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研究生:施俊吉
研究生(外文):Chun-Chi Shih
論文名稱:車型機器人平台使用神經系統結構之設計
論文名稱(外文):DESIGN A CAR-LILE MOBILE ROBOT PLATFORM USING NERVOUS SYSTEM STRUCTURE
指導教授:呂虹慶
指導教授(外文):Hung-Ching Lu
學位類別:碩士
校院名稱:大同大學
系所名稱:電機工程學系(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:2004
畢業學年度:93
語文別:英文
論文頁數:100
中文關鍵詞:車型機器人神經系統
外文關鍵詞:Car-like mobile robotmobile robotnervousrobot
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本論文主要是藉由軟硬體設計出一個具有人工智能的車型機器人,藉由開放性與模組化的設計使得控制系統理論得以容易的使用在所提出的車型機器人上。在本篇論文中以模糊控制理論為例,車型機器人可以完成延著牆壁行走並產生房間地圖的成果。在機器人系統中主要分為兩大部分:操作者介面與機器人本體。操作者介面目前提供一套呼叫以Ttu- 為開頭的函式庫所寫成的GUI 介面DEMO程式,同時程式本身亦可在機器人上執行,進而安裝在某一電腦上透過Internet 完成控制。在機器人本體部分共裝載了數十組控制器、各類感測器與一個強而有力的中央處理器,經實驗測試後發現在常態的控制法則下,皆能完成預期的效果。本論文所開發的機器人因其具有高度的擴充性,因此提供了一個實驗室用於開發其他車行機器人的泛用平台,對於日後的研發將是一個不可獲缺的墊腳石。
In this thesis a car-like mobile robot is presented with artificial intelligent(AI). Several kinds of control theories can be used on the car-like mobile robot because of the open design and modules design. For example, a fuzzy base robot can walk following the wall and create a little map. The proposed car-like mobile robot has two major parts: user interface system and robot. The former, along with its graphic user interface(GUI) is created by Ttu- library and it includes some programs that can be installed in the main computer of robot or the client computer on Internet. Furthermore the car-like mobile robot is integrated with several controllers, sensors and a powerful computer to fit many general control theories. It is shown that the proposed platform has great potential in developing similar robots with other functions.
ABSTRACT(IN CHINESE) I
ABSTRACT(IN ENGLISH) II
CONTENTS III
LIST OF FIGURES V
LIST OF TABLES VIII
CHAPTER 1 INTRODUCTION 1
1.1 Preface 1
1.2 Research Motivation 2
1.3 The Organization of This Thesis 2
CHAPTER 2 BASIC CONCEPTS 3
2.1 Introduction 3
2.2 Fuzzy Control System Theory 4
2.3 PWM 9
2.4 Introduction of RTOS 13
2.5 Introduction of Nervous and Reflex Arc 19
CHAPTER 3 HARDWARE 23
3.1 Introduction 23
3.2 The Sensor Systems 23
3.3 The Motion Control Systems 34
3.4 The Main Computer and 8255-Array Cards 39
CHAPTER 4 SOFTWARE AND FIRMWARE 46
4.1 Introduction 46
4.2 The Firmware of The Sensor Systems 46
4.3 The Firmware of The Motion Control Systems 56
4.4 The Software on The Main Computer and ISA Cards Drivers 58
CHAPTER 5 AN EXAMPLE-MOVER ROBOT 64
5.1 Introduction 64
5.2 The Hardware of The Mover Robot 65
5.3 The Software of The Mover Robot 66
CHAPTER 6 IMPLEMENTATION AND RESULT 81
6.1 Introduction 81
6.2 Hardware Pictures 81
6.3 Software 85
6.4 The Mover Robot 90
CHAPTER 7 CONCLUSION 94
REFERENCES 95























LIST OF FIGURES


Fig. 2.1 Configuration of fuzzy control system 6
Fig. 2.2 PWM generation 10
Fig. 2.3 PWM generation circuit 11
Fig. 2.4 The block diagram of PWM demodulation 12
Fig. 2.5 Application, RTOS and hardware relations 13
Fig. 2.6 RTOS services circle 14
Fig. 2.7 Nested preemption 16
Fig. 2.8 Switching time and scheduling 17
Fig. 2.9 One kind Memory Management 19
Fig. 2.10 Schematic of biological neuron 20
Fig. 2.11 Reflex arc 22
Fig. 3.1 The block diagram of Ultra sonic ranger 24
Fig. 3.2 GP2D02 25
Fig. 3.3 The block diagram of infrared ranger 25
