研究生: |
張博詠 Po-Yung Chang |
---|---|
論文名稱: |
使用超寬頻無線測距與運動感測器之輪型機器人同步定位與建圖 Wheel Robot SLAM using UWB WIreless Sensor Network and Motion Sensor |
指導教授: |
高維文
Wei-Wen Kao |
口試委員: |
陳亮光
Liang-Kuang Chen 林紀穎 Chi-Ying Lin |
學位類別: |
碩士 Master |
系所名稱: |
工程學院 - 機械工程系 Department of Mechanical Engineering |
論文出版年: | 2015 |
畢業學年度: | 103 |
語文別: | 中文 |
論文頁數: | 65 |
中文關鍵詞: | 航位推算 、遞迴式最小平方法 、擴展式卡爾曼濾波器 、超寬頻 |
外文關鍵詞: | Dead Reckoning, Recursive Least Square, Extended Kalman Filter, Ultra wideband |
相關次數: | 點閱:567 下載:15 |
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隨著科技的進步,人力資源成本的提高,自動化設備、智慧型機器人的應用早已成為各家廠商、公司的研發重要方向之一,而其中室內機器人的應用如:家庭清掃、老人照護、居家保全、人機互動等是最為常見,但由於室內環境多樣性高且不固定,現今多數室內機器人都是以閃避障礙的方式來隨機移動,難以有更多元、高階的應用。
本篇論文將以輪型機器人為實驗載具,基於載具運動模型推導其狀態方程式,使用馬達編碼器及慣性感測器之量測資訊來做航位推算(Dead reckoning),以估測自身位置。此外使用超寬頻無線感測器網路(WSN)模組來測得機器人與環境相對應距離,將其量測值用最小平方法遞迴運算(RLS)可得載具在環境之相對位置。我們使用擴展式卡爾曼濾波器(EKF)整合所有感測器資料,藉此可以有效減少狀態方程所造成的累積誤差,此外建立無線測距量測值的挑選機制,剔除誤差過大之量測而得到較佳的修正量及位置,最後利用同步定位與建圖(SLAM)的概念來建立環境特徵位置。
本論文使用之無線測距模組為DecaWave公司所推出之TREK1000 Evaluation Kit,搭配其公司DW1000晶片的超寬頻(UWB)技術應用,可說是目前極為精準之無線定位模組,我們將在室外及室內分別做定位實驗以測試其性能表現。
Tech advances while the cost of human resources is gradually increasing as time goes by. Automation machine and intelligent robot application are not only important but inevitable for many places in the future. Meanwhile there are lots of company and factory working on it as one of their main development product.Nowadays we can find indoor robot with functions of cleaning, caring and guarding on the market, it usually move randomly with obstacle detection due to the complexity environment indoor. The robot application will be limited if that could not be solved.
In this thesis we use a two wheel robot to do our experiment. Base on robot dynamic model to derive state equation and getting data form encoder and motion sensor we can do the DR(Dead Reckoning)to predict self-position. Furthermore, using measurement equation and ranging sensor data to do RLS(Recursive Least Square), so that we get self-position relative to current environment. We intergrate all the data into EKF(Extended Kalman Filter), it helps to decrease the accumulate error caused by DR. On the other hand, set the threshold of ranging data could drastically improve EKF result. In the end, by using the concept of SLAM, we build feature position in the environment.
The ranging sensor we used here is called Trek1000 Evaluation Kit which made by DecaWave. It is a highly accuracy module compare to others because of its DW1000 chip and UWB(Ultra wideband) technique on it. Here we set indoor and outdoor experiment to examine the performance and discuss pros and cons.
