Study on supervision and control of robot over computer network nghiên cứu vấn đề giám sát và điều khiển robot qua mạng máy tính

vi List of Abbreviations ADSL Asymmetric digital subscriber line ALCAP Access link control application part ARP Address resolution protocol CAN Controller area network CCD Charge-couple

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

ĐẠI HỌC QUỐC GIA HÀ NỘI -

TRƯỜNG ĐẠI HỌC CÔNG NGHỆ

LUẬN ÁN TIẾN SĨ NGÀNH CÔNG NGHỆ ĐIỆN TỬ - VIỄN THÔNG

NGƯỜI HƯỚNG DẪN KHOA HỌC: PGS.TS Trần Quang Vinh

Hà Nội - 2015

i

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

ii

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-

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

iii

Contents

List of Abbreviations vi

List of Tables viii

List of Figures ix

Chapter 1: Introduction 1

1.1 Introduction to networked robot systems 1

1.2 Applications of networked robot systems 2

1.2.1 Industrial networked robots 2

1.2.2 Educational networked robots 3

1.2.3 Medical networked robots 4

1.2.4 Service networked robots 5

1.2.5 Other networked robots 6

1.2.6 Networked robots in Vietnam 6

1.3 Related works 7

1.3.1 Study of NRSs on localization 7

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

iv

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

v

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

vi

List of Abbreviations

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

IETF Internet engineering task force

IGMP Internet group management protocol

IRTP Interactive real-time protocol

ISP Internet service providers

LAS Link active scheduler

LEKF Lucas-extended Kalman filter

LRF Laser range finder

MAC Media access control

vii

MSSR Multi-sensor smart robot

NRS Networked robot system

NRSs Networked robot systems

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

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

viii

List of Tables

Table 2.1: Summary of data classification and transport protocols 40

Table 3.1: Normalized computational burden of filters 64

Table 3.2: Normalized computational burden of filters 66

Table 3.3: Network parameters during experiments 71

Table 5.1: Fuzzy rules of the user following behaviors 102

Table 5.2: Fuzzy rules of the obstacle avoidance behavior 105

Table 5.3: Rules for determination of behaviors 106

ix

List of Figures

Figure 1.1: The coal mining networked robot system[36] 3

Figure 1.2: A telerobotic laboratory platform[82] 4

Figure 1.3: The telesurgical system [14] 5

Figure 1.4: Service networked robots in urban areas [62] 6

Figure 1.5: Schematic structure of the thesis 15

Figure 2.1: The robot’s pose and parameters 17

Figure 2.2: Model of the NRS 20

Figure 2.3: Timing of signals in the NRS 21

Figure 2.4: Clock offset estimation 27

Figure 2.5: Overview of the developed NRS 29

Figure 2.6: Hardware configuration of the robot 30

Figure 2.7: Operation of the compass sensor 31

Figure 2.8: Operation of the omni-directional camera 32

Figure 2.9: Operation of the laser range finder [68] 32

Figure 2.10: Communications scheme between sensors and actuators 33

Figure 2.11: Graphic user interface of the control software written in Microsoft Visual C++ 34

Figure 2.12: Network topology in simulations 37

Figure 2.13: Characteristics of RTP, TCP and UDP in case of no network congestion 37

Figure 2.14: Characteristics of RTP, TCP and UDP in case of network congestion 38

Figure 2.15: Data communications in the NRS using multi-protocol model 40

Figure 2.16: Network state during the experiment 41

Figure 2.17: Images transmitted by two different transport protocols 41

Figure 3.1: An ordinary theory trajectory of the robot in the motion plane 62

x

Figure 3.2: An extreme theory trajectory of the robot in the motion plane 62

Figure 3.3: The theory, true and observation trajectories in X direction 62

Figure 3.4: The EKF, LEKF, PO-EKF, and true trajectories in the motion plane 63

Figure 3.5: RMSE of the EKF, LEKF, PO-EKF estimation and the true trajectories in X direction 63

Figure 3.6: RMSE between the EKF, LEKF, PO-EKF and the true trajectories in Y direction 64