Fig. 3.4 Locations of infrared rangers 26
Fig. 3.5 Idea D-T charts 26
Fig. 3.6 The left-front wheel 28
Fig. 3.7 CNY70 29
Fig. 3.8 Symbol of CNY70 29
Fig. 3.9 The block diagram of encoder system 30
Fig. 3.10 The CCD is installed at right-front side 31
Fig. 3.11 GPS Receiver 33
Fig. 3.12 Bar code reader 33
Fig. 3.13 The block diagram of velocity control system 35
Fig. 3.14 The encoder system 36
Fig. 3.15 The block diagram of steeling angle control system 37
Fig. 3.16 The steeling angle control system 37
Fig. 3.17 The right-down side of the robot 38
Fig. 3.18 The first ISA card 40
Fig. 3.19 The block diagram of the first ISA card 41
Fig. 3.20 The second ISA card 42
Fig. 3.21 The block diagram of the second ISA card 43
Fig. 3.22 The interface between iPAQ and our robot 44
Fig. 3.23 The block diagram of iPAQ and the robot interface card 44
Fig. 4.1 The firmware of the ultra sonic ranger 47
Fig. 4.2 The firmware of the ultra sonic ranger 47
Fig. 4.3 The firmware of the ultra sonic ranger 48
Fig. 4.4 The firmware of infrared ranger 49
Fig. 4.5 The test circuit of GP2D02 50
Fig. 4.6 The Reading Waveform of GP2D02 50
Fig. 4.7 The firmware of the encoder system 51
Fig. 4.8 The firmware of the encoder system 52
Fig. 4.9 The firmware of the encoder system 52
Fig. 4.10 The firmware of the encoder system 53
Fig. 4.11 The TtuBCRGet() function 55
Fig. 4.12 Demo program 58
Fig. 4.13 The membership function of infrared ranger 61
Fig. 4.14 PPC program 62
Fig. 4.15 PPC program 62
Fig. 4.16 Client program 63
Fig. 5.1 Def. of distances 66
Fig. 5.2 Fuzzy control system 67
Fig. 5.3 The membership function of 68
Fig. 5.4 The membership function of 68
Fig. 5.5 The simulation of the error 70
Fig. 5.6 The Def. of Wang model 71
Fig. 5.7 A turn-left corner 72
Fig. 5.8 The algorithm of turn-left 72
Fig. 5.9 The right side rangers installation 73
Fig. 5.10 Simple experiment to create a map 74
Fig. 5.11 The simple map of E.E. Building 76
Fig. 5.12 The algorithm of Choice-a-path 77
Fig. 5.13 The algorithm of Choice-a-path 78
Fig. 5.14 The algorithm of Choice-a-path 79
Fig. 6.1 The battery 82
Fig. 6.2 Front Infrared ranger 82
Fig. 6.3 Steeling angle servomotor 83
Fig. 6.4 Hard disk of the main computer 83
Fig. 6.5 Power system main board 84
Fig. 6.6 The robot on the testing table 84
Fig. 6.7 Another sight of encoder system 85
Fig. 6.8 GUI Server Program in Windows system 86
Fig. 6.9 ~Fig. 6.16 Fuzzy wall follow 90~91
Fig. 6.17~ Fig. 6.30 Creating the map 92~93








LIST OF TABLES

Table 5.1 Fuzzy rules table 69
[1]E. Rimon and D. E. Koditschek, “Exact robot navigation using artifical potential functions,” IEEE Trans. Robot. Automat., vol. 8, no. 5, pp. 501- 518, Oct., 1992.
[2]J. L. Crowley, “Navigation for an intelligent mobile robot,” IEEE Journal of Robot. Automat., vol. 1, no. 1, pp. 31- 41, Mar., 1985.
[3]A. Fujimori, P. N. Nikiforuk, and M. M.Gupta, “Adaptive navigation of mobile robots with obstacles avoidance,” IEEE Trans. Robot. Automat., vol. 13, no. 4, pp. 596- 602, Aug., 1997.
[4]J. C. Latombe, Robot Motion Path Planning. Boston, MA: Kluwer, 1991.
[5]G. Desaulniers and F. Soumis, “An efficient algorithm to find a shortest path for a car-like robot,” IEEE Trans. Robot. Automat., vol. 11, no. 6, pp. 819- 828, Dec., 1995.
