跳到主要內容

臺灣博碩士論文加值系統

(44.192.49.72) 您好!臺灣時間:2024/09/19 22:30
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:廖天佑
研究生(外文):Tien-Yu Liao
論文名稱:基於雷射測距儀之車輛定位及地圖建構系統設計
論文名稱(外文):Vehicle Localization and Mapping System Design Based on a Laser Range Finder
指導教授:吳炳飛吳炳飛引用關係
指導教授(外文):Bing-Fei Wu
學位類別:碩士
校院名稱:國立交通大學
系所名稱:電機與控制工程系所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:英文
論文頁數:80
中文關鍵詞:定位方位推估車輛雷射測距儀全球定位系統車輛速度感測器車輛方向盤轉角感測器微控制器車輛運動模型感測器模型觀測模型
外文關鍵詞:LocalizationDead reckoningVehicleLaser range finderGlobal positioning system (GPS)Vehicle velocity sensorVehicle steering wheel sensorMicroAutoBoxVehicle motion modelSensor modelObservation model
相關次數:
  • 被引用被引用:0
  • 點閱點閱:318
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
  本論文所論述的主要貢獻是利用各種感測器的搭配組合與應用,以雷射測距儀為主要感知元件,當車輛在行駛時,對於其周圍環境進行偵測以及障礙物的地圖建構,並且在未知環境中進行定位,將其應用於智慧車(Taiwan iTS-1)的運作上。
  實驗方法是在車上配備各種相關感測器,例如雷射測距儀、全球定位系統、微控制器、車輛方向盤轉角感測器以及車速感測器,利用上述之感測器互相搭配運作,以達到全自動的車輛定位、環境辨識以及地圖建構之功能。
  系統運作之初,利用全球定位系統給予車輛初始絕對座標值以及初始方向,之後即利用車速感測器以及車輛方向盤轉角感測器所提供的車輛運動資訊,進行估測下一個時刻的車輛狀態,以獲得其運動到達之位置及方位。在此車輛行進之同時,利用雷射測距儀對環境進行偵測,主動將未知之環境障礙物的分佈情形測得並標示於內建之環境地圖上,以達成自動環境偵測之功能。
  全球定位系統只作為位置參考訊號的情形下,本系統利用車輛運動模型(vehicle motion model)的演算法進行方位推估(dead reckoning)運算。並同時在車輛行進當中,以觀測模型(observation model)對於周圍環境進行偵測以及將環境障礙物紀錄於內建地圖上。實驗結果即能夠表示此方法的可行性效益。
  The main purpose and contribution of this thesis are to combine and apply several sensors when the vehicle is moving. The system can detect the surrounding environment actively and build the maps of obstacles based on a laser range finder, and the localization for the intelligent vehicle, Taiwan iTS-1, in unknown environments.
  Various kinds of sensors are located in the vehicle, such as the laser range finder, the global positioning system (GPS), MicroAutoBox, the vehicle steering wheel sensor, and the vehicle velocity sensor to achieve the fully automatic vehicle localization, environment distinguish and build the maps.
  In the first stage of this system, the initial coordinates and initial direction of the vehicle based on the global coordinates system are given in the environmental measurement by GPS. The next stage uses the information of the vehicle from the vehicle velocity sensor and the steering wheel sensor. The vehicle states can be estimated to obtain the position and direction of the vehicle after movements. A laser range finder is used to detect the environmental status actively as a vehicle moving, and the unknown obstacles can be easily obtained in order to build the maps automatically.
  When the GPS signal is as the position reference signal, the vehicle motion model is used to compute states of the vehicle at the period based on dead reckoning algorithm without GPS measurements. And the proposed system can detect the surrounding environment and the build map when the vehicle moving based on the observation model. The experimental results show the effectiveness of this approach.
