Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/91151
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dc.contributorChing-Chih Tsaien_US
dc.contributor蔡清池zh_TW
dc.contributor.author王孝慈zh_TW
dc.contributor.authorXiao-Ci Wangen_US
dc.contributor.other電機工程學系所zh_TW
dc.date2014zh_TW
dc.date.accessioned2015-12-10T05:49:46Z-
dc.identifierU0005-2104201511175300zh_TW
dc.identifier.citation[1] T. Fukao, H. Nakagawa, and N. Adachi, 'Adaptive tracking control of anonholonomic mobile robot,' IEEE Trans. Robot. Autom., vol. 16, no. 5, pp.609–615, Oct. 2000. [2] D. Maksarov and H. Durrant-Whyte, 'Mobile vehicle navigation in unknown environments: A multiple hypothesis approach,' Proc. Inst. Electr. Eng.—Control Application Theory, vol. 142, no. 4, pp. 385–400, Jul. 1995. [3] B. Triggs, 'Model-based sonar localisation for mobile robots,' Robot., Auton.Syst., vol. 12, no. 3/4, pp. 173–186, Apr. 1994. [4] S. Thrun , M. Bennewitz, W. Burgard, A. B. Cremers, F. Dellaert, D. Fox, D. Hahnel, C. Rosenberg, N. Roy, J. Schulte, D. Schulte, 'MINERVA: A second-generation museum tour-guide robot,' in Proc. IEEE International Conference on Robotics and Automation, pp 1999-2005,1999. [5] B. Graf, M. Hans, and R. D. Schraft, 'Mobile robot assistants,' IEEE Robotics and Automation Magzine, vol. 11, no.2, pp.67-77, 2004. [6] B. Jensen, G. Froidevaux, X. Greppin, A. Lorotte, L. Mayor, M. Meisser, G. Ramel, R. Siegwart,'The Interactive Autonomous Mobile System Robox,' in Proc. IEEE/RSJ International Conference on Intelligent Robots and System, pp. 1221-1227, 2002. [7] G. Kim, W. Chung, K.R. Kim, M. Kim, S. Han, R. H. Shinn, 'The autonomous tour-guide robot Jinny,' in Proc. IEEE/RSJ International Conference on Intelligent Robots and System, pp. 3450-3455, 2004. [8] P. S. Maybeck, Stochastic Models, Estimation, and Control, vol.1, New York, Academic Press, 1979. [9] F. Dellaert, D. Fox, W. Burgard, and S. Thrun, 'Monte Carlo Localization for Mobile Robots,' in Proc. IEEE Int. Conf. Robotics and Automation, May 1999, vol. 2, pp. 1322 –1328. [10] E. Kiriy and M. Buehler, 'Three-State Extended Kalman Filter for Mobile Robot Localization,' Tech. Rep., McGill University, Montreal, Canada, 2002. [11] C. C. Tsai, H. S. Lin, and J. C. Hsu, 'Ultrasonic Localization and Pose Tracking of an Autonomous Mobile Robot via Fuzzy Adaptive Extended Information Filtering,' IEEE Trans. Instrum. Meas., vol. 57, no. 9, September 2008 [12] R. Siegwart and I. R. Nourbakhsh, Introduction to Autonomous Mobile Robots. The MIT Press. [13] N. Ganganath and H. Leung, 'Mobile Robot Localization Using Odometry and Kinect Sensor.' in Proc. IEEE Int. Conf. ESPA, pp. 91 –94, 2012. [14] C. C. Tsai, Y. C. Wang, F. C.. Tai, 'Global Localization Using Dead-Reckoning and KINECT Sensors for Anthropomorphous Two-Armed Robots with Omnidirectional Mecanum Wheels,' in Proc. of 2012 National Symposium on System Science and Engineering, National Taiwan Ocean University, Keelung,Taiwan, June 17-18, 2012. [15] A.G.O. Mutambara, Decentralized estimation and control for multisensory systems, CRC Press LLC, 1998. [16] S. Thrun, Y. Liu, D. Koller, A. Y. Ng, Z. Ghahramani, and H. Durrant-Whyte,'Simultaneous localization and mapping with sparse extended information filters,' International Journal of Robotics Research, Vol. 23, No.7, pp.693-716.2004. [17] H. H. Lin, C. C. Tsai, 'Ultrasonic Localization and Pose Tracking of an Autonomous Mobile Robot via Fuzzy Adaptive Extended Information Filtering,' IEEE Transactions on Instrumentation and Measurement, vol.57, no.9,pp.2024-2034,2008. [18] S. E. Webster, J. M. Walls, L. L. Whitcomb, R. M. Eustice, 'Decentralized Extended Information Filter for Single-Beacon Cooperative Acoustic Navigation:Theory and Experiments,' IEEE Transactions on Robotics, vol. 29, no. 4, pp.957 – 974, 2013. [19] C. C Tsai, Y. S. Huang, H. H. Lin, Y. C., 'Map-based posture tracking of a nursing-care walking assistant using laser scanner,' in Proceedings of 2005 Chinese Automatic Control Conference, Nov.18-19, 2005. [20] Y. R. Lee, System Design, Intelligent Adaptive Motion Control for Mecanum Wheeled Omnidirectional Robots, Master thesis, Department of Electrical Engineering, National Chung-Hsing University, 2011. [21] PrimeSense Ltd., Palo Alto, CA 94301, USA, The Prime-Sensor Reference Design, 1.08 edition, 2010. [22] N. Karlsson, E. Di Bernardo, J. Ostrowski, L. Goncalves, P. Pirjanian, and M.E. Munich, 'The vSLAM Algorithm for Robust Localization and Mapping, ' in Proc. IEEE International Conference on Robotics and Automation, 2005.zh_TW
