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系統識別號 U0002-3006200921074700
中文論文名稱 應用擴展式卡爾曼濾波器於輪型機器人循跡和動態避障之研究
英文論文名稱 Extended Kalman Filter for the Path Tracking and Dynamic Obstacle Avoidance of a Differential Mobile Robot
校院名稱 淡江大學
系所名稱(中) 電機工程學系碩士班
系所名稱(英) Department of Electrical Engineering
學年度 97
學期 2
出版年 98
研究生中文姓名 盧廷星
研究生英文姓名 Ting-Xing Lu
電子信箱 695460245@s95.tku.edu.tw
學號 695460245
學位類別 碩士
語文別 中文
第二語文別 英文
口試日期 2009-06-16
論文頁數 44頁
口試委員 指導教授-黃志良
委員-施慶隆
委員-洪敏雄
委員-楊智旭
委員-王銀添
中文關鍵字 模糊分散滑動控制  擴展式卡爾曼濾波器  差速輪型機器人  軌跡追蹤  靜(動)態避障 
英文關鍵字 Differential mobile robot (DMR)  Navigation  Extended Kalman filter  Path planning using piecewise lines  Ultrasonic sensor 
第三語言關鍵字  
學科別分類 學科別應用科學電機及電子
中文摘要 本論文探討差速輪型機器人在世界座標中規劃其折線運動路徑,並應用左右兩輪之伺服馬達的編碼器與擴展式卡爾曼濾波器估算其當下的世界座標值,以進行軌跡追蹤,並以超音波感測器偵測相關靜(動)態障礙物,達成避障的功能。基於建築物的特徵性、省能源的操控性及規劃的方便性,我們以折線組成相關的運動路徑。此外,為了達成快速追蹤及省能量的操作,提出了三步驟的軌跡追蹤模式分別為趨近、微調和慣性導引步驟。差速輪型機器人之操控(或軌跡追蹤)並不像車型輪型機器人,能直接以前輪控制方向及後輪控制前進或後退之速度,是故本論文亦推導其操控方法與運動路徑之關係。由於差速輪型機器人之運動學為非線性,也會受到外界雜訊干擾(例如,地面不平造成輪子與地面摩擦力不足打滑或機構鬆脫),以致造成運動軌跡與已規劃的軌跡不同,有鑑於此,我們將以擴展式卡爾曼濾波器估測差速輪型機器人之位置及方位。根據所估測的姿態(即位置及方位)和規劃的路徑,應用所建議的軌跡追蹤模式及馬達速度的模糊分散滑動控制,達成所設定的任務。本論文亦將採用分散主動式嵌入視覺系統,量測其於影像平面座標的運動軌跡,並以類神經網路轉換至世界座標值,然後與應用擴展式卡爾曼濾波器所估算的世界座標值,進行比較。
英文摘要 From the viewpoint of the constraint of house architecture, the energy consumption of differential mobile robot (DMR), and the ease of trajectory planning, a desired (or planning) trajectory made up by a set of piecewise stretching lines is addressed. Then we design the navigation with the following three modes: approach mode, fine-tune mode, and inertia navigation mode, such that the DMR can fast move to the desired trajectory in the manner of energy saving. For the localization of a DMR, the extended Kalman filter (EKF) is employed to estimate the posture of the DMR. Based on the estimated posture and the planning path, a fuzzy decentralized variable structure velocity control of the left and right wheels of the DMR is employed to accomplish the assigned task. The avoidances of various static obstacles with different shapes and sizes, and on different locations of the desired trajectory, and dynamic obstacle in the perpendicular direction of the desired trajectory are achieved by using seven ultrasonic rangers on the DMR and the designed strategy for obstacle avoidance. Finally, the localization between an EKF and a distributed active embedded vision system are compared by various experiments.
第三語言摘要
論文目次 目錄
中文摘要 I
英文摘要 II
目錄 III
圖目錄 V
表目錄 VII
第一章 緒論 1
1.1 研究背景及動機 1
1.2 研究目的與方法 2
1.3 論文架構 3
第二章 系統描述及任務陳述 5
2.1 系統描述 5
2.2 任務陳述 12
第三章 差速輪型機器人之二維運動學及操控 14
第四章 擴展式卡爾曼濾波器及模糊分散式滑動控制器 19
4.1 擴展式卡爾曼濾波器 19
4.2 模糊分散式滑動控制器 21
第五章 實驗結果與討論 26
5.1 實驗預備 26
5.2 實驗結果 29
第六章 結論與未來展望 39
6.1 結論 39
6.2 未來展望 39
參考文獻 41
圖目錄
圖2.1 差速輪型機器人實體圖及硬體架構圖 7
圖2.2 實驗板VP2812實體圖及核心架構圖 9
圖2.3 Maxon直流伺服馬達 10
圖2.4 馬達驅動IC-L298N 10
圖2.5 SRF05訊號時序圖 11
圖2.6 SRF05距離量測結果圖 12
圖3.1 DMR於世界座標位置角度示意圖 14
圖3.2 折線軌跡追蹤的策略圖 17
圖3.3 微調模式的方法圖 18
圖4.1 模糊滑動控制(FSMC)控制第i軸DMR輪子轉速方塊圖 24
圖4.2 FSMC的歸屬函數圖 24
圖5.1 DMR的軌跡追蹤流程圖 27
圖5.2 分散主動式視覺影像系統圖 28
圖5.3 影像特徵及影像平面的姿態計算圖 28
圖5.4 DMR於無負載的模糊分散式滑動控制響應圖 31
圖5.5 DMR於實際地面的模糊分散式滑動控制響應圖 32
圖5.6 超音波距離感測器擺放於DMR的位置圖 35
圖5.7 DMR動態避障策略圖 35
圖5.8 DMR避障流程圖 36
圖5.9 DMR遇單靜態障礙的軌跡追蹤及避障之響應圖 37
圖5.10 DMR遇雙靜態障礙的軌跡追蹤及避障之響應圖 38
圖5.11 DMR遇動態障礙的軌跡追蹤及避障之響應圖 38
表目錄
表2.1 輪型機器人規格表 8
表2.2 L298N的訊號功能表 11
表4.1 模糊變數規則表 25
表4.2 正規化模糊規則表 25
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[2] K. T. Song and W. H. Tang, “Environment perception for a mobile robot using double ultrasonic sensors and a CCD camera,” IEEE Trans. Ind. Electronics, vol. 43, no. 3, pp. 372-379, May 1996.
