臺灣博碩士論文加值系統

本研究的主要目的是設計一種可以在室內環境中導航的自主機器人，以提供關於所述環境中懸浮微粒2.5（PM2.5）的濃度信息。此外，移動機器人配備有空氣過濾裝置，該空氣過濾裝置僅在PM2.5具有高濃度時才運行. 移動機器人的智能導航和過濾空氣的能力將造成電池消耗，這是智能機器人的問題之一. 此外，機器人將提供室內環境中PM2.5同心性的圖，這將給操作員理想環境的資訊或具有PM2.5高同心度的危險場所。PM2.5同心度圖將顯示在連接移動機器人的遠端電腦上. 本文的主要目的是使系統自動化，並去除了操作人員親自探勘的需求，以避免PM2.5質對人體的危害.

The main purpose of this research is to design an autonomous mobile robot that can navigate around an indoor environment to provide information about the particulate matter 2.5 (PM¬2.5) concentration on the environment. Also, the mobile robot is equipped with an air filtering device that would operate whenever there is high concentration of the particulate matter. Moreover, the robot would provide a map of the concentricity of particulate matter inside the indoor environment which would give the operators about ideas where are the ideal places or the hazardous places with high concentration of PM2.5. The mobile robot also has a capability to navigate around and filter the air intelligently to decrease the battery consumption. The map of the concentration of PM2.5 will be displayed on the remote computer that controls the operation of the autonomous mobile robot automatically. As a result, the system is fully autonomous and the requirement of having a human operator to avoid the hazardous effects of the particulate matter to humans is no longer required.

Table of Contents
摘要 I
Abstract II
Acknowlegements III
Table of Contents IV
List of Figures VII
List of Tables XIII
Chapter 1 Introduction 1
1.1 Background of the Study 1
1.2 Research Motivation 3
1.3 Literature Review................................................…..............................................4
1.3.1 Autonomous Mobile Robot Advancements………………………………...4
1.3.2 Particulate Matter 2.5 (PM2.5)……………………………………………...6
1.4 Organization of Dissertation 8
Chapter 2 Autonomous Mobile Robot 10
2.1 Kinematics of a Mobile Robot 11
2.2 Sonar Sensors 13
Chapter 3 Interval Type-2 Fuzzy-PID Dual-Mode Controller 19
3.1 Type-1 Fuzzy Logic Controller 19
3.1.1 Fuzzification………………………………………………………...…….20
3.1.2 Fuzzy Rules…………………………………………………………….....22
3.1.3 Defuzzification……………………………………………………………24
3.2 Interval Type-2 Fuzzy-PID Dual-Mode Controller 25
3.3 Interval Type-2 Fuzzy Logic Controller 26
3.4 Type-2 Fuzzy Logic System 30
3.5 Proportional Integral Derivative Controller (PID Controller) 33
Chapter 4 Active Air Filtering and PM2.5 Real-Time Mapping System 35
4.1 StarGazer Indoor Positioning System 35
4.1.1 Operation of StarGazer Device..................................................................…...37
4.1.2 Data Acquisition...………………....……………………………...………40
4.2 Particulate Matter 2.5 (PM2.5) Sensor 41
4.3 Microcontoller (Arduino Uno) 43
4.4 PM2.5 Real-Time Mapping System 44
4.5 Air Filtering System 50
4.6 Path planning of mobile robot to locations with high concentration of PM2.5 51
Chapter 5 Experiment and Results 56
5.1 Mobile Robot’s Trajectory Experiment Results 58
5.2 Type-1 Fuzzy Controller Experiment Results 64
5.3 Type-1 Fuzzy-PID Controller Experiment Results 68
5.4 IT2FPIDDMC Controller Experiment Results 72
Chapter 6 Conclusion and Future Works 76
References 77