[6]J. L. Diaz de Leon S. and J. H. Sossa A., “Automatic path planning for a mobile robot among obstacles of arbitrary shape,” IEEE Trans. Syst., Man, Cybern., Part B: Cybern., vol. 28, no. 3, pp. 467-472, June, 1998.
[7]Y. H. Liu and S. Arimoto, “Path planning using a tangent graph for mobile robots among polygonal and curved obstacles,” The International Journal of Robotics Research, vol. 11, no. 4, pp. 376-382, Aug., 1992.
[8]L. E. Kavraki, M. N. Kolountzakis, and J. C. Latombe, “Analysis of probabilistic roadmaps for path planning,” IEEE Trans. Robot. Automat., vol. 14, no. 1, pp. 166- 171, Feb., 1998.
[9]S. Sundar and Z. Shiller, “Optimal obstacle avoidance based on the Hamilton-Jacobi-Bellman equation,” IEEE Trans. Robot. Automat., vol. 13, no. 2, pp. 305- 310, Apr., 1997.
[10]C. Alexopolous and P. M. Griffin, “ Path planning for a mobile robot,” IEEE Trans. Syst., Man, Cybern., vol. 22, no. 2, pp. 318-322, Mar./Apr., 1992.
[11]I. Namgung and J. Duffy, “Two dimensional collision-free planning using linear parametric curve,” Journal of Robotic Systems, vol. 14, no. 9, pp. 659-673, Sept., 1997.
[12]C. H. Sheu and K. Y. Young, “A heuristic approach to robot path planning based on task requirements using a genetic algorithm,” Journal of Intelligent and Robotic Systems, vol. 16, pp. 65-88, May, 1996.
[13]C. J. Taylor and D. J. Kriegman, “Vision-based motion planning and exploration algorithms for mobile robots,” IEEE Trans. Robot. Automat., vol. 14, no. 3, pp. 417-426, June, 1998.
[14]J. Barraquand, B. Langlois, and J. C. Latombe, “Numerical potential field techniques for robot path planning,” IEEE Trans. Syst., Man, Cybern., vol. 22, no. 2, pp. 224-241, Mar./Apr., 1992.
[15]Y. K. Hwang and N. Ahuja, “ A potential field approach to path planning,” IEEE Trans. Robot. Automat., vol. 8, no. 1, pp. 23-32, Feb., 1992.
[16]M. Galicki, “Optimal planning of a collision-free trajectory of redundant maipulators,” The International Journal of Robotics Research, vol. 11, no. 6, pp. 549-559, Dec., 1992.
[17]C. Canudas de Wit, N. Fixot, and K. J. Astrom, “Trajectory tracking in robot manipulators via nonlinear estimated state feedback,” IEEE Trans. Robot. Automat., vol. 8, no. 1, pp. 138-144, Feb., 1992.
[18]E. Freund and R. Mayr, “Nonlinear path control in automated vehicle guidance,” IEEE Trans. Robot. Automat., vol. 13, no. 1, pp. 49-60, Feb., 1997.
[19]C. Szepesvari and A. Lorincz, “An integrated architecture for motion-control and path-planning,” Journal of Robotic Systems, vol. 15, no. 1, pp. 1-15, Jan., 1998.
[20]W. Li, C. Ma, and F. M. Wahl, “A neuro-fuzzy system architecture for behavior-based control of a mobile robot in unknown environments,” Fuzzy Sets and Systems, Vol. 87, pp. 133-140, 1997.
[21]A. Zelinsky, “A mobile robot exploration algorithm,” IEEE Trans. Robot. Automat., vol. 8, no. 6, pp. 707-717, Dec., 1992.
[22]G. Conte and R. Zulli, “Hierarachical path planning in a multi-robot Environment with a simple navigation function,” IEEE Trans. Syst., Man, Cybern., vol. 25, no. 4, pp. 651-654, Apr., 1995.
[23]G. Conte, S. Longhi, and R. Zulli, “Motion planning for unicycle and car-like robots,” International Journal of Systems Science, vol. 27, no. 8, pp. 791-798, Aug., 1996.
[24]A. Zelinsky, “Usuing path transforms to guide the search for findpath in 2D,” The International Journal of Robotics Research, vol. 13, no. 4, pp. 315-325, Aug., 1994.
[25]A. W. Divelbiss and J. T. Wen, “A path space approach to nonholonomic motion plannning in the presence of obstacles,” IEEE Trans. Robot. Automat., vol. 13, no. 3, pp. 443-450, June, 1997.
