跳到主要內容

臺灣博碩士論文加值系統

(3.237.38.244) 您好!臺灣時間:2021/07/26 11:00
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

: 
twitterline
研究生:詹鴻慶
研究生(外文):Chan, Hung-Ching
論文名稱:實作Qi模組化架構於服務機器人上的SLAM與導航系統
論文名稱(外文):Modular systems for SLAM and navigation using the Qi software environment on a service robot
指導教授:許宏銘許宏銘引用關係
指導教授(外文):N. Michael Mayer
口試委員:許宏銘林惠勇李祖聖
口試委員(外文):N. Michael MayerLin, Huei-YungLi, Tzuu-Hseng
口試日期:2012-07-04
學位類別:碩士
校院名稱:國立中正大學
系所名稱:電機工程研究所
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:2012
畢業學年度:100
語文別:英文
論文頁數:51
中文關鍵詞:導航
外文關鍵詞:SLAMNavigation
相關次數:
  • 被引用被引用:0
  • 點閱點閱:511
  • 評分評分:
  • 下載下載:132
  • 收藏至我的研究室書目清單書目收藏:0
近年來,有關於機器人的競賽非常多,不論是人型機器人或輪型機器人皆有相關競賽,諸如足球競賽、越野競賽、智慧型服務機器人競賽…等。本篇論文主要實作以輪型機器人為主,企圖將服務型機器人的功能實行模組化後並可以成功使用,實作的模組(module)有兩個,第一個模組是Simultaneous localization and mapping(SLAM),以搖桿控制機器人行走並使用雷射測距儀器(Laser Range Finder)得到機器人前方的資訊去進行處理,也就是說,我們從雷射測距儀器(Laser Range Finder)得到sensor data以及從機器人本身的encoder得到encoder data再加上來自於搖桿的command,這些資料會輸入到SLAM module,經過self-localization找到確切的地圖位置再結合sensor data將地圖畫出。第二個模組是導航(Navigation),當我們給機器人定一個目標位置後,將sensor data和encoder data持續輸入到navigation module裡,經過其內部的APF運算出機器人在當時需要往哪個方向移動,利用這個結果加上sensor data去作選擇,這些選擇能使機器人可以自主地走到目標位置,並在行走期間可以避開路途中的障礙物。
In recent year, more and more robot competitions attract many countries to join, whether the robot is humanoid robot or wheeled robot, and there are many types of competition, such as football competitions, cross-country competitions, and the intelligent service robot competitions. In this thesis, we use a wheeled robot. The main content of my thesis is that we attempted to create the software modules for a service robot, and the module can be successfully implemented. There are two main modules:
The first one is Simultaneous localization and mapping (SLAM) module. The SLAM module requires input from several sensors. The first one is the laser range finder (laser scanner) to get the range sensor data of the environment, the second one is encoder data from the robot’s differential wheels which will be treat as the reference data to generate fixed amount of particles for particle filters. Particle filters are used to match the observation with the current map, and to find the exact location in the map, which will fix accumulate error from encoder. The third one is joystick command, which will control many functions, such as draw map on the screen, save map in computer, start to use the particle filter.
The second one is navigation, when giving the robot a target position, the robot can autonomously go to the target position. While the robot is moving, it will get the range sensor data from laser range finder and the encoder data from the robot, which will feed as the input data of navigation module. We use the artificial potential field method to calculate the current desired moving direction of robot. This method can also be used to avoid obstacles while moving when the robot’s path is occupied by obstacles, such as human, chairs or tables etc... .
TABLE OF CONTENTS........................................................i
ACKNOWLEDGMENTS.........................................................ii
摘要...................................................................iii
ABSTRACT................................................................iv
LIST OF FIGURES..........................................................v
CHAPTER 1 INTRODUCTION...................................................1
1.1 Motivation......................................................1
1.2 Background......................................................1
1.3 Equipment.......................................................3
CHAPTER 2 THE PROCESSES OF SLAM AND NAVIGATION AND QI ENVIRONMENT........7
2.1 The SLAM concept and the functions of process in SLAM...........7
2.2 The navigation process sequence................................20
2.3 The Qi environment.............................................25
2.3.1 Action server................................................26
2.3.2 User interface...............................................27
2.3.3 Design flow..................................................28
CHAPTER 3 IMPLEMENTATION AND RESULTS....................................29
3.1 The implementation and results of the SLAM system on Qi........29
3.2 The implementation and results of the navigation system on Qi..32
CHAPTER 4 COMPARISON AND CONCLUSIONS....................................39
REFERENCES..............................................................41


[1]H.Durrant-Whyte and T. Bailey, “Simultaneous localization and mapping: Part I,” IEEE Robot. Autom. Mag., vol. 13, no. 2, pp. 99–108, Jun. 2006.
[2]H. Durrant-Whyte and T.Bailey, “Simultaneous Localization and Mapping (SLAM): Part II,” IEEE Robot. Autom.Mag., vol. 13, no. 3, pp. 108–117, Sep. 2006.
[3]S. Thrun, Robotics and Cognitive Approaches to Spatial Mapping, chapter Simultaneous Localization and Mapping, pages 13–41. Springer Verlag, 2008.
[4]M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit, “FastSLAM: A factored solution to the simultaneous localization and mapping problem.” In AAAI-02, Edmonton, Canada, 2002. AAAI.
[5]K. Murphy, “Bayesian map learning in dynamic environments.” In Advances in Neural Information Processing Systems 11. MIT Press, 1999.
[6]A. Eliazar and R. Parr, “DP-SLAM: Fast, Robust Simultaneous Localization and Mapping without Predetermined Landmarks,” in Proceedings of The International Joint Conference on Artificial Intelligence, pp. 1135-1142, 2003.
[7]V. Lumelsky, T. Skewis, “Incorporating Range Sensing in the Robot Navigation Function,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 20, pp. 1058–1068, 1990.
[8]I. Kamon, E. Rivlin, E. Rimon, “A New Range-Sensor Based Globally Convergent Navigation Algorithm for Mobile Robots,” in Proceedings of the IEEE International Conference on Robotics and Automation, Minneapolis, April 1996.
[9]F. Mastrogiovanni, A. Sgorbissa and R. Zaccaria, “Robust navigation in an unknown environment with minimal sensing and representation," IEEE Transactions on Systems, Man and Cybernetics, Vol. 39, No. 1, pp. 212-229, 2009.
[10]O. Khatib, S. Quinlan, “Elastic Bands: Connecting, Path Planning and Control,” in Proceedings of IEEE International Conference on Robotics and Automation, Atlanta, GA, May 1993
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top