簡易檢索 / 詳目顯示

研究生: 阮世雄
Hung - The Nguyen
論文名稱: 基於自主行為之導航應用於空中和地面移動機器人
Autonomous Behavior Based Navigation between Aerial and Ground Mobile Robot
指導教授: 李敏凡
Min-Fan Lee
口試委員: 高維文
Wei-Wan Kao
郭重顯
Chung-Hsien Kuo
學位類別: 碩士
Master
系所名稱: 工程學院 - 自動化及控制研究所
Graduate Institute of Automation and Control
論文出版年: 2014
畢業學年度: 102
語文別: 英文
論文頁數: 121
中文關鍵詞: 無人機聯合運作架構模糊邏輯控制器遠端感測感測器融合
外文關鍵詞: Unmanned aerial vehicle, joint operation, fuzzy controller, remote sensing, sensor fusion
相關次數: 點閱:421下載:12
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 近年來應用空中無人機於災難搜救,環境監控,安全監控的發展相當受到注目。空中無人機之一的平翼形機種由於其飛行模式不能盤旋於一定點亦無法低空飛行,所以無法提供大範圍的影像資訊。地面移動機器人雖然可利用其傳感器,如聲納或雷射測距儀導航,但需要預先給定目標位資訊。
    本文提出了AMR(空中移動機器人)和GMR(地面移動機器人)之間的聯合行動架構以實現一個完整的自動偵搜和救援行動。首先,AMR將通過影像偵測搜索目標。接著AMR將保持追蹤GMR並提供地面環境資訊,其地面障礙物資訊結合GMR本地感測器資訊作為行為控制器的輪入進行即時的導航使得地面機器人抵達目標物。實驗結果證實該系統能找到目標物並以安全的路徑抵達目標物。高度停懸控制器實現了在XY平面保持在8.35厘米及在Z軸方向16.79厘米的精度。基於模糊理論之行為控制器結合避障行為與導航實現了在X軸方向保持在51.15厘米及在Y軸方向63.11厘米的精度。本文提出之系統結合空中與地面機器人之聯合行動架構將有助於改善福島核災教援之限制,其僅依靠獨立運作之地面與空中機器人並沒有協作和遠程操作模式。相較於傳統之同步建圖與定位(SLAM),本文所提出的空中和地面架構能提供高效率和有效性。未來亦可應用此系統於水源物質檢測及結合微型光譜儀的各項應用。


    Disaster search and rescue, environment monitoring, and security surveillance are the fields that had strong boost in utilization of Unmanned Aerial Vehicle (UAV) in recent years. The flat wing platform cannot provide hovering ability because of mechanical structure and be restricted by visual resolution at high altitude. The ground mobile robots can use local sensors like sonar or laser range finder to navigate but need to identify the target beforehand.
    This thesis proposed joint operation architecture between AMR (Aerial Mobile Robot) and GMR (Ground Mobile Robot) to achieve a full autonomous search and rescue operation. First, AMR will search for the target by visual technique. After that, AMR will track GMR and provide visual sensing of the ground facts. This will combined with information from local sensors on GMR as inputs for behavior based controller, to navigate GMR to target in real time. Experimental results confirm that system can locate and approach the target a trough a collision free path with reliable performance. The hovering controller of altitude as well as position achieved accuracy of 8.35 cm in xy plane and 16.79 cm in z axis. The behavior based fuzzy controller will navigate the robot to the destination with obstacle avoidance and its average error was 51.15 mm in x axis and 63.11mm in y axis. The proposed autonomous joint cooperation between UAV and AMR contribute to improve the limitation on the disaster response robotic used in Fukushima nuclear power plant disaster. They only have independent operation (ground and aerial), no collaboration and in tele-operated mode. In comparison to the conventional SLAM only on ground mobile robot, our proposed aerial and ground architecture highly improved the efficiency and effectiveness. Both UAV and GMR have on-board micro-spectrometer and spectra analysis system for monitoring of water resource and oil spill in the ocean.

