Nghiên cứu một số mô hình đảm bảo an ninh cơ sở dữ liệu và thử nghiệm ứng dụng

VIETNAM NATIONAL UNIVERSITY, HANOI -VNU UNIVERSITY OF ENGINEERING AND TECHNOLOGY Phung Manh Duong STUDY ON SUPERVISION AND CONTROL OF ROBOT OVER COMPUTER NETWORK DOCTORAL THESIS IN ELECTRONICS AND TELECOMMUNICATIONS TECHNOLOGY Hanoi - 2015 VIETNAM NATIONAL UNIVERSITY, HANOI -VNU UNIVERSITY OF ENGINEERING AND TECHNOLOGY Phung Manh Duong STUDY ON SUPERVISION AND CONTROL OF ROBOT OVER COMPUTER NETWORK Major: Electronic Engineering Code: 62 52 70 01 DOCTORAL THESIS IN ELECTRONICS AND TELECOMMUNICATIONS TECHNOLOGY SUPERVISOR: Assoc Prof Dr Tran Quang Vinh Hanoi - 2015 ĐẠI HỌC QUỐC GIA HÀ NỘI -TRƯỜNG ĐẠI HỌC CÔNG NGHỆ Phùng Mạnh Dương NGHIÊN CỨU VẤN ĐỀ GIÁM SÁT VÀ ĐIỀU KHIỂN ROBOT QUA MẠNG MÁY TÍNH Chuyên ngành: Kỹ thuật Điện tử Mã số: 62 52 70 01 LUẬN ÁN TIẾN SĨ NGÀNH CÔNG NGHỆ ĐIỆN TỬ - VIỄN THÔNG NGƯỜI HƯỚNG DẪN KHOA HỌC: PGS.TS Trần Quang Vinh Hà Nội - 2015 DECLARATION BY CANDIDATE I hereby declare that this thesis is my own work and effort and that it has not been submitted anywhere for any award Where other sources of information have been used, they have been acknowledged Author Phung Manh Duong i Acknowledgements This thesis has been completed with the contributions of many people First of all, I would like to express my special thanks Prof Tran Quang Vinh, my supervisor, for his guidance, encouragement, and support I was truly fortunate to have the opportunity to work with him as a PhD student I am greatly indebted to Prof Kok Kiong Tan and the members of Mechatronics and Automation Laboratory for their support and advices during my research in National University of Singapore I am grateful to Ms Nguyen Thi Thanh Van and Mr Tran Thuan Hoang for their supports on simulation and experiment in my research I would like to express my appreciation to the teachers and students in VNU-UET When I started working in this college, I immediately noticed and valued the high level of communication and the free flow of ideas here I appreciate the National Foundation for Science and Technology Development (NAFOSTED) for their financial support for my attendance of international conferences Finally, I owe special thanks to my family, who always accept and encourage my decisions in the professional and the personal fields over the past years ii Contents List of Abbreviations vi List of Tables viii List of Figures ix Chapter 1: Introduction 1.1 Introduction to networked robot systems 1.2 Applications of networked robot systems .2 1.2.1 Industrial networked robots 1.2.2 Educational networked robots 1.2.3 Medical networked robots 1.2.4 Service networked robots .5 1.2.5 Other networked robots 1.2.6 Networked robots in Vietnam 1.3 Related works 1.3.1 Study of NRSs on localization 1.3.2 Study of NRSs on stabilization control .10 1.3.3 Study of NRSs on navigation 11 1.4 The goal of the research .13 1.5 The organization of this thesis .14 Chapter 2: System Model .16 2.1 State-space representation of the NRS 16 2.2 The communications network .22 2.2.1 Network types .22 2.2.2 Network characteristics .25 2.3 The Robot 29 2.3.1 Hardware configuration 29 2.3.2 Data communications 35 2.4 Conclusion 42 Chapter 3: Localization Using Optimal Filter .43 iii 3.1 Robot localization 43 3.2 Localization of NRSs 44 3.3 Localization of NRSs using past-observation based extended Kalman filter 45 3.3.1 The standard Kalman filter 46 3.3.2 Optimal filter for linear NRSs 47 3.3.3 Optimal filter for nonlinear NRSs 54 3.4 Implementation of the PO-EKF for the differential-drive network robot 55 3.4.1 Measurement of network state 55 3.4.2 Implementation of the prediction phase .56 3.4.3 Implementation of the correction phase .57 3.5 Simulations 58 3.5.1 Simulation setup 58 3.5.2 Simulation result 63 3.6 Experiments 66 3.6.1 Experimental setup .67 3.6.2 Experimental result .68 3.7 Discussion 73 3.8 Conclusion 75 Chapter 4: Motion Control Using Lyapunov Stability Theory and Predictive Filter 76 4.1 Introduction 76 4.2 Problem formulation 77 4.3 Stabilization of non-networked robot system 81 4.4 Stabilization of NRS 83 4.5 Simulations 85 4.6 Experiments 90 4.7 Discussion 92 4.8 Conclusion 95 Chapter 5: Navigation Using Behavior-based Model 96 iv 5.1 Introduction 96 5.2 Behavior-based navigation for NRSs 98 5.2.1 User following 99 5.2.2 Obstacle avoidance .102 5.2.3 Goal reaching 105 5.2.4 Supervisory module 105 5.3 Simulations 106 5.4 Experiments 110 5.5 Conclusion 116 Chapter 6: Conclusion 117 List of Publications Related to This Thesis 121 References 123 v List of Abbreviations 2D Two-dimensional 3D Three-dimensional ADSL Asymmetric digital subscriber line ALCAP Access link control application part ARP Address resolution protocol CAN Controller area network CCD Charge-coupled device CSMA/CD Carrier sense multiple access with collision detection DARPA Defense advanced research projects agency EKF Extended Kalman filter FIP Factory instrumentation protocol GPS Global positioning system GUI Graphic user interface