Figure 3.7: RMSE between the EKF, LEKF, PO-EKF and the true trajectories in orientation 64

Figure 3.8: The EKF, LEKF, PO-EKF, and true trajectories of the robot in the motion plane 65

Figure 3.9: RMSE between the EKF, LEKF, PO-EKF and the true trajectories in X direction 65

Figure 3.10: RMSE between the EKF, LEKF, PO-EKF and the true trajectories in Y orientation 65

Figure 3.11: RMSE between the EKF, LEKF, PO-EKF and the true trajectories in orientation 65

Figure 3.12: Experimental configuration with local Internet service providers 68

Figure 3.13: Experimental configuration with VPN connections to the servers located at the United State 68

Figure 3.14: Comparison between the EKF, LEKF and PO-EKF in local configuration 70

Figure 3.15: Comparison between the EKF, LEKF and PO-EKF in VPN configuration 72

Figure 3.16: Estimate by the PO-EKF with uniform distribution noise 73

Figure 3.17: Estimate by the PO-EKF with non-zero mean noise 74

Figure 4.1: The goal of the controller 77

Figure 4.2: The robot poses and navigation variables 80

Figure 4.3: NRS with the presence of a state estimator 84

xi

Figure 4.4: Stable control of the non-networked robot system 87

Figure 4.5: Velocities during the stable control of the non-networked robot system 88

Figure 4.6: Stable control of the NRS without using the predictive filter 88

Figure 4.7: Velocities of the robot during the stable control without using the predictive filter 89

Figure 4.8: Stable control of the NRS with the use of the predictive filter 89

Figure 4.9: Velocities of the robot during the stable control with the use of the predictive filter 90

Figure 4.10: Setup of the experimental environment 91

Figure 4.11: Stable control of NRS with the use of the predictive filter 91

Figure 4.12: Velocities of the robot during the stable control with the use of the predictive filter 92

Figure 4.13: Trajectories of the robot during the stable control with different control parameters 93

Figure 4.14: RMSE of the predictive filter with the time-out value set to 5 seconds 94

Figure 5.1: General navigation scheme for mobile robot systems 97

Figure 5.2: An architecture of the behavior-based navigation 98

Figure 5.3: The architecture of our behavior-based navigation system 99

Figure 5.4: Membership function of the network delay input 101

Figure 5.5: Membership function of the command state input 101

Figure 5.6: Membership function of the adjusting ratio output 101

Figure 5.7: The membership functions of input variables D right , D forward , and D left 104 Figure 5.8: The membership functions of output variables ωL and ωR 104

Figure 5.9: Configuration of sonar sensors in simulations 108

Figure 5.10: Result of the behavior-based navigation 109

Figure 5.11: Network state during navigation 110

Figure 5.12: Angular velocities of the left and right wheels during navigation 110

xii

Figure 5.13: Experimental setup with the start position, target position, 111 Figure 5.14: Result of the behavior-based navigation 112 Figure 5.15: Network state during the user following process 113 Figure 5.16: Angular velocities of the left and right wheels during the user-

following process 113

Figure 5.17: Distance to objects measured by the front sonar during navigation 114 Figure 5.18: Angular velocities of the left and right wheels during navigation 115 Figure 5.19: A sequence of images showing the motion of robot during the

navigation 115

1

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 netnet-work could be wired or wireless, and based on any of a variety of protocols such as TCP, UDP, or 802.11.”

To reflect a broader set of problems and applications, the definition was

extend-ed to include two subclasses of networkextend-ed 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) 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

com-2

• Teleoperated NRSs: In teleoperated systems, human operators send commands

to the robot The robot executes the commands and returns feedback to the 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

oper-NRSs call for the integration of several fields: robotics, perception (sensor 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

sys-1.2 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

1.2.1 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 1.1(a)] 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 1.1(b)] Advantages of the system include the lation of human labor from the harsh environments and the endurance of the robot

iso-in hard work Difficulties relate to the problem of sensor fusion and localization

3

Figure 1.1: The coal mining networked robot system[36]: (a) The robot with

actua-tors and sensors; (b) The operator with input devices and user graphic interfaces

