VIET NAM 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 SUMMARY OF DOCTORAL THESIS IN ELECTRONICS AND TELECOMMUNICATIONS TECHNOLOGY Hanoi - 2013     Thesis was completed at:   VNU University of Engineering and Technology Supervisor: Assoc Prof Dr Tran Quang Vinh Approved by: Thesis will be defended in Thesis can be refered at: - National Library of Vietnam - Library and Information Center, VNUH     Chapter 1: Introduction 1.1 Introduction to networked robot systems A networked robot, defined by the IEEE Society of Robotics and Automation, is a robotic device connected to a communications network 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 802.11 There are two subclasses of networked robot systems (NRSs) including autonomous and teleoperated systems NRSs call for the integration of several fields: robotics, perception (sensor systems), 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 communication networks The challenging issues include the guarantee of system reliability and performance under the influence of time-varying transmission delay, message loss, message out-oforder, and non-guaranteed transmission bandwidth Many new applications are now being developed ranging from automation to exploration 1.2 Applications of networked robot systems Appeared in 1994, the first NRS which permitted Internet users to operate a manipulator to excavate artifacts buried in a nuclear contaminated region to look for evidence of ancient water flows had received over 2.5 million accesses for seven months 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 crystals Today, networked robots have proved their applicability in industry (e.g coal mining), health (e.g telesurgery), education (e.g virtual laboratory), service (e.g cooperating guidance), and many other applications In Vietnam, NRS has gained the research interest and is expecting to yield new way of interaction to deal with urgent problems such as transportation and surveillance 1.3 Related work Adapting to this emerge field of robotics, there have been a number of projects dealing with problems involved in development of networked robots It is able to group those works into three topics: localization, stabilization control, and navigation In localization, the proposed approaches include advance interface techniques (e.g virtual map, telepresence, 3D reconstruction…) and optimal filters (Kalman filter and its improvements) In stabilization control, the concepts of predictive filter, time buffer, and event-based control were introduced In navigation, the methods are either direct or behavior-based approach Beside the strengths and weaknesses of each method, studies on NRS in general mainly deal with the time delay, hardly address the message loss and out-of-order delivery 1.4 The goal of the research Motivated from the wide applicability and active research, this work addresses the problem of supervision and control of NRSs The goal is to realize new and effective algorithms for the localization, stabilization control, and navigation of NRSs As networks are in general very complex and can greatly differ in their architecture and implementation depending on the medium used, and on the applications they are meant to serve, this work employs the Internet as the communication network and limits its influence factors to the time delay, message loss, and out-of-order delivery The robot is the type with two wheels, differential drive 1.5 The organization of this thesis     The thesis consists of chapters Chapter gives a brief overview of NRSs Chapter describes the model of the NRS The localization algorithm is introduced in chapter Chapter presents the design of the stable controller Chapter deals with the navigation problem The thesis ends with chapter which lists summary of the research, declaration of main contributions, and recommendation for future work Chapter 2: System Model the sensor to the controller The model of the NRS in state space is then written as: x k  f (x k 1 , kca n 1u k  n 1 , w k 1 ) z k  ksc m z k  m  ksc m h( x k  m , v k  m ) (2.4) where k   is the time index, x k   n is the state vector, u k   q is the input vector, z k   m is the measured output, f and h are the system functions, and wk and vk are respectively the process and measurement noises 2.1 State-space representation of the NRS 2.2 The communications network The model of the NRS used in this study is presented in figure 2.2 in which the process