    Table of Contents ABSTRACT I Table of Contents III List of Figures VI List of Tables X Chapter 1 Introduction 1 1.1 Background and Motivation 1 1.2 Literature Review 3 1.3 Purpose 5 1.4 Contribution 6 1.5 Organization 7 Chapter 2 System analysis 8 2.1 Survey of Multi-Rotor UAV Platform 8 2.2 Kinematic Model of UAV 9 2.2.1 Dynamic model of AR.Drone 9 2.2.2 PID Controller 12 2.3 Navigation Control for GMV 15 2.3.1 GMR P3-DX kinematics model 17 2.3.2 Self-localization 21 2.3.3 Path planning behavior controller 23 Chapter 3 Methodology 26 3.1 System Overview 26 3.2 Hovering Control System 29 3.3 Aerial visual aid system 32 3.3.1 Camera Calibration 33 3.3.2 Vision perception 38 3.3.3 Image stitching System 44 3.4 Behavior based fuzzy controller for GMR 49 3.4.1 Artificial Neural network classifier 49 3.4.2 Obstacle avoidance controller 54 3.4.3 Goal seeking controller 61 3.4.4 Wall following controller 63 3.5 Scan matching self-localization 65 Chapter 4 Experimental and Results 70 4.1 Experimental Setup 70 4.1.1 Sensory Equipment 71 4.1.2 Onboard Camera 73 4.1.3 Ground robot 75 4.2 Hovering and tracking Controller 76 4.2.1 Simulation test 76 4.2.2 AMR test 79 4.3 Navigation Control System 82 4.3.1 Target and obstacle detection 82 4.3.2 Scan matching localization 86 4.3.3 Fuzzy controller 90 4.4 Joint Operation 93 Chapter 5 Conclusion and Future Work 95 5.1 Discussion 95 5.2 Conclusion 96 5.3 Future work 97 Appendix 99 Reference 101 Biography 108

    Reference
    [1] Y. Zhang and H. Cui, "Techniques of UAV system land use changes detection application," in PIAGENG 2010 - Photonics and Imaging for Agricultural Engineering, December 25, 2010 - December 26, 2010. vol. 7752 Shanghai, China: SPIE, 2011.
    [2] C.-C. Li, G.-S. Zhang, T.-J. Lei, and A. D. Gong, "Quick image-processing method of UAV without control points data in earthquake disaster area," Transactions of Nonferrous Metals Society of China (English Edition), vol. 21, pp. s523-s528, 2011.
    [3] W. Jun, Z. Dong, Z. Liu, and G. Zhou, "Geo-registration and mosaic of UAV video for quick-response to forest fire disaster," in MIPPR 2007: Pattern Recognition and Computer Vision, November 15, 2007 - November 17, 2007. vol. 6788 Wuhan, China: SPIE, 2007.
    [4] A. J. Davison, "Vision-based SLAM in real-time," in IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I PART 1 ed. vol. 4477 LNCS Girona, Spain: Springer Verlag, 2007, pp. 9-12.
    [5] L. Merino, F. Caballero, J. R. Martinez-de Dios, and A. Ollero, "Cooperative Fire Detection using Unmanned Aerial Vehicles," in 2005 IEEE International Conference on Robotics and Automation., 2005, pp. 1884-1889.
    [6] B. Li and C. Zhang, "Adaptive fuzzy control for mobile robot obstacle avoidance based on virtual line path tracking," in 2006 IEEE International Conference on Robotics and Biomimetics, ROBIO 2006 Kunming, China: Inst. of Elec. and Elec. Eng. Computer Society, 2006, pp. 1454-1458.
    [7] G.-W. Kim, K.-T. Nam, and S.-M. Lee, "Visual servoing of a wheeled mobile robot using unconstrained optimization with a ceiling mounted camera," in 16th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN, August 26, 2007 - August 29, 2007 Jeju, Korea: Institute of Electrical and Electronics Engineers Inc., 2007, pp. 212-217.
    [8] B. C. B. Chou, "Aerial Visual Servo Control System for Ground Mobile Robot Navigation in Aerial - Ground Joint Operation," in Graduate Institute of Automation and Control. vol. Master of Science Taipei, Taiwan: National Taiwan University of Science and Technology, 2013.
    [9] K.-Y. Chang, P. Alexandra, H.-J. Hsu, and M.-F. R. Lee, "Trajectory Control Using Linear Control System on Mobile Robot," in Advanced Materials Research vol. (Volumes 383 - 390), 2010.
    [10] M. Liu, C. Pradalier, Q. Chen, and R. Siegwart, "A bearing-only 2D/3D-homing method under a visual servoing framework," in 2010 IEEE International Conference on Robotics and Automation, ICRA 2010, May 3, 2010 - May 7, 2010 Anchorage, AK, United states: Institute of Electrical and Electronics Engineers Inc., 2010, pp. 4062-4067.
    [11] C. Ivancsits, "Visual Navigation System for Small Unmanned Aerial Vehicles," in Graduate Institute of Automation and Control. vol. Master of Science Taipei, Taiwan: National Taiwan University of Science and Technology, 2010.
