無人機磁力探勘與異常視覺化分析系統

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：64

、訪客IP：3.149.250.34

姓名

楊承學(Cheng-xue Yang) 查詢紙本館藏

畢業系所

人工智慧國際碩士學位學程

論文名稱

無人機磁力探勘與異常視覺化分析系統
(Unmanned Aerial Vehicle Magnetic Survey and Anomaly Visualization Analysis System)

相關論文

★ 整合GRAFCET虛擬機器的智慧型控制器開發平台	★ 分散式工業電子看板網路系統設計與實作
★ 設計與實作一個基於雙攝影機視覺系統的雙點觸控螢幕	★ 智慧型機器人的嵌入式計算平台
★ 一個即時移動物偵測與追蹤的嵌入式系統	★ 一個固態硬碟的多處理器架構與分散式控制演算法
★ 基於立體視覺手勢辨識的人機互動系統	★ 整合仿生智慧行為控制的機器人系統晶片設計
★ 嵌入式無線影像感測網路的設計與實作	★ 以雙核心處理器為基礎之車牌辨識系統
★ 基於立體視覺的連續三維手勢辨識	★ 微型、超低功耗無線感測網路控制器設計與硬體實作
★ 串流影像之即時人臉偵測、追蹤與辨識─嵌入式系統設計	★ 一個快速立體視覺系統的嵌入式硬體設計
★ 即時連續影像接合系統設計與實作	★ 基於雙核心平台的嵌入式步態辨識系統

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2029-7-22以後開放)

摘要(中)

傳統的磁場探測方法受到地形和範圍的限制，效率通常較低。為了解決這些問題，本論文提出一種無人機磁力探測及異常視覺化分析系統，結合無人機技術與磁場感測器，利用無人機的高機動性，於大範圍區域收集磁場數據，大幅減少探測與分析的時間。此系統除了具備磁場數據收集功能外，也提供無人機自動控制、機器學習模型分析磁場數據以及視覺化輸出等三大功能。無人機自動控制使用穩定且可靠的分散式資料服務(DDS)與無人機連線，負責規劃磁場探測飛行路徑；通過霍爾效應感測器收集地面磁場數據；再由數據分析模組對磁場數據進行分析，同時以孤立森林演算法標記磁場異常區域；而視覺化模組則將分析結果透過二維和三維圖像直觀呈現，協助使用者進行深入分析和作出決策。實驗結果顯示，本系統能夠在複雜環境中迅速且精確
地完成磁場探測任務，有效識別地下磁場異常。相比傳統方法，大幅提升了探測的時效性和精確性。

摘要(英)

Traditional magnetic field detection methods are often constrained by terrain and range limitations, resulting in lower efficiency. To address these issues, this paper proposes a drone-based magnetic detection and anomaly visualization analysis system. The system integrates drone technology with magnetic field sensors, leveraging the high mobility of drones to collect magnetic field data over large areas, thereby significantly reducing the time required for detection and analysis.

The system comprises three major functionalities: magnetic field data collection, automatic drone control, and machine learning-based data analysis and visualization. The automatic drone control employs a stable and reliable Data Distribution Service (DDS) to connect with the drone and plan the magnetic detection flight path. Ground magnetic field data is collected using Hall effect sensors. The data analysis module processes the magnetic field data, utilizing the Isolation Forest algorithm to identify and mark anomalous magnetic field regions. The visualization module then presents the analysis results intuitively through two-dimensional (2D) and three-dimensional (3D) images, aiding users in conducting in-depth analyses and making informed decisions.

Experimental results demonstrate that the proposed system can efficiently and accurately perform magnetic field detection tasks in complex environments, effectively identifying underground magnetic anomalies. Compared to traditional methods, this system significantly enhances detection timeliness and accuracy.

關鍵字(中)

★ 無人機
★ 磁力探勘
★ 異常視覺化
★ 分析系統
★ 分散式資料分發服務

關鍵字(英)

★ Drone
★ Magnetic Survey
★ Anomaly Visualization
★ Analysis System
★ Data Distribution Service