and controller are communicated through a communications network The network causes the delay, out-oforder, and loss to the data exchanged in the system The networks used in NRSs vary in communications protocols and topologies For industrial and transport applications, it is convenient to use fieldbuses (e.g FIP and PROFIBUS) and automotive buses (e.g CAN) For service, education, and some others, it is more appropriate to employ general purpose networks (e.g IEEE LAN’s and ATM-LAN) and the Internet Nevertheless, those networks introduce several common characteristics when being used for NRSs k+n λkcau Actuator Process k+n+1 Sensors x k+n+1 z Network (Delay, Loss, Out of Order)  k+n+m+1 λsck+n+1 z k u Controller Figure 2.2: Model of the networked robot system  Let n be the network delay (in number of sampling periods) between the controller and the actuator, m be the network delay between the sensor and the controller, kca be the binary random variable described the arrival of inputs from the controller to the actuator, ksc be the binary random variable described the arrival of measurements from ti  tk  ( j  i )Ts   Network delay: Network delay is inevitable and is in general time-varying Its value can be measured by reading the timestamp added to the sending message and then comparing it with the receiving time This method requires the internal clocks of sender and receiver to be synchronized Out-of-order delivery: Messages delivered through different routes may arrive in wrong order An out-of-order message with sequence number i arrived at time k (i  k ) equivalents to a delayed message with the time delay:   (2.10) where tk is the transfer time at time k, j is the sequence number of the last received chronologic message, and Ts is the sampling period  Message loss: Message loss is inevitable and can be defined as a binary random variable k :  1, if a message arrives during time k  1to k 0, otherwise k   (2.11) Figure 2.5: The robot’s pose and parameters The kinematic model of the robot in the continuous and discrete time domain is given by: 2.3 The robot We have developed a real NRS to serve as a platform for study and experiment Figure shows an overview of the system x  vc cos  y  vc sin     xk 1  xk  Ts vc (k ) cos  k yk 1  yk  Ts vc (k )sin  k (2.13) (2.16)  k 1   k  Tsc (k ) c where vc is the tangent velocity, ωc is the angular velocity, and Ts is the sampling period 2.3.2 Hardware configuration Figure 2.4: Overview of the developed NRS 2.3.1 Kinematic model The robot used in this study is the two wheeled, differential-drive mobile robot Its pose includes the position of the wheels axis center (x, y) and the chassis orientation  with respect to the X axis Figure 2.5 shows the coordinate systems and notations for the robot where (XG, YG) is the global coordinate, (XR, YR) is the local coordinate related to the robot chassis, R denotes the radius of driven wheels, and L denotes the distance between the wheels The hardware configuration contains two parts: actuators and sensors, and user-interaction devices (figure 2.4) The actuators and sensors include 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 user-interaction devices include a control computer and a joystick 2.3.3 Data communications The data communications is handled by a multi-protocol model The model utilizes different protocols for each type of the exchanged data so that the overall performance is enhanced The choice of protocols     is based on analysis of protocols in conjunction with the data exchanged in a NRS Three main transport protocols including the TCP, UDP, and RTP are analyzed TCP is a sophisticated protocol which was originally designed for the reliable transmission of static data such as e-mails and files over low-bandwidth, high-error-rate networks UDP is based on the idea of sending a datagram from a device to another as fast as possible without due consideration of the network state RTP is designed for the delivery of real-time multimedia data Simulations by ns-2 show that each protocol has its strengths and weaknesses so that there is no single protocol can simultaneously adequate for transmitting all types of data of the NRS Figure 2.16 shows the implementation of the multi-protocol model Experimental results show that the multi-protocol model is adequate for data communications between components of the NRS Data of the NRS can be classified into three groups: administrative data, control signals, and