1.2.2 Educational networked robots

Networked robots enable the development of telelaboratory In [82], a telelaboratory was developed for the training of robotics Via the Internet and world wide web, students are able to connect to the laboratory to operate a real manipulator (figure 1.2) The students also have access to other facilities such as the teach pendant and control computer to fulfill the practical lesson Advantages of telelaboratory include the sharing of expensive systems among educational units, the ease in evaluation process, and the flexibility in practice

4

Figure 1.2: A telerobotic laboratory platform[82]: (a) The telelaboratory with

ro-botic devices; (b) The web-based interface to interact with the telelaboratory

1.2.3 Medical networked robots

Medical networked robots bring users chance to be diagnosed and treated remotely

by experienced doctors In [53], Masuda developed a telerobot for echography mote operators use two joysticks to control: one moves the robot; the other adjusts the ultrasonic probe’s angle At each stop position, the robot captures an ultrasonic image, compresses it, and sends it over the Internet to the examiner In [14], a tele-surgical system was developed Two slave manipulators are placed at patient’s site They are controlled by two joysticks located at operator’s site The interaction be-tween the operator and patient is conducted with the support of tactile and force feedback sensors (figure 1.3)

Re-5

Figure 1.3: The telesurgical system [14]: (a) Setup of slave manipulators around

the operating table; (b) An experiment with the surgeon and patient

1.2.4 Service networked robots

Networked robots can supply services at home, office, and public areas In [62],

Sanfeliu et al developed new ways of cooperation between networked robots and

human beings Robots have tasks of guidance, assistance, transportation, and veillance in urban areas Their architecture integrates cooperating urban robots, in-telligent sensors (video cameras, acoustic sensors, etc.), intelligent devices (PDA, mobile telephones, etc.) and communications (figure 1.4)

sur-6

Figure 1.4: Service networked robots in urban areas [62]: (a) A service robot; (b)

The robot interacts with human to supply information; (c) Robots cooperate in a

guidance task

1.2.5 Other networked robots

Networked robots can be applied in other various applications In [50], Luo duced a robot for remote surveillance The robot has five sensory subsystems for the fire detection, intruder detection, motor diagnosis, obstacle detection, and environ-ment detection In [4], the robot named GARBI was developed for the tasks of ob-servation and manipulation in shallow water environments It has a 3D vision sys-tem to support the remote control In [65], a prototype rover was designed as a test-and-validation vehicle for a Mars technology program Other applications of net-worked robots include pet robot, tour-guide robot, intelligent wheelchair, etc [49,

intro-51, 81]

1.2.6 Networked robots in Vietnam

In Vietnam, several works on NRSs have been proposed recently In [1], Cong Thanh developed a telemanipulator system The system consists of two manipula-tors (master and slave) communicated through a local area network During the op-eration, human operators act on the master manipulator At remote site, the slave

7

manipulator mimics the master based on signals received via the network In [78], a telehealthcare system was developed In the system, cameras are arranged at pa-tient’s house to record and transmit the vision data to a remote processing central If abnormal behaviors are detected, the system will send messages to doctors In [37],

a NRS for searching high radioactivity regions in a disaster area was introduced The system uses a partial swarm optimization algorithm to enable the cooperation between the robots to fulfill the task

Though the proposed works are mainly preliminary results, they prove the plicability of NRSs in Vietnam In fact, NRSs would be also applicable to emerging fields in Vietnam such as traffic control, ubiquitous healthcare, and underwater ex-ploration

ap-1.3 Related works

Adapting to this emerge field of robotics, there have been a number of projects ing with problems involved in networked robots This section reviews studies on the localization, stabilization control, and navigation which are topics covered in this thesis

deal-1.3.1 Study of NRSs on localization

Localization is fundamental to the functioning operation of robotic systems In der to complete a given task, the robot needs to know its own state in the environ-ment In NRSs, there are two approaches in localization including advance interface techniques and optimal filters

or-A Localization using advance interface techniques

Advance interface techniques locate the robot by reconstructing the operating ronment In [86], a virtual environment which is proportional to the real dimension

envi-8

of the working environment is constructed at client site Before commands are sent

to the server program, they are processed in the virtual environment to predict the upcoming position of the robot Based on it, the system can tolerate a certain amount of time delays as well as allow users to experience the interaction with the robot as in the real environment In [74], the model-based virtual reality technique was used to construct the polygonal model of the complete 3D world The operator controls the robot by issuing commands to the model An extension of this tech-nique called ecological interface paradigm was introduced in [55] It uses multiple sets of information to create a 3-D virtual environment that is augmented with the real video information