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[6] A. M. Ladd, K.E. Bekris, A.P. Rudys, D.S. Wallach and L. E. Kavraki, “ On the feasibility of using wireless Ethernet for indoor localization,” IEEE Trans. Robotics & Autom., vol. 20, no. 3, pp. 555-559, Jun. 2004.
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[10] C. L. Hwang and N. W. Chang, “Fuzzy decentralized sliding-mode control of a car-like mobile robot in distributed sensor network spaces,” IEEE Trans. Fuzzy Syst., vol. 16, no. 1 pp. 97-109, Feb. 2008.
[11] C. L. Hwang and C. Y. Shih, “A distributed active-vision network-space approach for trajectory tracking and obstacle avoidance of a wheeled robot,” IEEE Trans. Ind. Electronics, vol. 56, no. 3, pp. 846-855, Mar. 2009.
[12] L. Jetto, S. Longhi and G. Venturini, “ Development and experimental validation of an adaptive extended Kalman filter for the location of mobile robots,” IEEE Trans. Robot. & Automat., vol. 15, no. 2, pp. 219-229, Apr. 1999.
[13] W. R, Williamson, M. F. Abdel-Hafe, I. Rhee, E.J. Song, J. D. Wolfe, D. F. Chichka and J. L. Speyer, “An instrumentation system applied to formation flight,” IEEE Trans. Contr. Syst. Technol., vol. 15, no. 1, pp. 75-84, Jan. 2007.
[14] D. M. Bevly and B. Parkinson,“ Cascade Kalman filters for accurate estimation of multiple biases, dead-reckoning navigation, and full state feedback control of ground vehicles,” IEEE Trans. Contr. Syst. Technol., vol. 15, no. 2, pp.19 9-208, Mar. 2007.
[15] K. B. Purvis, K.J. Astrom and M. Mhammash, “Estimation and optimal configurations for localization using cooperative UAVs,” IEEE Trans. Contr. Syst. Technol., vol. 16, no. 5, pp. 947-958, Sep. 2008.
[16] C. A. Lightcap and S. A. Banks, “An extended Kalman filter for real-time estimation and control of a rigid-link flexible-joint manipulator,” IEEE Trans. Contr. Syst. Technol., vol. 17, 2009 (to be appeared).
[17] M. J. Daigle, X. D. Koutsoukos and G. Biswas, “Distributed diagnosis in formations of mobile robots,” IEEE Trans. Robotics, vol. 23, no. 2, pp.353-369, Apr. 2007.
[18] S. Han. H. S. Lim and J. M. Lee, “An efficient localizations scheme for a differential-driving mobile robot based on RFID system,” IEEE Trans. Ind. Electron., vol. 54, no. 6, pp. 3362-3369, Dec. 2007.
[19] W.Tsui, M. S. Masmoudi, F. Karray, I. Song and M. Masmoudi,“Soft-computing-based embedded design of an intelligent wall/lane following vehicle,” IEEE/ASME Trans. Mechatron., vol. 13, no. 1, pp. 125-135, Feb. 2008.
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[21] C. L. Hwang, C. C. Liu and T. X. Lu, “Distributed active embedded vision system for the navigation of differential mobile robots,” the 10th International Conference of Automation Technology, Cheng-Kung University, Tainan, Taiwan, June 27th-29th, 2009.
[22] C. L. Hwang and N. W. Chang, “Fuzzy decentralized sliding-mode control of a car-like mobile robot in distributed sensor network spaces,” IEEE Trans. Fuzzy Syst., vol. 16, no. 1 pp. 97-109, Feb. 2008.
[23] Homepage of VP2812: http://www.vp-ic.com
[24] Homepage of maxonmotor: http://www.maxonmotor.com.tw
[25] Homepage of L298N:
http://www.datasheetcatalog.com/datasheets_pdf/L/2/9/8/L298N.shtml
[26] Homepage of SRF05:
http://www.robot-electronics.co.uk/htm/srf05tech.htm
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