ABSTRACT (CHINESE) i
ABSTRACT (ENGLISH) ii
ACKNOWLEDGEMENT iv
TABLE OF CONTENTS v
LIST OF FIGURES viii
LIST OF TABLES xi
Chapter 1 Introduction 1
1.1 Introduction 1
1.2 Intelligent transportation system (ITS) 3
1.3 Motivation 4
1.4 Related work 6
1.5 System architecture 6
1.6 Brief sketch of contents 8
Chapter 2 Main Components 10
2.1 Taiwan iTS-1 intelligent vehicle platform 10
2.2 Laser range finder 12
2.3 MicroAutoBox of dSPACE 13
2.4 RTK-DGPS 14
2.5 Vehicle velocity sensor 18
2.6 Vehicle steering wheel sensor 19
2.7 PC-based human machine interface 20
Chapter 3 An Important Perception Sensor : Laser Range Finder 23
3.1 Operating principle 24
3.2 Improvement of the quality of the transmitted data 24
3.3 Making the reliable scanning distance 25
3.4 Choice the scanning angles to makes the measurements more correct 27
3.5 Enhance the transmission efficiency 29
3.5.1 The best examination instrument for the logic circuit: logic analyzer 35
3.6 Adaptive detection efficiency 36
3.7 Related problems 39
3.8 The features and advantages of laser range finder system 41
Chapter 4 Vehicle Motion, Perception and Observation 43
4.1 Vehicle motion model 43
4.2 Sensor model 44
4.3 Observation model 54
Chapter 5 Experimental Results 56
5.1 The basal experiment of the laser range finder 56
5.2 The vehicle positions dead reckoning and obstacles detection 62
Chapter 6 Conclusions and Future Work 72
6.1 Conclusions 72
6.2 Future work 73
Reference 74
Appendix 78
Appendix A : Laser range finder [18-19] 78
Appendix B : The algorithms about to determine the sensor model [5] 80
[1]Bing-Fei Wu, Tsen-Wei Chang, Jau-Woei Perng, Hsin-Han Chiang, Chao-Jung Chen, Tien-Yu Liao, Shinq-Jen Wu and Tsu-Tian Lee, “Design and Implementation of the Intelligent Stop and Go System in Smart Car, TAIWAN iTS-1”, Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Taipei, Taiwan, pp. 2073-2077, Oct. 8-11, 2006.
[2]Hsin-Han Chiang, Tien-Yu Liao, Jau-Woei Perng, Bing-Fei Wu and Tsu-Tian Lee, “Design and Implementation of an Intelligent Vehicle Platform for Developing a Driver Assist System”, Proceedings of the 14th National Conference on Fuzzy Theory and Its Applications, Kaohsiung, Taiwan, Dec. 14-15, 2006.
[3]Bing-Fei Wu, Tsu-Tian Lee, Hsin-Han Chang, Jhong-Jie Jiang, Cheng-Nan Lien, Tien-Yu Liao, and Jau-Woei Perng, “GPS Navigation Based Autonomous Driving System Design for Intelligent Vehicles”, accepted by IEEE International Conference on Systems, Man and Cybernetics, Montreal, Canada, Oct. 7-10, 2007.
[4]Tien-Yu Liao, Jau-Woei Perng and Bing-Fei Wu, “On-Line Vehicle Localization and Mapping Design Based on 2D Laser Range Finder”, Proceedings of National Symposium on System Science and Engineering (NSSSE ‘07), Taipei, Taiwan, June 21-22, 2007.
[5]Sebastian thrun, Wolfram Burgard and Dieter Fox, “Probabilistic Robotics”, Book, MIT Press, 2005.
[6]M.W.M. Gamini Dissanayake, Paul Newman, Steven Clark, Hugh F. Durrant-Whyte and M. Csorba, “A Solution to the Simultaneous Localization and Map Building (SLAM) Problem”, IEEE Transactions on Robotics and Automation, Vol. 17, No. 3, pp. 229-241, June, 2001.
[7]Hugh Durrant-Whyte, “Localisation, Mapping and the Simultaneous Localisation and Mapping (SLAM) Problem”, SLAM Summer School 2002, 2002.
[8]Hans P. Moravec and Alberto Elfes, “High Resolution Maps from Wide Angle Sonar”, Proceedings of 1985 IEEE International Conference on Robotics and Automation, Vol. 2, pp. 116-121, March, 1985.