    [12] C.-H. L. Chen and M.-F. R. Lee, "Global path planning in mobile robot using omnidirectional camera," in 2011 International Conference on Consumer Electronics, Communications and Networks, CECNet 2011, April 16, 2011 - April 18, 2011 XianNing, China: IEEE Computer Society, 2011, pp. 4986-4989.
    [13] H. Lim, J. Park, D. Lee, and H. J. Kim, "Build Your Own Quadrotor: Open-Source Projects on Unmanned Aerial Vehicles," Robotics & Automation Magazine, IEEE, vol. 19, pp. 33-45, 2012.
    [14] H. N. Koivo and J. T. Tanttu, "Tuning of PID controllers: Survey of SISO and MIMO techniques," in IFAC Symposia Series, Singapore, Singapore, 1991, pp. 75-80.
    [15] K. J. Astrom, T. Hagglund, C. C. Hang, and W. K. Ho, "Automatic tuning and adaptation for pid controllers - survey," Control Engineering Practice, vol. 1, pp. 699-714, 1993.
    [16] R. W. Jones and M. T. Tham, "Maximum sensitivity based PID controller tuning: A survey and comparison," in 2006 SICE-ICASE International Joint Conference, Busan, Korea, Republic of, 2006, pp. 3258-3263.
    [17] C. Jung and W. Chung, "Design of test tracks for odometry calibration of wheeled mobile robots," International Journal of Advanced Robotic Systems, vol. 8, pp. 1-9.
    [18] L. Ojeda and J. Borenstein, "Reduction of Odometry Errors in Over-constrained Mobile Robots," in The International Society for Optical Engineering, Orlando, FL, United states, 2003, pp. 431-439.
    [19] X. Song and Y. Wang, "A novel model-based method for odometry calculation of all-terrain mobile robots," in World Congress on Intelligent Control and Automation (WCICA), Chongqing, China, 2008, pp. 581-586.
    [20] P. Campoy, J. F. Correa, I. Mondragon, C. Martinez, M. Olivares, L. Mejias, and J. Artieda, "Computer vision onboard UAVs for civilian tasks," Journal of Intelligent and Robotic Systems: Theory and Applications, vol. 54, pp. 105-135, 2009.
    [21] S. S. D. C. Botelho, P. Drews Jr, G. L. Oliveira, and M. D. S. Figueiredo, "Visual odometry and mapping for underwater autonomous vehicles," Valparaiso, Chile, 2009.
    [22] R. Garcia Garcia, M. A. Sotelo, I. Parra, D. Fernandez, and M. Gavilan, "2D visual odometry method for global positioning measurement," in 2007 IEEE International Symposium on Intelligent Signal Processing, WISP Alcala de Henares, Spain: Inst. of Elec. and Elec. Eng. Computer Society, 2007.
    [23] I. Parra, M. A. Sotelo, and L. Vlacic, "Robust visual odometry for complex urban environments," in IEEE Intelligent Vehicles Symposium, Eindhoven, Netherlands, 2008, pp. 440-445.
    [24] Y. Hara, H. Kawata, A. Ohya, and S. i. Yuta, "Map building for mobile robots using a SOKUIKI sensor -robust scan matching using laser reflection intensity," in 2006 SICE-ICASE International Joint Conference, Busan, Korea, Republic of, 2006, pp. 5951-5956.
    [25] Y.-H. Ji, S.-H. Hong, J.-B. Song, and J.-H. Choi, "DSM update for robust outdoor localization using ICP-based scan matching with COAG features of laser range data," in 2011 IEEE/SICE International Symposium on System Integration, SII 2011, Kyoto, Japan, pp. 1245-1250.
    [26] R. Ray, D. Banerji, S. Nandy, and S. N. Shome, "Keypoints based laser scan matching - A robust approach," in 2012 IEEE International Conference on Robotics and Biomimetics, ROBIO 2012 - Conference Digest Guangzhou, China: IEEE Computer Society, pp. 741-746.
    [27] P. Biber, "The Normal Distributions Transform: A New Approach to Laser Scan Matching," in IEEE International Conference on Intelligent Robots and Systems. vol. 3 Las Vegas, NV, United states: Institute of Electrical and Electronics Engineers Inc., 2003, pp. 2743-2748.
    [28] J. Li, J. Bao, and Y. Yu, "Localization for a rescue robot based on NDT scan matching," in Key Engineering Materials, Sanya, China, 2010, pp. 445-450.
    [29] T. Rofer, "Using histogram correlation to create consistent laser scan maps," in IEEE International Conference on Intelligent Robots and Systems, Lausanne, Switzerland, 2002, pp. 625-630.
