] X. Xin, J. Jiang, and Y. Zou. "A review of visual-based localization," Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence, 2019.[21] N. Piasco et al., "A survey on visual-based localization: On the benefit of heterogeneous data," Pattern Recognition, 2018,vol.74, pp. 90-109.[22] D. Nister, O. Naroditsky and J. Bergen, "Visual odometry," Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, pp. I-I.[23] G. Klein and D. Murray, "Parallel Tracking and Mapping for Small AR Workspaces," 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, 2007, pp. 225-234.[24] J. Engel, V. Koltun and D. Cremers, "Direct Sparse Odometry," in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, vol. 40, no. 3, pp. 611-625.[25] J. Engel, T. Schöps, and D. Cremers, "LSD-SLAM: Large-scale direct monocular SLAM." European conference on computer vision, 2014, pp. 834-849.[26] R. Mur-Artal, J. M. M. Montiel and J. D. Tardós, "ORB-SLAM: A Versatile and Accurate Monocular SLAM System," in IEEE Transactions on Robotics, 2015, vol. 31, no. 5, pp. 1147-1163.[27] A. Gautam, S. Mahangade, V. I. Gupta, R. Madan and K. Arya, "An experimental comparison of visual SLAM systems," 2021 International Symposium of Asian Control Association on Intelligent Robotics and Industrial Automation (IRIA), 2021, pp. 13-18.[28] M. Filipenko and I. Afanasyev, "Comparison of Various SLAM Systems for Mobile Robot in an Indoor Environment," 2018 International Conference on Intelligent Systems (IS), 2018, pp. 400-407.[29] M. Colledanchise, and P. Ögren, "Behavior Trees in Robotics and AI: An Introduction," 2017, ArXiv:1709.00084.[30] D. Malyuta, C. Brommer, D. Hentzen, T. Stastny, R. Siegwart, and R. Brockers, “Long-duration fully autonomous operation of rotorcraft unmanned aerial systems for remote-sensing data acquisition,” Journal of Field Robotics, 2020, pp. 137-157.[31] C. Brommer, D. Malyuta, D. Hentzen and R. Brockers, "Long-Duration Autonomy for Small Rotorcraft UAS Including Recharging," 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018, pp. 7252-7258.[32] J. Wang and E. Olson, "AprilTag 2: Efficient and robust fiducial detection," 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016, pp. 4193-4198.[33] R. Ranftl, K. Lasinger, D. Hafner, K. Schindler and V. Koltun, "Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross-Dataset Transfer," in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, vol. 44, no. 3, pp. 1623-1637. zh_TW