vision data    The administrative data contains the access control, user validation, and configuration information This type of data has small packet size with the bandwidth lower than 10Kbps In the implementation, the TCP is adopted for the communications of the administrative data The control signals include the control commands, synchronous messages, and sensory measurements This data consumes the bandwidth from 1Kbps to 100Kbps and requires real-time delivery With those features, the UDP is utilized to deliver the control signals The vision data is transmitted periodically with large packet size It consumes a lot of bandwidth and requires real-time delivery In our model, the RTP is employed for the transmission of the vision data Figure 2.16: Data communications in the NRS using multi-protocol model Chapter 3: Localization Using Optimal Filter 3.1 Robot localization Localization, the estimation of robot’s location relative to the environment, is the most fundamental problem to provide robots truly autonomous capabilities In order to complete a given task, the robot needs to know where it is Localization methods include the dead reckoning, absolute positioning, and sensor fusion 3.2 Localization of NRSs In NRSs, the localization faces new challenges related to the communications network In this study, a new localization algorithm based on the Kalman filter’s theory is proposed This algorithm is     K k  FPi  H iT [ H i Pi  H iT  Ri ]1 able to deal with mixed uncertainties of random delay, message loss, and out-of-order delivery 3.3 Localization of NRSs using past-observation based extended Kalman filter The localization algorithm is derived through two steps First, it is developed for the linear system It is then expanded to the nonlinear system Pk  Pk  K k H i Pi  F T  The time update equations at the prediction phase: xˆ k  f (xˆ k 1 , u k  n 1 , 0) Pk  Ak 1 Pk1 AkT1  Wk 1Qk 1WkT1 (3.7) m F   Ak  j ( I  K k  j H k  j )  ksc m H k  m x k  m  ksc m v k  m  H x  v i j 1 (3.8)   T ) 1 K k  FPi  H iT ( H i Pi  H iT  Vi RV i i i     xˆ  xˆ  K [z  h (xˆ , 0)] i k (3.10) The priori error covariance: Pk  Ak 1 Pk1 AkT1  Qk 1 (3.14) The posteriori state estimate (correction phase): xˆ k  xˆ k  K k (z ik  H i xˆ i ) k k i  k 3.4 Simulation The priori state estimate (prediction phase): xˆ k  Ak 1xˆ k 1  Bk 1u k  n 1 k (3.46) P  P  K k H i Pi  F T  k Using the Kalman filter’s theory, a new optimal filter can be derived as follows: Figure 3.13 compares the localization result of three methods: extended Kalman filter (EKF), improved extended Kalman filter (LEKF) [28], and our filter (PO-EKF) Table 3.2 shows the amount of floating point operations and the execution time of different filters, scaled with respect to the EKF It is concluded that the PO-EKF is better accurate than the EKF, as accurate as the LEKF, and less computational demanding than the LEKF (3.15) The Kalman gain and posteriori error covariance: 10   (3.45)  The data update equations at the correction phase: z ik  ksc m z k  m i (3.29) Expanding the filter to the nonlinear system by linearizing it around the previous estimates and then applying the above equations gives a new filter called the PO-EKF as follows: If the functions f and h in equation (2.4) are linear, the NRS can be rewritten as: x k  Ak 1x k 1  kca n 1 Bk 1u k  n 1  w k 1  Ak 1x k 1  Bk 1u k  n 1  w k 1 (3.30)   0.1 0.2 EKF PO-EKF LEKF Error in X (m) RMSE in X (m) 0.15 0.1 0.05 -0.1 -0.2 -0.3 EKF PO-EKF LEKF -0.4 200 400 600 Time (100ms) 800 1000 Parameter Floating point operations Execution time EKF LEKF PO-EKF 1.0 36.5 4.7 1.0 33.7 2.4 3.5 Experiment Figure 3.24 presents the experimental result with the network parameters measured as follows: the time delay is between 300ms and 500ms; the out-of-order rate is 2.4%; and the loss rate is 1.3% The PO-EKF is more accurate than the EKF, as accurate as the LEKF, and less computationally demanding than the LEKF This result is consistent with the theory and simulation results 150 Chapter 4: Stable Control using Lyapunov Stability Theory and Predictive Filter 4.1 Introduction In non-networked robot system, a number of researches have been proposed and the problem of stabilization control has been solved in both theoretic and experimental aspects In NRSs, several works have been introduced to deal with the stabilization problem, but they mainly focus on the time delay In this study, we present our approach with the use of the