List of Figures
Figure 2-1: P3DX mobile robot’s physical dimension and sonar sensor orientation 10
Figure 2-2: Pioneer 3-DX Mobile Robot……………………… 11
Figure 2-3: A model of a two-wheeled mobile non-holonomic robot …………… 12
Figure 2-4: Principle of an active sonar 14
Figure 2-5: Acquired data from sonar sensors 15
Figure 2-9: System error for S0....................................................................................16
Figure 2-10: System error for S7……………………………………………………..16
Figure 2-11: Specular Reflection Effect……………………………………………...18
Figure 3-1: System architecture of the type-1 fuzzy logic system 19
Figure 3-2: Membership Function for Sonar012, Sonar34, Sonar567 21
Figure 3-3: The schematic diagram of the IT2FPIDDMC 26
Figure 3-4: Membership function for e3 27
Figure 3-5: Membership function for e4 27
Figure 3-6: Key components of FLC system 28
Figure 3-7: Membership function for e5 29
Figure 3-8: Membership function for e6 29
Figure 4-1: Hagisonic StarGazer Indoor Positioning Device 35
Figure 4-2: Passive Landmark attached to the ceiling 37
Figure 4-3: Actual setup of the StarGazer Indoor Positioning Device 37
Figure 4-4: StarGazerMonitor UI. The computer serial setting is boxed for setting up the correct computer settings 38
Figure 4-5: Map building mode – the landmarks were already detected and displayed in the StarGazerMonitor 39
Figure 4-6: Map and data acquired from MATLAB 40
Figure 4-7: Experimental scenario – minimum light 40
Figure 4-8: Data acquired when light is abundant ………....... 41
Figure 4-9: Sharp GP2Y10 Dust Sensor 42
Figure 4-10: Sharp GPY10 Dust Sensor Pin Assignments …………………. 42
Figure 4-11: Arduino Microcontroller 43
Figure 4-12: Mobile robot with air-filtering and PM2.5 mapping system 45
Figure 4-13: Map acquired from the first run of the mobile robot 46
Figure 4-14: Map acquired from the second run of the mobile robot …………….…46
Figure 4-15: Data acquired from MATLAB with high concentration of PM¬2.5 ..……47
Figure 4-16: Data acquired from MATLAB with low concentration of PM2.5……………………………………………………………………………...…..48 Figure 4-17: 3D representation of the PM2.5 concentration map…...……….…….…50 Figure 4-18: Air filter control signal response…...……….…….……......………..…50 Figure 4-19: PID controller system architecture for path planning ………......…..…51 Figure 4-20: System architecture for path planning and obstacle avoidance controlller…………………………………………………………………………….53 Figure 4-21: Path of the mobile robot to reach the goal…………. ………......…..…54 Figure 4-22: Map acquired after the mobile robot implements the air filtering system…………………………. ………......……………………………………...…55
Figure 5-1: Experiment scenario 1 – mobile robot following the inside contours of a structured wall 56
Figure 5-2: Experimental scenario 2 – mobile robot following the inside contours of an unstructured wall 57
Figure 5-3: Experimental scenario 3 – mobile robot following the outside contours of a structured wall 58
Figure 5-4: Experiment scenario 4 – mobile robot following the outside contours of an unstructured wall 58
Figure 5-5: Experimental scenario 1 mobile robot track using type-1 fuzzy controller
59
Figure 5-6: Experimental scenario 2 mobile robot track using type-1 fuzzy controller
59
Figure 5-7: Experimental scenario 3 mobile robot track using type-1 fuzzy controller
60
Figure 5-8: Experimental scenario 4 mobile robot track using type-1 fuzzy controller
60
Figure 5-9: Experimental scenario 1 mobile robot track using PID controller……….61
Figure 5-10: Experimental scenario 2 mobile robot track using PID controller………61
Figure 5-11: Experimental scenario 3 mobile robot track using PID controller………62
Figure 5-12: Experimental scenario 4 mobile robot track using PID controller………62
Figure 5-13. Experimental scenario 1 mobile robot track using IT2FPIDDMC…..…63
Figure 5-14. Experimental scenario 2 mobile robot track using IT2FPIDDMC…..…63
Figure 5-15. Experimental scenario 3 mobile robot track using IT2FPIDDMC…..…64
Figure 5-16. Experimental scenario 4 mobile robor track using IT2FPIDDMC…..…64
Figure 5-17: Experimental scenario 1 response curve using type-1 fuzzy controller for e1 65
Figure 5-18: Experimental scenario 2 response curve using type-1 fuzzy controller for e1 65
Figure 5-19: Experimental scenario 3 curve using type-1 fuzzy controller for e1 66
Figure 5-20: Experimental scenario 4 response curve using type-1 fuzzy controller for e1 66
Figure 5-21: Right wheel velocity response curve for type-1 fuzzy controller 67
Figure 5-22: Left wheel velocity response curve for type-1 fuzzy controller 67
Figure 5-23: Left and right wheel velocity response curve for type-1 fuzzy controller 68
Figure 5-24: Experimental scenario 1 response curve using type-1 fuzzy PID controller for e1……………………………………………………………………………………………………….68
Figure 5-25: Experimental scenario 2 response curve using type-1 fuzzy PID controller for e1 69
Figure 5-26: Experimental scenario 3 response curve using type-1 fuzzy PID controller for e1 69
Figure 5-27: Experimental scenario 4 response curve using type-1 fuzzy PID controller for e1 70
Figure 5-28: Right wheel velocity response curve for type-1 fuzzy PID controller 71
Figure 5-29: Left wheel velocity response curve for type-1 fuzzy PID controller 71
Figure 5-30: Left and right wheel velocity response curve for type-1 fuzzy PID controller……………………………………………………………………………..71
Figure 5-31: Experimental scenario 1 response curve using interval type-2 fuzzy PID dual-mode controller 72
Figure 5-32: Experimental scenario 2 response curve using interval type-2 fuzzy PID dual-mode controller…………………………………………………………………72
Figure 5-33: Experimental scenario 3 response curve using interval type-2 fuzzy PID dual-mode controller 73
Figure 5-34: Experimental scenario 4 response curve using interval type-2 fuzzy PID dual-mode controller 73
Figure 5-35: Right wheel velocity response curve for interval type-2 fuzzy PID dual-mode controller 74
Figure 5-36: Left wheel velocity response curve for interval type-2 fuzzy PID dual-mode controller………………………………………………………………….. …...74
Figure 5-37: Left and right wheel velocity step response for interval type-2 fuzzy PID dual-mode controller 75