    [30] M. Bosse and J. Roberts, "Histogram matching and global initialization for laser-only SLAM in large unstructured environments," in IEEE International Conference on Robotics and Automation, Rome, Italy, 2007, pp. 4820-4826.
    [31] Q. Qiu and J. Han, "Laser scan matching using multiplex histograms with feature components," in 2009 IEEE International Conference on Robotics and Biomimetics, ROBIO 2009, Guilin, China, 2009, pp. 275-280.
    [32] S. Nurmaini and S. Z. M. Hashim, "An embedded fuzzy type-2 controller based sensor behavior for mobile robot," in 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008, Kaohsiung, Taiwan, 2008, pp. 29-34.
    [33] H. Yu, J. Zhu, Y. Wang, M. Hu, and Y. Zhang, "Robot navigation based on fuzzy behavior controller," in 9th International Symposium on Neural Networks, Shenyang, China, pp. 365-375.
    [34] M. E. Sanjuan, "Neural-network based on-line adaptation of model predictive controller for dynamic systems with uncertain behavior," in ASME 2005 International Mechanical Engineering Congress and Exposition, Orlando, FL, United states, 2005, pp. 1033-1040.
    [35] X. Wang, Z.-G. Hou, A. Zou, M. Tan, and L. Cheng, "A behavior controller based on spiking neural networks for mobile robots," Neurocomputing, vol. 71, pp. 655-666, 2008.
    [36] W.-P. Lee, J. Hallam, and H. H. Lund, "Applying genetic programming to evolve behavior primitives and arbitrators for mobile robots," in IEEE Conference on Evolutionary Computation, ICEC, Indianapolis, IN, USA, 1997, pp. 501-506.
    [37] S. Panda and N. P. Padhy, "Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design," Applied Soft Computing Journal, vol. 8, pp. 1418-1427, 2008.
    [38] A. Pothou, S. Karamitsos, A. Georgopoulos, and I. Kotsis, "Assessment and comparison of registration algorithms between aerial images and laser point clouds," in Revue Francaise de Photogrammetrie et de Teledetection, 2 Avenue Pasteur, Saint-Mande Cedex, 94165, France, 2006, pp. 28-33.
    [39] A. Pothou, S. Karamitsos, A. Georgopoulos, and I. Kotsis, "Performance evaluation for aerial images and airborne laser altimetry data registration procedures," in Annual Conference of the American Society for Photogrammetry and Remote Sensing 2006: Prospecting for Geospatial Information Integration, Reno, NV, United states, 2006, pp. 1095-1107.
    [40] K. Hirota, "History and recent trends in soft computing: Research and application aspects in Japan," in IEEE International Conference on Intelligent Engineering Systems, Proceedings, INES, Budapest, Hungary, 1997, pp. 31-37.
    [41] A. Kamiya, S. J. Ovaska, R. Roy, and S. Kobayashi, "Fusion of soft computing and hard computing for large-scale plants: A general model," Applied Soft Computing Journal, vol. 5, pp. 265-279, 2005.
    [42] D. Tikk, L. T. Koczy, and T. D. Gedeon, "A survey on universal approximation and its limits in soft computing techniques," International Journal of Approximate Reasoning, vol. 33, pp. 185-202, 2003.
    [43] R. Nelson, Flight Stability and Automatic Control, 2nd ed., 1998.
    [44] F. ŠOLC, "Modelling and Control of a Quadrocopter," 30.12.2010 2010.
    [45] Z. Zhengyou, "Flexible camera calibration by viewing a plane from unknown orientations," in 1999 IEEE International Conference on Computer Vision. vol. 1, 1999, pp. 666-673.
    [46] J. Heikkila and O. Silven, "A four-step camera calibration procedure with implicit image correction," in 1997 Computer Vision and Pattern Recognition, 1997, pp. 1106-1112.
    [47] J. Y. Bouguet, "Camera calibration toolbox for matlab," 2010.
    [48] J. Salvi, X. Armangue, and J. Batlle, "A comparative review of camera calibrating methods with accuracy evaluation," Pattern Recognition, vol. 35, pp. 1617-1635, 2002.
    [49] M. A. Fischler and R. C. Bolles, "Random Sample Consensus: a paradigm for model fitting with applications to image analysis and automated cartography," Communications of the ACM, vol. 24, pp. 381-395, 1981.
    [50] L. A. Zadeh, "Fuzzy sets," Information and Control, vol. 8, pp. 338-353, 1965.

    QR CODE