[1]I. Guvenc, Z. Sahinoglu, S. Gezici, “Ultra-wideband Positioning System: Theooretical Limits, Ranging Algorithms, and Protocols”,Cambridge university Press, October 6, 2008
[2]T. Ye, M. Walsh, P. Haigh, J. Barton, A. Mathewson, B. O’Flynn, “Experimental Impulse Radio IEEE 802.15.4a UWB Based Wireless Sensor Localization Technology: Characterization, Reliability and Ranging” Trinity College Dublin, June 23-24,ISSC 2001
[3]M. Yavari, B. G. Nickerson, “Ultra Wideband Wireless Positioning Systems”, Technical Report TR14-230 March 27, 2014
[4]E. P. Herrera, R. Quiros, H. Kaufmann, “Analysis of a Kalman Approach for a Pedestrian Positioning System in Indoor Environments”, Vienna University of Technology
[5]L. Zwirello, C. Ascher, G. F. Trommer and T. Zwick, “Study on UWB/INS Integration Techniques.”, Karlsruher Institut, IEEE 2011
[6]G. Welch and G. Bishop, “An Introduction to the Kalman Filter”., Department of Computer Science University of North Carolina, July 24, 2006
[7]H. Durrant-Whyte and T. Bailey, “Simultaneous Localizatoin and mapping: Part I.”, Robotics and Automation Magazine, pp.99-110, June, 2006.
[8]T. Bailet and H. Durrant-Whyte, “Simultaneous Localization and Mapping: Part II.”, Robotics and Automation Magazine, pp.108-117, September, 2006
[9]楊智翔,導航機器人之研製,國立中央大學資訊工程學系碩士論文,桃園,2010
[10]張明達,結合雷射測距儀與車輛運動模型的同步定位與建圖,國立台灣科技大學機械工程系碩士論文,臺北,2011
[11]S. Fu, Z. G. Hou, G. Yang, “An Indoor Navigatoin System for Autonomous Mobile Robot using Wireless Sensor Network.”, IEEE International Conference, Okayama, Japan, March 26-29, 2009
[12]A. Benini, A. Mancini, A. Marinelli, S. Longhi, . “A Biased Extended Kalman Filter for Indoor Localizaton of a Mobile Agent using Low-Cost IMU and UWB Wireless Sensor NetWork”, Polytechnic University of Marche, Department of Information Engineering, Ancona, Italy
[13]石曜華,無線測距網路的定位研究,國立台灣科技大學機械工程系碩士論文,臺北,2013
[14]盛中德,應用無線射頻技術於農業環境定位系統之研究,國立中興大學,台中,2013
[15]朱建亮,網路型機器人的設計與單相機LSD同步定位與建圖的實現,國立台灣科技大學機械工程系碩士論文,臺北,2015
[16]黃良吉,GPS與感測器整合於三維地面車輛定位之應用,國立台灣科技大學機械工程系碩士論文,臺北,2006
[17]陳倫斌,加速度計與電子羅盤於行人定位之應用,國立台灣科技大學機械工程系碩士論文,臺北,2009
[18]劉建良,感測器於個人定位之步行分析與應用,國立台灣科技大學機械工程系碩士論文,臺北,2008
[19]張弘毅,整合GPS與MEMS感測器於自行車導航系統之應用,國立台灣科技大學機械工程系碩士論文,臺北,2010
[20]林子閔,超音波與感測器整合於導盲手杖定位之應用,國立台灣科技大學機械工程系碩士論文,臺北,2009
[21]許竣揚,單一相機同時定位與環境地圖建製於二維移動載具之實現,國立台灣科技大學機械工程系碩士論文,臺北,2011
[22]黃坤祥,GPS虛擬距離與加速度儀之卡門濾波器整合定位,國立台灣科技大學機械工程系碩士論文,臺北,2005
[23] 邱敬洲,以深度影像資料庫為基礎的嵌入式全向輪機器人同步定位與建圖, 國立台灣科技大學機械工程系碩士論文,臺北,2014
[24]李其真,基於資料庫影像與RGB-D相機影像之同步定位與建圖,國立台灣科技大學機械工程系碩士論文,臺北,2014
[25]劉家榮,以Android手機影像追蹤實現載具同時定位與建圖,國立台灣科技大學機械工程系碩士論文,臺北,2012
[26]A. Fitzgibbon, M. Pilu, R. B. Fisher, “Direct Least Square Fitting of Ellipse”, Tern Analysis and machine intelligence, vol. 21, no. 5, May 1999
[27]D. Dardari, A. Conti, U. Ferner, A. Giorgetti, M. Z. Win, “Ranging With Ultrawide Bandwidth Signals in Multipath Environment”, Proceedings of the IEEE, Vol.97, No.2, February 2009
[28]http://wshnt.kuas.edu.tw/network/s10/UWB.html