[9]Alberto Elfes, “Using Occupancy Grids for Mobile Robot Perception and Navigation”, Computer, Vol. 22, Issue 6, pp. 46-57, June, 1989.
[10]Wolfram Burgard, Dieter Fox, Daniel Hennig and Timo Schmidt, “Position Tracking with Position Probability Grids”, Proceedings of the First Euromicro Workshop on Advanced Mobile Robot, pp. 2-9, Oct. 9-11, 1996.
[11]Wolfram Burgard, Dieter Fox, Daniel Hennig and Timo Schmidt, “Estimating the Absolute Position of a Mobile Robot Using Position Probability Grids”, Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI-96), 1996.
[12]Dieter Fox, Wolfram Burgard, Sebastian Thrun and Armin B. Cremers, “Position Estimation for Mobile Robots in Dynamic Environments”, Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), Madison, Wisconsin, 1998.
[13]Jens-Steffen Gutmann, Wolfram Burgard, Dieter Fox and Kurt Konolige, “An Experimental Comparison of Localization Methods”, Proceedings of the 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems, Victoria, B.C., Canada, Vol. 2, pp. 736-743, Oct. 13-17, 1998.
[14]Dieter Fox, Wofram Burgard and Sebastian Thrun, “Markov Localization for Mobile Robots in Dynamic Environments”, Journal of Artificial Intelligence research, pp. 391-427, Nov. 1999.
[15]Dieter Fox, Wolfram Burgard, Frank Dellaert and Sebastian Thrun, “Monte Carlo Localization: Efficient Position Estimation for Mobile Robots”, Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99), Orlando, Florida, 1999.
[16]Frank Dellaert, Dieter Fox, Wolfram Burgard and Sebastian Thrun, “Monte Carlo Localization for Mobile Robots”, Proceedings of 1999 IEEE International Conference on Robotics and Automation, Vol. 2, pp. 1322-1328, May 10-15, 1999.
[17]China-Motor, http://www.china-motor.com.tw/eng_version/.
[18]Telegrams for Configuring and Operating the LMS2xx Laser Measurement System, http://www.sick.com.
[19]SICK, Laser Measurement Sensors, LMS291, http://team.caltech.edu/members/SICK/LMS_Quick_Manual_V1_1.pdf.
[20]dSPACE Product Webpage, http://www.dspace.com/ww/en/pub/home/products/hw/micautob.cfm.
[21]NovAtel, http://www.novatel.com/products/propakv3.htm.
[22]HP/Agilent 1673G Standalone Logic Analyzer, http://www.4gte.com/EquipmentPages/1673Glogicanalyzer.htm.
[23]W. H. Crouse, D. L. Anglin, 劉崇富 譯,“汽車學(二)----汽車驅動系統與底盤”,高立,台北,民國九十年。
[24]范逸之、江文賢、陳立元,“C++ Builder與RS-232串列通訊控制”,文魁,台北,民國九十一年。
[25]余明興等,“Borland C++ Builder 6程式設計經典”,文魁,台北,民國九十一年。
[26]Time研究室:蔡孟凱、雷穎傑、黃昭維、陳錦輝、陳正凱,“C++ Builder6完全攻略”,金禾,台北,民國九十一年。
[27]洪國勝、江國軍、龍國忠、洪月裡,“C++Builder 6程式設計快樂上手”,旗標,台北,民國九十一年。
[28]張耀仁,“C++程式設計”,�眳p,台北,民國九十三年。
[29]林伸茂,“8051單晶片徹底研究-基礎篇”,旗標,台北,民國八十二年。
[30]劉銘中、林進誠,“MCS-51單晶片原理與I/O應用(C語言版) ”,儒林,台北,民國九十年。
[31]賴麒文,“8051單晶片韌體設計的藝術(使用C語言)-軟體建構的思維與解決方法”,文魁,台北,民國九十年。
[32]林振義,“8086微處理機與IBM PC”,全威,台北,民國九十一年。
[33]蔡朝陽,“單晶片微電腦8051/8951原理與應用”,五版,全華,台北,民國九十一年。
[34]郭庭吉,“8051 C語言專題製作”,台科大,台北,民國九十三年。
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