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 z 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. Dr Tran Quang Vinh Hanoi - 2015 z 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 z 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 z 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 Nation- al 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 do appreciate the National Foundation for Science and Technology Development (NAFOSTED) for their financial support for my attendance of international confer- ences. 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 z Contents List of Abbreviations. vi List of Tables. viii List of Figures. ix Chapter 1: Introduction.1 Introduction to networked robot systems .2 Applications of networked robot systems .1 Industrial networked robots .2 Educational networked robots .3 Medical networked robots .4 Service networked robots .5 Other networked robots .6 Networked robots in Vietnam .1 Study of NRSs on localization .2 Study of NRSs on stabilization control.3 Study of NRSs on navigation.4 The goal of the research.5 The organization of this thesis .14 Chapter 2: System Model .1 State-space representation of the NRS .2 The communications network .42 Chapter 3: Localization Using Optimal Filter .2 Localization of NRSs.3 Localization of NRSs using past-observation based extended Kalman filter 45 3.1 The standard Kalman filter .2 Optimal filter for linear NRSs .3 Optimal filter for nonlinear NRSs .4 Implementation of the PO-EKF for the differential-drive network robot .1 Measurement of network state .2 Implementation of the prediction phase .3 Implementation of the correction phase .75 Chapter 4: Motion Control Using Lyapunov Stability Theory and Predictive Filter .3 Stabilization of non-networked robot system .4 Stabilization of NRS .95 Chapter 5: Navigation Using Behavior-based Model .2 Behavior-based navigation for NRSs.
117 List of Publications Related to This Thesis .123 v z 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 z 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 z List of Tables Table 2.1: Summary of data classification and transport protocols .1: Normalized computational burden of filters .2: Normalized computational burden of filters .3: Network parameters during experiments .1: Fuzzy rules of the user following behaviors.2: Fuzzy rules of the obstacle avoidance behavior .3: Rules for determination of behaviors .106 viii z List of Figures Figure 1.1: The coal mining networked robot system[36] .2: A telerobotic laboratory platform[82] .3: The telesurgical system [14] .4: Service networked robots in urban areas [62] .5: Schematic structure of the thesis.1: The robot’s pose and parameters.2: Model of the NRS.3: Timing of signals in the NRS.4: Clock offset estimation.5: Overview of the developed NRS.6: Hardware configuration of the robot.7: Operation of the compass sensor.8: Operation of the omni-directional camera.9: Operation of the laser range finder [68] .10: Communications scheme between sensors and actuators.11: Graphic user interface of the control software written in Microsoft Visual C++.12: Network topology in simulations.13: Characteristics of RTP, TCP and UDP in case of no network congestion .14: Characteristics of RTP, TCP and UDP in case of network congestion .15: Data communications in the NRS using multi-protocol model.16: Network state during the experiment .17: Images transmitted by two different transport protocols .1: An ordinary theory trajectory of the robot in the motion plane.2: An extreme theory trajectory of the robot in the motion plane.3: The theory, true and observation trajectories in X direction.4: The EKF, LEKF, PO-EKF, and true trajectories in the motion plane.5: RMSE of the EKF, LEKF, PO-EKF estimation and the true trajectories in X direction.6: RMSE between the EKF, LEKF, PO-EKF and the true trajectories in Y direction.7: RMSE between the EKF, LEKF, PO-EKF and the true trajectories in orientation.8: The EKF, LEKF, PO-EKF, and true trajectories of the robot in the motion plane.9: RMSE between the EKF, LEKF, PO-EKF and the true trajectories in X direction.10: RMSE between the EKF, LEKF, PO-EKF and the true trajectories in Y orientation.11: RMSE between the EKF, LEKF, PO-EKF and the true trajectories in orientation.12: Experimental configuration with local Internet service providers.13: Experimental configuration with VPN connections to the servers located at the United State.14: Comparison between the EKF, LEKF and PO-EKF in local configuration .15: Comparison between the EKF, LEKF and PO-EKF in VPN configuration .16: Estimate by the PO-EKF with uniform distribution noise .17: Estimate by the PO-EKF with non-zero mean noise .1: The goal of the controller.2: The robot poses and navigation variables.3: NRS with the presence of a state estimator.4: Stable control of the non-networked robot system.5: Velocities during the stable control of the non-networked robot system .6: Stable control of the NRS