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Localization of an Autonomous Mobile Robot Using Fuzzy Extended Information Filters
|關鍵字:||Extended Information filter;增廣訊息濾波;localization;fuzzy logics;sensor fusion;mobile robot;ultrasonics;定位;自動導航車;感測器;超音波||出版社:||電機工程學系所||引用:|| J. J. Leonard and H. F. Durrant-Whyte, Directed sonar sensing for mobile robot navigation. Boston/London/Dordrecht: Kluwer, 1992.  D. Maksarov and H. Durrant-Whyte, “Mobile vehicle navigation in unknown environments: A mutiple hypothesis approach,” IEEE Proceeding Control Application Theory, vol. 142, no. 4, pp.385-400, 1995.  A. M. Sabatini, “A digital signal processing technique for compensating ultrasonic sensors,” IEEE Transactions on Instrumentation and Measurement, vol. 44, no. 4, pp.869-874, 1995.  B. Triggs, “Model-based sonar localization for mobile robots,” Robotics, Autonomous System, vol.12, pp.173-186, 1994.  A. G. O. Mutambara, Decentralized estimation and control for multisensor systems, CRC Press LLC, 1998.  A. Gelb, Applied optimal estimation, The MIT Press, 1974.  F. L. 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本論文的目的是在探討應用模糊增廣訊息濾波策略(Fuzzy extended information filtering)於自動導航車定位之方法與技術。為偵測及避免非線性濾波技術的發散問題，本文提出由模糊調諧器(Fuzzy tuner)及指數加權訊息濾波技術(Extended information filtering)所組成的模糊增廣訊息濾波技術，並對其主要特性作詳盡之研究。
文中提出四種自動導航車定位系統結合模糊增廣訊息濾波技術之信號處理方法，來增進定位估測的精確度與強健度，其一是使用一個超音波定位系統，由兩個超音波發射器及三個接收器之超音波飛行時間(Time of flight)測量法，然後配合模糊增廣訊息過濾(FEIF)方法，用來改進機器人的動態的定位與定向姿態估計的準確。其二是使用一台雷射掃描器及至少用3 個反光板之三角測量法(Three-point triangulation) 被提出，找到一個機器人的最初姿勢，然後配合模糊增廣訊息過濾方法，用來改進機器人的姿態估計的準確。其三是使用一個超音波定位系統，由兩個超音波發射器及三個接收器之超音波飛行時間測量法，然後並使用一台雷射掃描器配合模糊增廣訊息過濾方法用來改進機器人的動態的定位與定向姿態估計的準確。其四是使用一個主動式RFID定位系統，利用從四個電子標籤 (Tag) 所發射訊號強度RSSI數據，經由Reader接收後分別被轉化成距離，再使用所提出的最小平方法 (Least square method)，可估測出導覽機器人的最初姿勢; 並使用一台雷射掃描器配合模糊增廣訊息過濾方法，來追蹤機器人的動態姿態估計的準確。
This dissertation presents methodologies and techniques for localization of an autonomous mobile robot (AMR) using the fuzzy extended information filtering (FEIF) scheme. The FEIF, composed of a fuzzy tuner and the exponential weighted extended information filter (EIF), is presented in order to detect and avoid the nonlinear filter divergence problems. The main features of the FEIF scheme are studied in some details.
Four novel localization systems together with the FEIF signal processing method are proposed to improve the accuracy and robustness of pose estimation for the AMR. The first one establishes on a novel ultrasonic localization system which consists of two ultrasonic transmitters and three receivers, and uses the FEIF to improve the estimation of both the static and dynamic position and orientation of the AMR. A fuzzy extended information filter is presented to improve estimation accuracy and robustness for the proposed localization system, while the system lacks of sufficient information of complete models or the process and measurement noise varies with time. The second one investigates pose estimation and tracking of the AMR using a laser scanner with at least three retro-reflectors. A three-point laser triangulation method is presented to find an initial posture of the robot and then a FEIF method is used to improve the accuracy of the robot's pose estimation in motion. The third one employs a FEIF approach to improving global localization of an indoor AMR with ultrasonic and laser scanning measurements. The fourth one applies a least-squares method and a FEIF scheme to global pose estimation of an tour-guide robot with radio-frequency-identification (RFID) and laser scanning measurements.
In these four methods, not only the static position and orientation of the robot can be determined uniquely with respect to an inertial frame of reference, but also the moving pose estimates can be obtained by the FEIF-based sensor fusion approach. Numerous simulation and experimental results are provided to show the effectiveness and merits of the proposed localization systems and the FEIF signal processing method.
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