Lyapunov theory and predictive filter to deal with mixed uncertainties of random delay, message loss, and out-of-order data delivery 4.2 Problem formulation Consider the robot with kinematic model described in equation (2.13) Let the difference between the present pose ( x, y, ) and the 11   50 100 Time (100ms) Figure 3.24: Comparison between the EKF, LEKF, and PO-EKF Figure 3.15: Root mean square error of the EKF, LEKF, and POEKF in X direction Table 3.2: Normalized computational burden of the filters 12     v cos  goal pose ( x2 , y2 , ) given in the robot reference frame { X R , YR , R } be the error vector e  ( x2  x, y2  y,   )T The task of the controller layout is to find a control constraint, if it exists, of the tangent and angular velocities such that the error e is driven toward zero: lim e(t )      v   v (4.2)  sin   t  According to the work of Brockett [78], Cartesian state-space representations of the robot cannot be asymptotically stabilized via smooth and time invariant feedback laws A new coordinate system is defined with three parameters (,,) called navigation variables as shown in figure 4.2 and equation (4.1) sin  The goal now is to establish smooth control laws that drive the navigation variables (,,) toward zero Our approach consists of two steps First, control laws that stabilize the non-networked robot system are derived A predictive filter is then introduced to extend those control laws to the NRS 4.3 Stabilization of Non-networked Robot System Control laws to stabilize non-networked robot system are derived based on [74] Defining the Lyapunov function in the positive definite quadratic form as follows: V  V1  V2   2    h  ;  ,h  (4.3) We can prove that the derivation of the Lyapunov function V is always negative if the control laws are chosen as follows: Figure 4.2: The robot poses and navigation variables  x2  x    y2  y    atan  y2  y, x2  x      atan  y2  y, y2  x     v  ( cos  )  ;        (4.1) Without lost of generality, we assume that the final desired pose of the robot is ( x2 , y2 , )  (0,0,0) which can also be expressed by (  ,  , 2 )  (0,0,0) The kinematic equation (2.13) is then written in the navigation variables domain (,,) as:  (  h ) (4.8) Discretizing above equations gives the stable control laws in discrete time domain: vk  ( cos  k )  k wk   k   13   cos  sin  (4.5) cos  k sin  k k 14   ( k  hk ) (4.12) 4.4 Stabilization of NRS 4.5 Simulations and experiments Consider the NRS described in equation (2.4) Due to the network, the system is time-varying in which the control input at time k would not reach the actuator until time k+n whereas the measurement at time k actually reflects the system state at time k-m Thus, in order to ensure the stabilization of control laws (4.12), we need to predict the system state at time k+n based on the measurements taken at time km, xˆ ( k  n | k  m) (figure 2.2) Figure 4.11 presents the trajectories and orientations of the robot in three experiments in which the robot respectively starts from points (-4,-4,00), (-4,-4,450), and (-4,-4,900) to reach the destination (0,0,00) Figure 4.12 describe the tangent and angular velocities of the robot during the operation The robot goes toward the goal position while the velocities go to zero The system therefore is stable Orientation (degree) Y (m) -1 -2  The time update equations at prediction phase: -3 xˆ k  f k 1 (xˆ k 1 , u k 1 , 0) -4 Pk  Ak 1 Pk1 AkT1  Wk 1Qk 1WkT1 100 In chapter 3, the PO-EKF allows to estimate the present state from past observations If we add an extrapolated phase based on the time update equation, the PO-EKF can be augmented to estimate xˆ ( k  n | k  m) as follows: (4.15) degree 45 degree 90 degree -4 -3 -2 X (m) -1 60 40 20 0 (a) degree 45 degree 90 degree 80 20 40 Time (s) 60 80 (b)  The data update equations at correction phase: Figure 4.11: Stable control of the NRS with the use of the predictive filter: (a) Trajectory of the robot in the motion plane; (b) Variation of the direction of the robot m F   Ak  j ( I  K k  j H k  j ) j 1   T ) 1 K k  FPi  H iT ( H i Pi  H iT  Vi RV i i i    xˆ  xˆ  K [z  h (xˆ , 0)] k k k k (4.16) i P  P  K k H i Pi  F T  k  k  The predictive equation at extrapolated phase: xˆ k  n  f k  n 1 (xˆ k  n 1 , u k  n 1 , 0) (4.17) 15   16   0.4 behaviors that together result in the desired robot motion The key advantage of the behavior-based navigation is that it can adapts very quickly to the change of the network and