List of Tables

Table 2-1: Pioneer 3-DX Mobile Robot Specifications 14
Table 3-1: Fuzzy Controller Fuzzy Rules 23
Table 3-2: Fuzzy inference rule base of running velocity 30
Table 4-1: Hagisonic StarGazer Specifications 36
Table 4-2: PM2.5 Sensor Pin Definition 43
Table 4-3: Data Acquired from the System 49

References
[1]Itai Kloog “Fine particulate matter (PM2.5) association with peripheral artery disease admissions in the northeastern United States” in Epidemiology, Vol. 26, pp. 572-277, June 2016
[2]Chen, Hsiang-Ching, Ku, Chih-Hung “characteristics of pm2.5 in Taiwan’s largest cities during 2006–2009” in Epidemiology, Vol. 22, January 2011
[3]Wang Min Ling “Breathing the Air We don’t Know: The Problem of PM2.5”
[4]Yee-Lin Wu, Jhih-Siang Jian, Jhong-You Kel, and Chen-Chieh Kuo “Identification of the Impact of Dust Storm on the Ambient PM10 Concentrations in Southern Taiwan”
[5]Mohamed F. Yassin, Bothaina E.Y. Al Thaqeb, Eman A.E. Al-Mutiri, “Assessment of Indoor PM2.5 in Different Residential Environments” I Atmospheric Environment, vol. 56, pp. 65-68, September 2012.
[6] Standalone: Sharp Dust Sensor
http://arduinodev.woofex.net/2012/12/01/standalone-sharp-dust-sensor/