HTTP Hypertext transfer protocol ICMP Internet control message protocol ID Identifier IETF Internet engineering task force IGMP Internet group management protocol IP Internet protocol IRTP Interactive real-time protocol ISP Internet service providers LAN Local area network LAS Link active scheduler LEKF Lucas-extended Kalman filter LRF Laser range finder MAC Media access control vi MSSR Multi-sensor smart robot NRS Networked robot system NRSs Networked robot systems NS2 Network simulator ns-2 PC Personal computer PID Proportional-integral-derivative PO-EKF Past observation-based extended Kalman filter PO-KF Past observation-based Kalman filter PROFIBUS Process field bus QoS Quality of service RARP Reverse address resolution protocol RMSEs Root mean square errors RTCP RTP control protocol RTP Real-time transport protocol RTT Round-trip time STCP Stream control transmission protocol SNRP Simple network robot protocol TCP Transmission control protocol TEAR TCP emulation at receivers TICP Transparent inter-process communication UDP User datagram protocol VINT Virtual internetwork test VPN Virtual private network vii Chapter Conclusion Though NRS is a young field of robotics, it has been actively employed in industry, medicine, education, services, and many other applications In Vietnam, NRS has gained the research interest in recent years and is expecting to yield new way of interaction to deal with urgent problems such as transportation and surveillance This thesis studies some fundamental problems of NRS including the localization, stability control, and navigation The goal is to realize new and effective algorithms contributing to the development of NRSs In order to achieve the goal, the system model is first defined and formulated The network is modeled as a module between the process and controller which delivers input signals and observation measurements with possible delay, loss, and out-of-order The delay is assumed to be random, but measurable at each sampling time; the out-of-order is considered as a long delay; and the packet loss is modeled as a binary random variable From those assumptions, a discrete time-varying statespace model of the NRS has been derived and employed as basis for the development of algorithms A real NRS system has been developed to serve experiments It consists of a mobile robot connected to a remote controller via the Internet The robot is the type with two wheels and differential drive It contains basic components for sensing and navigation including drive motors for motion control, sonar ranging sensors for obstacle avoidance, compass and GPS sensors for heading and global positioning, and laser range finder (LRF) and vision system for mapping and navigation The remote controller contains a joystick as input device and a graphic user interface with con117 trol algorithms running at background The data communications is carried out by a multi-protocol model This model utilizes the TCP for the administrative data, UDP for the control signals, and RTP for the live video The problem of localization is addressed from the optimal filtering approach The Kalman filter’s theory is applied to derive a new filter called past observationbased extended Kalman filter (PO-EKF) This filter combines knowledge of the robot kinematics, network state, and feedback measurements to give an optimal estimate of the robot’s pose A relevance factor that reflects the relevance of past measurement to the present is derived to compensate the influence of network The simulation and experiment results confirms the accuracy and computational efficiency of the filter The stabilization control is investigated based on the combination between the Lyapunov technique and the predictive filter First, the Lyapunov stability theory is employed to derive control laws that stabilizes the non-networked robot system Those control laws are then extended to the NRS by implementing a predictive filter between the sensor and controller The filter compensates the influence of the network to acquire an accurate estimate of the system state and consequently ensures the convergence of the control laws The structure of filter includes the PO-EKF plus a time-based extrapolated phase Simulations and experiments were implemented to verify the validity of the control scheme Finally, the results of the localization and stabilization control are integrated into a behavior-based model to deal with the navigation problem The model divides a complex task into individual behaviors Each behavior deals with a different aspect of the network and environment A supervision layer is used to handle the coordination between behaviors It determines the priorities of behaviors based on sensory inputs and then blends their output to give a final decision Simulations and experiments were conducted to validate the applicability of the proposed approach 118 Main contributions The main contributions of this study are as follows: • Development of a unified state-space representation