operating environments without requiring the operator’s effort 0.2 5.2 Behavior-based navigation for NRSs degree 45 degree 90 degree 0.3 0.2 0.1 0 20 40 Time (s) Angular velocity (rad/s) Tangent velocity (m/s) 0.8 -0.2 60 degree 45 degree 90 degree 0.6 20 40 Time (s) 60 Figure 5.3 shows the architecture of the behavior-based navigation for NRSs It has three behaviors including the user following, obstacle avoidance, and goal reaching, and one supervisory module (b) (a) Figure 4.12: Velocities of the robot during the stable control with the use of the predictive filter: (a) Tangent velocity; (b) Angular velocity Chapter 5: Navigation Using Behavior-based Model 5.1 Introduction The final goal of most mobile robot systems is the ability to determine its own position in the environment and then drive towards some goal locations to complete given tasks This process is navigation and typically contains steps: perception, the robot interprets its sensors to extract meaningful data; localization, the robot determines its position in the environment; cognition, the robot decides how to act to achieve its goals; and motion control, the robot modulates its motor outputs to achieve the desired trajectory There are two main approaches in navigation including direct and behaviorbased navigations In direct navigation, operators set the primitive force or velocity commands to perform remote control Environmental information and robot states are transmitted in real time for displaying on the operator’s monitor Behavior-based navigation, on the other hand, uses the concept of designing sets of Figure 5.3: The architecture of our behavior-based navigation system The user following behavior translates high level commands of the teleoperator to low level control signals so that the robot moves accordantly with the teleoperator’s desire This behavior also updates the network state during the operation to tune control signals so that the system is more adaptive This behavior is implemented using fuzzy logic with four steps characterized for fuzzy systems: defining the problem, defining the linguistic variables and membership functions, building the fuzzy rules, and defuzzification 18 17     The obstacle avoidance behavior tends to avoid collisions with obstacles that are in the vicinity of the robot When appearing obstacles, this behavior uses the ultrasonic data to avoid them The fuzzy logic is employed to implement this behavior including four steps similarly to the user following behavior 20 F Obstacle 18 G Goal wall 16 E Obstacle 14 The goal reaching behavior uses the control algorithm described in chapter wall 12 Y(m) D Finally, the supervisory module determines the priority of behaviors and decides the control signals the actuators shall receive Inputs of the supervision module include the network states (delay, loss, outof-order) and values of the three sonar sensors (front, left, right) Its outputs are the rotational speeds of the left and right driven motors The operation of the supervision layer program is based on fuzzy rules such as “If context Then behavior” wall 10 C B Obstacle wall Obstacle Obstacle Start A Obstacle 10 X(m) 15 20 Figure 5.14: Result of the behavior-based navigation 5.3 Simulations and experiments Simulations and experiments have been carried out to evaluate the navigation model Figure 5.14 present the navigation result in unknown environment with many obstacles The robot succeeded in following user commands, avoiding obstacles and reaching the goal position The matching between control signal and network state confirmed the functioning operation of the proposed navigation model (figure 5.15-5.16) 2500 2000 B Time delay (ms) Delay Loss Out-of-order E C D G A F 1500 1000 500 0 200 400 600 800 1000 1200 Time (100ms) 1400 1600 1800 Figure 5.15: Network state during navigation 20 19     2000 Angular velocity (rad/s) 25 L B 20 R E 10 A F D  C 15 200 400 600 800 1000 1200 Time (100ms) G 1400 1600 1800 2000 Figure 5.16: Angular velocities of the left and right wheels during navigation  Chapter 6: Conclusion This study proposed algorithms for fundamental problems of NRS including the localization, stabilization control, and navigation The development of algorithms was carried out through steps of analyzing the applicability, evaluating the related research, formulating the system model, proposing the algorithms, and finally evaluating the performance through simulations and experiments  