On another note, Hou and Su put four cameras in the robot field as external ual sensors to formulate four adjacent grids of vision without the dead zone [34] A recognition algorithm is implemented to recognize and give out the relative location

vis-of the robot, target, landmark and obstacle symbols In [31], the robot pose is mated at the local site using odometry, sonar, and compass sensors This infor-mation is then transmitted to the remote site as the pose of the robot without consid-ering the its change during the communications In [41], a map-based localization method was proposed The absolute position of the robot is determined by compar-ing a reference map of the local site with the one built by the GPS map building technique at the remote site

esti-The limitation of advance interface techniques is that they avoid dealing with the key issues of communications networks, the network delay, message loss, and out-of-order delivery The data transmission between the remote controller and the actuator is often treated as a given condition and rarely touched From the viewpoint

of estimation theory, significant delay and loss are equivalent to the inaccuracy in state estimation and control that can easily downgrade the system performance

9

B Localization using optimal filters

The optimal filtering method incorporates parameters of the network into the space representation of the NRS and then uses the estimation theory to locate the

state-robot In [71], Sinopoli et al introduced an augmented Kalman filter for state

esti-mation with intermittent observations The arrival of observations is modeled as a

binary random variable λ t Its variance is defined as the variance of output noise if λ t

is 1, and as σ 2 I otherwise, where σ is a real constant and I is the identity matrix The

Kalman filter is then reformulated using a “dummy” observation (if a real tion does not arrive) and takes the limit as σ → ∞ This approach was expanded in [64] to simultaneously deal with random delay and packet drop In this work, the arrival of observations is also defined as a binary random variable, but an infinite buffer is introduced to store and rearrange delayed measurements The estimation is then computed from the initial to the latest available measurements at each step Due to the iteration, this filter is very computationally demanding A finite buffer can be used to lighten the problem, but it requires the time delay to be bounded

observa-Recently, Moayedi et al [54] have introduced an adaptive Kalman filter for

networked control systems with mixed uncertainties of random delays, packet outs, and missing measurements This work uses a set of matrices to represent the mixed uncertainties and add them into the system model The filter gain is then de-rived by solving a set of recursive discrete-time Riccati equations On a similar note, Ma and Sun [52] proposed an optimal filter to deal with random sensor delays, packet dropouts, and uncertain observations Three Bernoulli distributed random variables with known distributions are introduced to generate a unified model of the mixed uncertainties Based on it, a Riccati equation and a Lyapunov equation are solved to retrieve the estimator Both filters have advantages in dealing with net-work induced problems They are however not optimal for the out-of-order delivery

drop-In addition, those filters are designed for linear systems Further modifications are required to extend them to nonlinear systems such as NRSs

10

1.3.2 Study of NRSs on stabilization control

Similar to classic robot systems, stability is an important criteria of NRSs In the

literature, several works have been proposed for this problem Wargui et al

devel-oped a stable controller for NRSs with nonholonomic constraints [88] In this work, control laws that stabilize the system in delay-free scenario are first derived They are then extended to work with the time delay by adding a multistep predictive es-timator between the system output and the controller In [15], the maximum time delay allowed at the control input without losing the system stability is estimated Based on it, a single layered neural network is designed to stably control the net-worked robot In [47], Luck used a time buffer which was longer than the worst de-lay to make the system to be time-invariant and then applied the classic control the-ory In [89], Xi and Tarn proposed an event-based (non-time) control scheme to re-duce the impact of time delay on the system operation