論文目次

摘要 i
Abstract ii
Acknowledgments iii
Contents iv
List of Figures vi
List of Tables viii
1. Introduction 1
1.1 Research Background 1
1.2 Research Objectives 2
1.3 Article Structure 3
2. Related Works 4
2.1 Automated Control of Unmanned Aerial Vehicles 4
2.1.1 QGroundControl 4
2.1.2 MAVLink V2 5
2.1.3 Data Distribution Service 5
2.2 Magnetic Field Sensors 6
2.2.1 Hall Effect 6
2.2.2 Anisotropic Magnetoresistance 7
2.2.3 Giant Magnetoresistance 8
2.2.4 Tunneling Magnetoresistance 9
2.2.5 Superconducting Quantum Interference Device 10
2.3 I2C 11
2.4 Isolation Forest（iForest） 12
3. System Design 14
3.1 MIAT System Design Methodology 14
3.1.1 IDEF0 Hierarchical Modular Design 14
3.1.2 GRAFCET Discrete Event Modeling 15
3.2 System Architecture 18
3.3 System Discrete Event Modeling 21
3.3.1 Discrete Event Modeling for UAV Control 22
3.3.2 Discrete Event Modeling for Magnetic Field Data Collection 23
3.3.3 Discrete Event Modeling for Data Analysis 24
3.3.4 Discrete Event Modeling for Isolation Forest Data Analysis 25
3.3.5 Discrete Event Modeling for Magnetic Field Visualization 26
4. Experimental 27
4.1 Experimental Platform 27
4.1.1 UAV 28
4.1.2 Raspberry Pi 5 28
4.1.3 ROG Ally 29
4.1.4 MLX90393 29
4.1.5 Software Development Tools 30
4.2 UAV Mission Control System Verification 31
4.2.1 DDS Transmission of MAVLink Packets 31
4.2.2 UAV Magnetic Field Survey Mission Planning 33
4.3 UAV Magnetic Field Survey Experiment 33
4.4 Magnetic Field Data Collection 35
4.5 Magnetic Field Visualization Analysis System Verification 36
4.5.1 Original Magnetic Field Distribution 36
4.5.2 Single Anomalous Magnetic Field Distribution 38
4.5.3 Multiple Anomalous Magnetic Field Distribution 39
4.5.4 Marks abnormal magnetic fields (Isolation Forest) 40
4.6 Expected Contributions of the Research 40
5. Conclusion and Future Directions 42
5.1 Conclusion 42
5.2 Future Directions 42
Reference 44

參考文獻

[1] J.-x. LIU, H.-p. LIU, L. Rong, J.-q. XUE, Y.-h. LI, and W. Fang, "Application of aeromagnetic survey to mineral exploration of Jinping, Yunnan, China by using multirotor UAV," Transactions of Nonferrous Metals Society of China, vol. 33, no. 5, pp. 1550-1558, 2023.

[2] L.-S. Yoo, J.-H. Lee, S.-H. Ko, S.-K. Jung, S.-H. Lee, and Y.-K. Lee, "A drone fitted with a magnetometer detects landmines," IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 12, pp. 2035-2039, 2020.

[3] Y. Zheng, S. Li, K. Xing, and X. Zhang, "Unmanned aerial vehicles for magnetic surveys: A review on platform selection and interference suppression," Drones, vol. 5, no. 3, p. 93, 2021.
[4] G.-W. Huang and J.-T. Jeng, "Implementation of 16-channel AMR sensor array for quantitative mapping of two-dimension current distribution," IEEE Transactions on Magnetics, vol. 54, no. 11, pp. 1-5, 2018.

[5] S. Profanter, A. Tekat, K. Dorofeev, M. Rickert, and A. Knoll, "OPC UA versus ROS, DDS, and MQTT: performance evaluation of industry 4.0 protocols," in 2019 IEEE International Conference on Industrial Technology (ICIT), pp. 955-962, 2019.

[6] E. H. Hall, "On a new action of the magnet on electric currents," American Journal of Mathematics, vol. 2, no. 3, pp. 287-292, 1879.

[7] F. T. Liu, K. M. Ting, and Z.-H. Zhou, "Isolation forest," in 2008 eighth ieee international conference on data mining, pp. 413-422, 2008.

[8] C.-H. Chen, M.-Y. Lin, and X.-C. Guo, "High-level modeling and synthesis of smart sensor networks for Industrial Internet of Things," Computers & Electrical Engineering, vol. 61, pp. 48-66, 2017.

[9] Y. Maruyama, S. Kato, and T. Azumi, "Exploring the performance of ROS2," in Proceedings of the 13th international conference on embedded software, pp. 1-10, 2016.

[10] K. Sun, P. Qi, X. Tao, W. Zhao, and C. Ye, "Vector magnetic field imaging with high-resolution TMR sensor arrays for metal structure inspection," IEEE Sensors Journal, vol. 22, no. 14, pp. 14513-14521, 2022.

[11] L.-S. Yoo, J.-H. Lee, Y.-K. Lee, S.-K. Jung, and Y. Choi, "Application of a drone magnetometer system to military mine detection in the demilitarized zone," Sensors, vol. 21, no. 9, p. 3175, 2021.

[12] P. Calou and M. Munschy, "Airborne magnetic surveying with a drone and determination of the total magnetization of a dipole," IEEE Transactions on Magnetics, vol. 56, no. 6, pp. 1-9, 2020.

[13] P. Lipovský, K. Draganová, J. Novotňák, Z. Szőke, and M. Fiľko, "Indoor mapping of magnetic fields using uav equipped with fluxgate magnetometer," Sensors, vol. 21, no. 12, p. 4191, 2021.

[14] A. D. Eslamlou, A. Ghaderiaram, E. Schlangen, and M. Fotouhi, "A review on non-destructive evaluation of construction materials and structures using magnetic sensors," Construction and Building Materials, vol. 397, p. 132460, 2023

指導教授

陳慶瀚(Ching-han Chen)

審核日期

2024-8-13

推文