[26]P. Ferbach, “A method of progressive constraints for nonholonomic motion plannning,” IEEE Trans. Robot. Automat., vol. 14, no. 1, pp. 172-179, Feb., 1998.
[27]K. Tanaka and M. Sano, “Trajectory stabilization of a model car via fuzzy control,” Fuzzy Sets and Systems, vol. 70, pp. 155-170, 1995.
[28]Z. P. Jiang and N. Henk, “Tracking control of mobile robots: a case study in backstepping,” Automatica., vol. 33, no. 7, pp. 1393-1399, July,1997.
[29]L. X. Wang and J. M. Mendel, “Generating fuzzy rules by learning from examples,” IEEE Trans. Syst., Man, Cybern., vol. 22, no. 6, pp. 1414-1427, June, 1992.
[30]L. A. Zadeh, “Fuzzy Set,” Inform. Contr., vol. 8, pp. 338-353, 1965.
[31]L. A. Zadeh, “Outline of a new approach to the analysisof complex systems and decision processes,” IEEE Trans. Syst., Man, Cybern., vol. 3, no. 1, pp. 28-44, Mar., 1973.
[32]E. H. Mamdani, J. J. Ostergard, and E.Lembessis, “Uses of fuzzy logic for implementing rule-base control of industrial processes,” in Fuzzy Sets and Decision Analysis, H. J. Zimmermann, B. R. Gaines, and L. A. Zadeh, North Holland, Amsterdam, pp. 429-445, 1984.
[33]C. C. Lee, “Fuzzy logic in control systems: fuzzy logic controller, part I,” IEEE Trans. Syst., Man, Cybern., vol. 20, no. 2, pp. 404-418, Feb., 1990.
[34]T. Takagi and M. Segeno, “Fuzzy identification of systems and its application to modelling and control,” IEEE Trans. Syst., Man, Cybern., vol. 15, no. 1, pp. 116-132, Jan., 1985.
[35]M. Segeno and G. T. Kan, “Fuzzy modeling and control of multilayer incinerator,” Fuzzy Sets and Systems, vol. 18, pp. 329-346, 1986.
[36]B. Kosko, Neural Networks and Fuzzy Systems— a dynamical systems approach to machine intelligence, Prentice Hall: New Jersey, 1991.
[37]D. Nguyen and B. Widrow, “The truck backer-upper: An example of self-learning in neural network,” IEEE Control System Magzine, vol. 10, no. 3, pp. 18-23, Sept., 1990.
[38]L. X. Wang and J. M. Mendel, “Generating fuzzy rules from numerical data, with applications,” USC SIPI, no. 169, Univ. Southern Calif., Los Angeles, 1991.
[39]Ziemer and Tranter, “Principle of Communications,” pp. 218-221 Houghton Mifflin 1995.
[40]http://search390.techtarget.com/sDefinition/0,,sid10_gci213667,00.html
[41]http://linuxdevices.com/articles/AT4627965573.html
[42]http://vv.carleton.ca/~neil/neural/neuron-a.html
[43]http://education.vetmed.vt.edu/Curriculum/VM8054/Labs/Lab9/Examples/exsomarc.htm
[44]http://gge.unb.ca/Resources/HowDoesGPSWork.html
[45]Petru Rusu, Emil M. Petriu, Thom E. Whalen, Aurel Cornell and Hans J. W. Spoelder, “Behavior-based neuro-fuzzy controller for mobile robot navigation,” IEEE Transactions On Instrumentation And Measurement, Vol. 52, No.4 August 2003
[46]A. Saffiotti, “The uses of fuzzy logic in automous robot navigation: a catalogus raisonne,” Soft Comput., Vol 1, no.4, pp. 180-197, 1997
[47]J. -S. R. Jang, “ANFIS: Adaptive-network-based fuzzy inference systems,” IEEE Trans. Nerual Networks, Vol. 23, pp. 665-685, June 1993.
[48]Tzuu-Hseng S. Li, Shih-Jie Chang, Yi-Xiang Chen, “Implementation of human-like driving skills by autonomous fuzzy behavior control on an FPGA-based car-like mobile robot,” IEEE Trans, Industrial Electronics, Vol. 50, No.5, pp. 867-880, October 2003.
[49]Chih-Ying Chuang, “Fuzzy-Based path planning study for car-like mobile robot,” Master Thesis, Department of Electrical Engineering, Tatung University, June 2002.
[50]http://www.acroname.com/robotics/parts/R19-IR02.html
[51]http://www.stillhq.com/extracted/linux-webcam/
[52]http://ucos-ii.com/contents/products/ucos-ii/downloads/
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