In addition to the above main approaches, some other interesting works were

al-so introduced In [72], the minimum jerk motion model is employed to predict the input from the user a time into the future This predicted input is then used to stabi-lize the remote robot In [35], multiple commands with all possible happened delay are sent in one packet to ensure the relevant of control signal

It is recognizable from the above approaches that they mainly focus on dealing with the time delay However, communications over the network is influenced by not only the time delay but also other various problems such as the message loss, out-of-order delivery, and limited transmission bandwidth These problems affect the control in such a complicated way that may cause the overall system to be un-stable

es to act to the teleoperator’s hands In [85], a haptic device was also implemented, but the author focused on the communications A compensative parameter that re-flects the time delay was proposed to continuously adjust the feedback transfer function of the control loop As the result, the force feedback to the operator was as exact as at the time of measurement Advantage of force feedback is the supply of operators with sense of interaction However, control architectures still need to be developed to ensure the functioning operation of the whole system

In [31], a direct control architecture for Internet-based personal robot, which is insensitive to the time delay is introduced Main components of the architecture in-cludes a command filter to recover the loss information of control commands and a path generator and a path-following controller to reduce the time difference between the real and virtual robots In addition, a posture estimator is employed to overcome the difference between the two environments In another approach, Wang and Liu introduced the telecommanding concept [87] In telecommanding, each command is designed for an independent task and is defined with multiple events so that the ro-

fu-To overcome the above disadvantage, the notion of event-based control was proposed in [29] With the event-based controller, the robot follows a time-

13

independent route planned by the operator to avoid a growing error caused by time delay or obstacle disturbance If an obstacle is encountered on the route, the robot will stop autonomously and the remote operator will assist it to detour the obstacle, then recover the original path In another approach, a fuzzy-neural adaptive model was introduced to navigate the robot in an Internet-based smart space [38] This model separates the system to nine subsystems, each corresponds to a discrete oper-ating point The delay encountered by the network transmission and processing time

is incorporated into the state-space dynamic model of each subsystem A linear transformation similar to the “Smith predictor” is applied to those time-delay sub-systems to obtain the delay-free subsystems Based on the delay-free subsystems, a fuzzy-neural adaptive controller is designed to maintain the stable and functioning operation of the system

Similar to studies on stable control, researches on navigation mainly deal with the time delay, hardly address other various network problems such as the message loss and message out-of-order In addition, noises in the system are often ignored In practice, noises are unavoidable and may downgrade the system performance if ap-propriate compensation is not considered

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 of the research is to realize new and effective algorithms for the localization, stabilization control, and naviga-tion which are fundamental problems 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 uses the Internet as the communications network and limits its influence factors to the time delay, message loss, and out-of-order delivery

14

The localization algorithm will be designed using optimal filter approach A unified state-space presentation of the system is constructed to describe the influ-ence of networked induced problems Based on it, estimation theory is applied to formulate a new filter for the localization of NRS

The stable controller will be developed using the Lyapunov stability theory and optimal filter The Lyapunov stability theory is employed to derive the control laws that stabilize the non-networked robot system Those control laws are then extended

to the NRS by using optimal filters

Finally, the navigation will be addressed using the behavior-based approach This approach combines the results of localization and stable control into a single architecture This architecture divides the complex navigation task into separate be-haviors to execute The coordination between the behaviors will be handled by the fuzzy logic The accuracy of the localization and control is required to be 30 cm in position and 100 in orientation for the functioning operation of the navigation

1.5 The organization of this thesis

The thesis consists of six chapters organized as shown in figure 1.5 Chapter 1 gives

a brief overview of the NRS It discusses applications and reviews related works The chapter ends with the research goal and thesis structure

Chapter 2 describes the system model A state-space representation of the NRS

is introduced with the existence of network parameters Details of each parameter are then analyzed Finally, the kinematic model, hardware configuration, and data communications of the NRS are presented

The problem of localization is described in chapter 3 This chapter first

discuss-es related methods with the evaluation of their strengths and weakndiscuss-essdiscuss-es It then presents our algorithm from the theory to the simulations and experiments