dc.identifier.urihttp://hdl.handle.net/11455/91151-
dc.description.abstractThis thesis presents new techniques for two multisensorial extended information filter (MEIF) methods for global pose localization of an autonomous omnidirectional mobile robot (AOMR) driven by omnidirectional Mecanum wheels in indoor environments by fusing measurements from one KINECT sensor, one laser scanner and four encoders mounted on the omnidirectional Mecanum wheels. MEIF and fuzzy MEIF methods are proposed to achieve multisensorial global pose initialization and tracking for the AOMR with nonlinear measurement models corrupted with time-vary noise characteristics. With the measurements of the azimuth angles and distances of any neighboring two KINECT-based landmarks, two static global pose initialization algorithms are presented to estimate both start-up position and orientation of the AOMR. Once the initial global pose is roughly determined, both MEIF and fuzzy MEIF dynamic pose tracking approaches are proposed to fuse the measurements from the four encoders, KINECT and laser scanner, in order to continuously keep track of the accurate moving poses of the robot at slow speeds less than 100 cm/sec. Through simulations and experimental results, the proposed methods are shown capable of obtaining accurate estimation of both unknown initial and continuous moving positions and orientations of the robot.en_US
dc.description.abstract本篇論文的主旨是針對裝置四個麥卡輪的全方位移動機器(AOMR),提出兩種多感測推廣型資訊濾波方法,用以融合由一個 KINECT 感測器,一個雷射掃描儀和四個編碼器的測量值,達成新型的全域定位技術。為解決具非線性量測模型與時變雜訊等特性的全方位移動機器定位問題,多感測推廣型資訊濾波和模糊多感測推廣型資訊濾波方法被提出來達成多感測器融合的全域姿態初始化和姿態追蹤。其次,提出兩種靜態全域姿態初始化演算法,僅使用 KINECT 感測器測量任意兩個相鄰的地標及其與全方位移動機器的距離和角度等量測值,估測全方位移動機器的起始位置和方位角。一旦全方位移動機器初始位置大致確定,多感測推廣型資訊濾波和模糊多感測推廣型資訊濾波動態姿態追踪的方法可用以融合四個編碼器、KINECT 和雷射掃描儀的測量資訊,全方位移動機器在小於 100cm /秒的低速下,可精確完成地估測連續位置與方位角追蹤。模擬與實驗結果驗證所提出方法可以精確的估測全方位移動機器在室內空間中的起始位置與姿態以及連續移動的位置和方位角追蹤。zh_TW
dc.description.tableofcontents致謝辭 ............................................................................ i 中文摘要 ..............................................................................................ii Abstract .............................................................................................. iii Contents........................................................................................... iv List of Figures ................................................................................... viii List of Tables .......................................................................................xii List of Nomenclature...................................................................... xiii List of Acronyms .................................................................................... xiv Chapter 1 Introduction ....................................................................... 1 1.1 Introduction ................................................................................. 1 1.2 Literature Survey ............................................................................ 3 1.2.1 Related Work of Localization for Mobile Robots.................................. 3 1.2.2 Related Work of Localization and Mapping for Mobile Robots ........... 4 1.3 Motivation and Objectives ........................................ 5 1.4 Main contributions ................................................ 6 1.5 Thesis Organization .................................................. 7 Chapter 2 System Description of the Experimental Autonomous Omnidirectional Mobile Robot (AOMR) .................................................... 8 2.1 Introduction ....................................................8 2.2 Introduction to the Head System ................................................ 12 2.3 Brief Description of Mechatronic Structure of the Mobile Base ............. 13 2.4 Brief Description of the Mobile Base Controller ........................................... 16 2.4.1 SoPC Architecture.............................. 16 2.4.2 System Architecture .................................................... 21 2.4.3 Wire Connectors .................................................... 23 2.4.4 QEP Circuitry .................................................... 23 2.4.5 Digital-to-Analog Converter: MCP4822............................................ 24 2.4.6 Signal Flow of the Mobile Base Controller........................................ 26 2.5 KINECT sensor ................................................... 