[7]Sungchul Kang, Woosub Lee, Kyungchul Shin, Munsang Kim “ROBHAZ-rescue: rough-terrain negotiable tele operated mobile robot for rescue mission” in International Workshop on Safety, Security and Rescue Robots, pp.105-110, June 2005
[8]Adarsh S, Mohamed Kaleemuddin S, Dines Bose, K I Ramachandran “Performance Comparison of Infrared and Ultrasonic Sensors for Obstacle of Different Materials in Vehicle/Robot Navigation Applications” in IOP Conf. Series: Materials Science and Engineering, Vol. 1, pp. 1-8, 2016
[9]R. Alba-Flores, F. Rios-Gutierrez, C. Jeanniton “Qualitative evaluation of a PID controller for autonomous mobile robot navigation implemented in an FPGA card” in International Conference on Natural Computation (ICNC), Vol. 7, pp. 233-238, September 2011
[10]Abdullah Al Mamun Khan, M. Sultan Mahmud Rana, Jubayer Alam Rabin, Abu Farzan Mitul, Shahjahan “Design and Implementation of a Robot for Maze-Solving with Turning Indicators Using PID Controller” in International Conference on Informatics, Electronics, and Vision (ICIEV), pp. 211-217, August 2013
[11]Cheng-Wen Lee “Green Suppliers Assessment Using Fuzzy AHP” in Journal in Advance Engineering, Vol. 4, No. 2, pp. 192-202/April 2009
[12]Min-Chan Hwang “The Fuzzy Sets with Continuous Membership Functions in Topological Spaces” in Journal in Advance Engineering, Vol. 4, No. 2, pp. 147-150/April 2009
[13]Yean-Ren Hwang, Shih-Yao Huang “Design FPGA Controllers for Air Motor via MRAC and Fuzzy Theory” in Journal in Advance Engineering, Vol. 4, No. 1, pp. 63-69/January 2009
[14]Ying-Jih Chao Lee “Appraisal Performance of FDI Strategy in China – AHP, Fuzzy Theory and TOPSIS Methods” in Journal in Advance Engineering, Vol. 9, No. 1, pp. 1-8/January 2014
[15]Siripun Thongchai, Kazukio Kawamura “Application of Fuzzy Control to a Sonar-Based Obstacle Avoidance Mobile Robot” in Proceedings of IEEE International Conference on Control Applications, pp. 425-430, 2000.
[16]Hasan A.Yousef “Fuzzy-Logic Obstacle Avoidance Control: Software Simulation And Hardware Implementation” in European Control Conference, pp. 1458-1463, 1999.
[17]H. Mamdani, S. Assilian, “An Experiment in Linguistic Synthesis With a Fuzzy Logic Controller”, in Int. Journal of Man-Machine Studies, Vol. 7, no. 1, p. 1-13, 1975.
[18]T. Takagi and M. Sugeno, “Derivation of fuzzy control rules from human operator’s control actions”, Proceedings of the IFAC Symp. on Fuzzy Information, Knowledge Representation and Decision Analysis, p. 55-60, July 1983
[19]L. A. Zadeh, “Outline of a new approach to the analysis of complex systems and decision processes”, IEEE Trans. on Systems, Man and Cybernetics, 3 (1): p. 28-44, Jan. 1973
[20]J. Borenstein and Y. Koren, “Obstacle avoidance with ultrasonic sensors,” in IEEE Journal o,f Robotics, and Automation, Vol. 4, no. 2, April 1988.
[21]R. E. King, Computational Intelligence in Control Engineering, Marcel Dekker, Inc., USA, 1999.
[22]L. A. Zadeh, ‘‘Fuzzy sets,” Information and Control, Vol. 8, pp. 338-353, 1965.
[23]J. Mendel, Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Upper Saddle River, NJ: Prentice-Hall, 2001
[27]P. Khunsongkiet, E. Boonchieng “Converting air quality monitoring low cost sensor data to digital value via mobile interface” in International Conference on Biomedical Engineering, pp. 57-62, February 2017
[28]Ling-Jyh Chen, Yao-Hua Ho, Hu-Cheng Lee, Hsuan-Cho Wu, Hao-Min Liu, Hsin-Hung Hsieh, Yu-Te Huang, Shih-Chun Candice Lung “An Open Framework for Participatory PM2.5 Monitoring in Smart Cities” in IEEE Access, Vol. 5, pp. 131-137. July 2017
[29]J. Mendel and R. John “Type-2 fuzzy sets made simple,” IEEE Trans. Fuzzy Syst., vol. 10, pp. 117-127, Apr. 2002.

[30]J. Mendel, Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Upper Saddle River, NJ: Prentice-Hall, 2001
[31]Q. Liang and J.M. Mendel, “Interval type-2 fuzzy logic systems: Theory and design,” IEEE Trans. Fuzzy Syst., vol. 8, no. 5, pp. 535-550, Oct. 2000.
[32]C. F. Juang, C. H. Hsu “Reinforcement Ant Optimized Fuzzy Controller for Mobile-Robot Wall-Following Control,” in IEEE Transactions on Industrial Electronics, Vol. 56, no. 56, pp. 3931-3940, October 2009
[33]Hani A. Hagras, “A Hierarchal Type-2 Fuzzy Logic Control Architecture for Autonomous Mobile Robots,” IEEE Transactions on Fuzzy Systems, vol. 12, no. 4, pp. 524-539, August 2004.
[34]Ruijiang Luo, Ying Han, Zheng Liu, “The Current Status and Factors of Indoor PM2.5 in Tangshan, China” in 10th International Symposium on Heating, Ventilation, and Air Conditioning, pp. 3824-3829, October 2017.
[35]R. Alba-Flores, F. Rios-Gutierrez, C. Jeanniton “Qualitative evaluation of a PID controller for autonomous mobile robot navigation implemented in an FPGA card” in International Conference on Natural Computation (ICNC), Vol. 7, pp. 233-238, September 2011
[36]Abdullah Al Mamun Khan, M. Sultan Mahmud Rana, Jubayer Alam Rabin, Abu Farzan Mitul, Shahjahan “Design and Implementation of a Robot for Maze-Solving with Turning Indicators Using PID Controller” in International Conference on Informatics, Electronics, and Vision (ICIEV), pp. 211-217, August 2013