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 solve fundamental problems of NRSs A real NRS was developed as the platform for experiments and evaluations A multi-protocol 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 the 22 21   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 [31], 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 environments proved the efficiency of the proposed model These results were published in [6][7][11]   List of Publications 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 Công 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 Communication 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 tòa 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 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.294299 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 Conference on Industrial Electronics and Applications (ICIEA), pp.1969-1974 12 Manh Duong Phung, Thi Thanh Van Nguyen, Thuan Hoang Tran, and Quang Vinh Tran (2013), “Localization of Networked Robot Systems Subject to Random Delay and Packet Loss”, The 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp.1442-1447 13 Manh Duong Phung, Thi Thanh Van Nguyen, Thuan Hoang Tran, Quang Vinh Tran (2013), “Localization of Internet-based Mobile Robot”, Tạp chí Khoa học Tự nhiên Công nghệ, Đại học Quốc gia Hà Nội, Tập 29, số 1, tr 1-13 23   24   [...]... 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 7 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. .. 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.294299 11 P M Duong, T T Hoang, N T T Van, D A Viet and T... 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... Mobile Robot Systems”, The 7th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp.1969-1974 12 Manh Duong Phung, Thi Thanh Van Nguyen, Thuan Hoang Tran, and Quang Vinh Tran (2013), “Localization of Networked Robot Systems Subject to Random Delay and Packet Loss”, The 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp.1442-1447 13 Manh Duong Phung,... goals; and motion control, the robot modulates its motor outputs to achieve the desired trajectory There are two main approaches in navigation including direct and behaviorbased navigations In direct navigation, operators set the primitive force or velocity commands to perform remote control Environmental information and robot states are transmitted in real time for displaying on the operator’s monitor... uses the control algorithm described in chapter 4 wall 3 12 Y(m) D Finally, the supervisory module determines the priority of behaviors and decides the control signals the actuators shall receive Inputs of the supervision module include the network states (delay, loss, outof-order) and values of the three sonar sensors (front, left, right) Its outputs are the rotational speeds of the left and right... Behavior-based navigation, on the other hand, uses the concept of designing sets of Figure 5.3: The architecture of our behavior-based navigation system The user following behavior translates high level commands of the teleoperator to low level control signals so that the robot moves accordantly with the teleoperator’s desire This behavior also updates the network state during the operation to tune control signals... goal of most mobile robot systems is the ability to determine its own position in the environment and then drive towards some goal locations to complete given tasks This process is navigation and typically contains 4 steps: perception, the robot interprets its sensors to extract meaningful data; localization, the robot determines its position in the environment; cognition, the robot decides how to... present the navigation result in unknown environment with many obstacles The robot succeeded in following user commands, avoiding obstacles and reaching the goal position The matching between control signal and network state confirmed the functioning operation of the proposed navigation model (figure 5.15-5.16) 2500 2000 B Time delay (ms) Delay Loss Out -of- order E C D G A F 1500 1000 500 0 0 200 400... Network state during navigation 20 19     2000 Angular velocity (rad/s) 25 L B 20 R E 10 A F D 5 0  C 15 0 200 400 600 800 1000 1200 Time (100ms) G 1400 1600 1800 2000 Figure 5.16: Angular velocities of the left and right wheels during navigation  Chapter 6: Conclusion This study proposed algorithms for fundamental problems of NRS including the localization, stabilization control, and navigation