without using the predictive filter .7: Velocities of the robot during the stable control without using the predictive filter .8: Stable control of the NRS with the use of the predictive filter .9: Velocities of the robot during the stable control with the use of the predictive filter .10: Setup of the experimental environment.11: Stable control of NRS with the use of the predictive filter .12: Velocities of the robot during the stable control with the use of the predictive filter .13: Trajectories of the robot during the stable control with different control parameters.14: RMSE of the predictive filter with the time-out value set to 5 seconds .1: General navigation scheme for mobile robot systems.2: An architecture of the behavior-based navigation.3: The architecture of our behavior-based navigation system.4: Membership function of the network delay input.5: Membership function of the command state input.6: Membership function of the adjusting ratio output.7: The membership functions of input variables Dright, Dforward, and Dleft.8: The membership functions of output variables ωL and ωR.9: Configuration of sonar sensors in simulations.10: Result of the behavior-based navigation.11: Network state during navigation.12: Angular velocities of the left and right wheels during navigation.13: Experimental setup with the start position, target position, .14: Result of the behavior-based navigation.15: Network state during the user following process.16: Angular velocities of the left and right wheels during the user- following process.17: Distance to objects measured by the front sonar during navigation.18: Angular velocities of the left and right wheels during navigation.19: A sequence of images showing the motion of robot during the navigation. 115 xii z Chapter 1 Introduction 1.1 Introduction to networked robot systems Appeared in 1994, the first networked robot system (NRS) had received over 2.5 million accesses for seven months. The system namely Mercury project permitted Internet users to operate a manipulator to excavate artifacts buried in a nuclear con- taminated region to look for evidence of ancient water flows [27]. In the next seven years, over forty such systems had been developed allowing users to remotely visit museums, tend gardens, navigate undersea, float in blimps, and handle protein crys- tals [25, 26, 48, 63, 76].
This new trend attracted the IEEE Society of Robotics and Automation and the name Networked Robots was created in 2004 with the follow- ing definition [62]: “A networked robot is a robotic device connected to a communications net- work such as the Internet or LAN. The network could be wired or wireless, and based on any of a variety of protocols such as TCP, UDP, or 802.” To reflect a broader set of problems and applications, the definition was extend- ed to include two subclasses of networked robot systems (NRSs): the autonomous systems and the teleoperated (manual) systems. • Autonomous NRSs: In autonomous systems, the controller generates control signals to operate the robot. The robot (including actuators and sensors) com- municates with the controller via the network.
The network extends the effective operating range of the robot, allowing multiple robots to communicate with each other over long distances to coordinate their activity. 1 z • Teleoperated NRSs: In teleoperated systems, human operators send commands to the robot. The robot executes the commands and returns feedback to the oper- ators via the network. Input devices and user graphic interfaces are used to sup- port the operator.
They act as a passive controller to translate operator’s com- mands to control signals. NRSs call for the integration of several fields: robotics, perception (sensor sys- tems), ubiquitous computing, artificial intelligence, and network communications. The topics of NRSs transcend ‘‘conventional’’ robotic problems such as localization and motion control to the type of distributed systems over heterogeneous communi- cations networks. The challenging issues include the guarantee of system reliability and performance under the influence of time-varying transmission delay, message loss, out-of-order delivery, and non-guaranteed transmission bandwidth.
Many new applications are now being developed ranging from automation to exploration.2 Applications of networked robot systems Applications of NRSs can be classified into five groups including industrial robots, educational robots, medical robots, service robots, and other various robots.1 Industrial networked robots Networked robots are applicable in industry. In [36], a teleoperated robot system for coal mining was developed. The robot is equipped with two cameras, two laser scanners, and some other sensors to extract the environmental parameters [figure 1. These parameters are then transmitted via the network to the human opera- tor.
The human operator, in a safe location, controls the robot to complete tasks such as shoveling and breaking [figure 1.