26 2.5.1 The PrimeSensor ................................................... 28 2.5.2 KINECT Calibration .................................................... 29 2.6 Laser scanner.............................................30 2.7 Kinematic Modeling and Odometry ................................................ 34 2.7.1 Kinematics Model .................................................... 35 2.7.2 Odometry ..................................................... 37 2.7.3 Point stabilization .................................................... 38 2.8 KINECT-based Detection of Artificial Landmarks ....................... 40 2.9 Laser Scanner Detection of Walls ........................................ 43 2.10 Experimental Results and Discussion...........................................45 2.10.1 Point Stabilization and Odometry Experiment ................................. 45 2.10.2 KINECT Landmark Detection Experiment ...................................... 48 2.10.3 Laser Scanner Detection Experimental ............................................ 50 2.11 Concluding Remarks ............................................. 51 Chapter 3 Multisensorial EIF-based Global Localization .............................. 52 3.1 Introduction ......................................................52 3.2 Multisensorial EIF and EKF Algorithms ..................................... 53 3.2.1 Multisensorial EIF (MEIF) algorithm ....................................... 53 3.2.2 Multisensorial EKF (MEKF) algorithm .................................... 55 3.2.3 MEKF Derivation from MEIF .............................................. 56 3.3 Pose Initialization ................................................. 61 3.3.1 Static Global Pose Initialization Using KINECT and Two Landmarks61 3.4 Dynamic Pose Tracking ............................................... 66 3.5 Simulation Results and Discussion ............................................. 70 3.5.1 Simulations of the Proposed Pose Initialization Method .................... 70 3.5.2 Simulation of the Proposed Static Pose Tracking ............................... 71 3.5.3 Simulation of the Proposed Dynamic Pose Tracking ......................... 75 3.6 Experimental Results and Discussion .............................................. 78 3.7 Concluding Remarks .............................................. 81 Chapter 4 Fuzzy Multisensorial EIF-based Global Localization ............................ 82 4.1 Introduction .......................................... 82 4.2 Multisensorial FEIF and FEKF Algorithms ................................. 83 4.2.1 FMEIF algorithm ............................................ 83 4.2.2 FMEKF algorithm............................. 85 4.2.3 Fuzzy Tuner ..................................................... 87 4.2.4 Fuzzy MEIF (FMEIF) Algorithm ...................................................... 90 4.3 Improved Static Global Pose Initialization with KINECT ........... 91 4.4 FMEIF-based Global Pose Initialization Algorithm ............................. 92 4.5 FMEIF Global Dynamic Pose Tracking ......................................... 96 4.6 Simulations and Discussion ........................................ 98 4.6.1 Simulations of the proposed pose initialization Method ..................... 98 4.6.2 FMEIF-based Global Pose Initialization ............................. 99 4.6.3 FMEIF Dynamic Pose Tracking ....................................... 106 4.7 Experimental Results and Discussion ............................................. 112 4.8 Concluding Remarks .................................................... 115 Chapter 5 Conclusions and Future Work .................................. 116 5.1 Conclusions ............................... 116 5.2 Future Work .................................................... 118 References ................................................... 119zh_TW
dc.language.isoen_USzh_TW
dc.rights不同意授權瀏覽/列印電子全文服務zh_TW
dc.subjectnoen_US
dc.subjectzh_TW
dc.title自主全向移動機器人之模糊多感測推廣型資訊濾波器全域定位與姿態追蹤zh_TW
dc.titleFuzzy Multisensorial EIF-based Global Localization and Pose Tracking for Autonomous Omnidirectional Mobile Roboten_US
dc.typeThesis and Dissertationen_US
dc.date.paperformatopenaccess2018-05-11zh_TW
dc.date.openaccess10000-01-01-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextwith fulltext-
item.grantfulltextrestricted-
item.cerifentitytypePublications-
item.languageiso639-1en_US-
item.openairetypeThesis and Dissertation-
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