of the NRS under the influence of network delay, message loss, and out-of-order delivery This representation has been adopted to deal with fundamental problems of NRSs A real NRS was developed as the platform for experiments and evaluations A multiprotocol model was proposed for the data communications between components of the NRS The model utilizes advantages of individual transport protocols in delivering certain types of communications data to enhance the communications performance These results were published in [1, 2, 3, 4, 5, 10] • A new optimal filter namely the PO-EKF was proposed for the problem of state estimation and localization of NRSs The filter can deal with the mixed uncertainties of network delay, message loss, and out-of-order delivery The optimality of the filter in term of minimizing the mean square error was theoretically proven The expansion of the filter to non-linear NRSs was derived A number of simulations, comparisons, and experiments were conducted The results confirmed the accuracy, computational efficiency, and implemental capability of the filter These results were published in [12, 13] • A control algorithm to stabilize the NRS was proposed It basically based on the approach in [88], but a new predictive filter was introduced to improve the accuracy and extend the functionality of the controller to deal with not only the network induced delay but also the message loss and out-of-order These results were published in [8, 9] • Development of a behavior-based navigation model to navigate the networked robot in unknown environments Fuzzy logic was employed to increase the adaptation of system to the network Simulations and experiments in various 119 environments proved the efficiency of the proposed model These results were published in [6, 7, 11] Future works Though this study presents significant results on NRSs, expanding those results to systems with multi networked robots would be an interesting problem In this configuration, sensory information does not assist specific robots but shares among them The localization and control algorithms need abilities to handle the heterogeneous data supplied from different sources with the synchronization and sampling mismatches The algorithms also have to be scalable to work with the partial rather than the complete set of information An initial approach for this problem could the development of a sensor fusion algorithm using multiple sampling rate 120 List of Publications Related to This Thesis Trần Quang Vinh, Phùng Mạnh Dương, Trần Hiếu (2005), “Giám sát điều khiển robot di động qua mạng LAN vô tuyến Internet”, Tạp chí khoa học Tự nhiên Cơng nghệ, Đại học Quốc gia Hà Nội, Tập 21, số 2, tr.85-91 Trần Quang Vinh, Vũ Tuấn Anh, Phùng Mạnh Dương, Trần Hiếu (2006), “Xây dựng robot di động dẫn đường cảm biến siêu âm cảm biến ảnh toàn phương”, Hội nghị Cơ điện tử toàn quốc lần thứ (VCM), tr.153-160 Manh Duong Phung, Quang Vinh Tran, Kok Kiong Tan (2010), “Transport Protocols for Internet-based Real-time Systems: A Comparative Analysis,” The Third International Conference on Communications and Electronics (ICCE) Phùng Mạnh Dương, Quách Công Hoàng, Vũ Xuân Quang, Trần Quang Vinh (2010), “Điều khiển robot di động qua mạng Internet sử dụng kiến trúc truyền thông CORBA”, The International Conference on Engineering Mechanics and Automation (ICEMA), pp.232-237 Trần Quang Vinh, Phạm Mạnh Thắng, Phùng Mạnh Dương (2010), “Mạng thông tin điều khiển hệ thống tự động hóa tịa nhà”, Tạp chí Khoa học Tự nhiên Công nghệ, Đại học Quốc gia Hà Nội, Tập 26, số 2, tr.129-140 Manh Duong Phung, Thanh Van Thi Nguyen, Cong Hoang Quach, Quang Vinh Tran (2010), “Development of a Tele-guidance System with Fuzzy-based Secondary Controller”, The 11th IEEE International Conference on Control, Automation, Robotics and Vision (ICARCV), pp.1826-1830 Manh Duong Phung, Thanh Van Thi Nguyen, Tran Quang Vinh (2011), “Control of an Internet-based Robot System Using Fuzzy Logic”, The 2011 IEICE International Conference on Integrated Circuits and Devices in Vietnam (ICDV), pp.98-101 Phùng Mạnh Dương, Nguyễn Thị Thanh Vân, Trần Thuận Hoàng, Trần Quang Vinh (2012), “Điểu khiển ổn định robot di động phân tán qua mạng máy tính 121 dụng lọc dự đoán với quan sát khứ”, Hội nghị Cơ điện tử Toàn quốc lần thứ (VCM), tr.778-786 T H Hoang, P M Duong, N V Tinh, T Q Vinh (2012), “A Path Following Algorithm for Wheeled Mobile Robot Using Extended Kalman Filter”, The 3rd IEICE International Conference on Integrated Circuits and Devices in Vietnam (ICDV), pp.179-183 10 Manh Duong Phung, Thuan Hoang Tran, Thanh Van Thi Nguyen and Quang Vinh Tran (2012), “Control of Internet-based Robot Systems Using Multi Transport Protocols”, 2012 IEEE International Conference on Control, Automation and Information Sciences (ICCAIS), pp.294-299 11 P M Duong, T T Hoang, N T T Van, D A Viet and T Q Vinh (2012), “A Novel Platform for Internet-based Mobile Robot Systems”, The 7th IEEE 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another, and