15

Chapter 1Introduction

Chapter 2System model

Chapter 4Motion control using Lyapunov theory and predictive filter

Chapter 5Navigation using behavior-

Figure 1.5: Schematic structure of the thesis

Chapter 4 describes the design of the stable controller It starts with the tion of the control laws that stabilize the non-networked robot system It then ex-plains the expansion of those control laws to the NRS The chapter ends with simu-lation and experiment results

deriva-The results from previous chapters are combined in chapter 5 to develop a gation model This chapter first highlights some navigation strategies It then de-scribes our behavior-based navigation with details of each behavior and experi-mental results

navi-The thesis ends with chapter 6 which lists summary of the research, declaration

of main contributions, and recommendation for future work

16

Chapter 2

System Model

2.1 State-space representation of the NRS

Consider a general robotic system with fundamental components including the troller, actuator, process, and sensor Its state-space representation in discrete-time domain is given by:

so that they are flexible and do not influence the kinematics of the robot The pose

of the robot is defined as the position of the wheels axis center (x, y) and the

orien-tation θ of the chassis with respect to the X axis Figure 2.1 shows the coordinate systems and notations for the robot where (XG, YG) is the global coordinate, (XR,

YR) is the local coordinate attached to the robot center, R denotes the radius of

driv-17

ing wheels, and L denotes the distance between the wheels It is assumed that the

robot is rigid moving on the plane with non-slipping and pure rolling

Figure 2.1: The robot’s pose and parameters

The kinematic model of the robot is then given by [30]:

where ωL and ωR are the angular velocities of the left and right wheels at its center

of rotation, respectively Let

18

be the tangential and angular velocities of the robot, equation (2.3) is rewritten as:

cossin

c c c

x v

y v

θθ

As the system operates over digital communications network, equation (2.6)

need be discretized Let T s be the sampling period, the discrete time model is

ob-tained from Taylor's expansion at k as:

( )cos( )sin( )

+ + +

2 2 2

2s t t c

T d e

dt =

where e is the approximation error and t c∈[ ,k k+ In our system, the sampling pe-1]riod is 100 ms resulting in the maximum approximation error of 0.5 cm This value definitely satisfies the accuracy requirement of the system which is 30 cm (radius of the robot chassis)

From the kinematic equation (2.8), vectors in the state equation (2.1) are defined for our system as follows:

• The input vector u contains setting angular velocities set by the controller to

the left and right driving wheels: [ ]T

c c

v ω instead of [ ]T

L R

ω ω for the convenience of notation

• The state function f is represented by equation (2.8) It relates the state vector

• The process noise w is modeled as the noise influencing the input This

ap-proach is adequate provided that the systematic errors are eliminated by the calibration process [77] Let a

ro-tached to the driving motors and the orientation θ is measured by the compass

sen-sor Detailed implementation of the sensors is presented in section 2.3.1 Based on it, vectors in the output equation (2.2) are defined for our system as follows:

• The output vector z includes the position and orientation of the robot:

x yθ

=

20

• The output equation (2.2) can be simply rewritten as:

= +

• Noise v are the measurement noise of sensors This noise and the process noise

are assumed to be independent, zero-mean, and white-noise processes with normal probability distributions The determination of their covariances is pre-sented in section 3.4

When distributing over communications networks, the robot system is ized and its functioning operation depends on a number of network parameters The 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

ControllerNetwork

uk-1

zk

Process xk

Figure 2.2: Model of the NRS

In this work, a network is modeled as a module between the process and ler which delivers input signals and observation measurements with possible delay,

control-loss, and out-of-order (figure 2.2) The delay is assumed to be random, but

measura-ble at each sampling time The out-of-order is considered as a long delay The packet

arrival is modeled as a binary random variable λk defined as follows [64]:

1, if a packet arrivesduring time 1to

λ be the binary random variable described the arrival of inputs from the controller

to the actuator, λk sc be the binary random variable described the arrival of

measure-ments from the sensor to the controller The NRS and its signal timing can be

de-scribed as in figure 2.3 The controller, which is a software program installed at the remote computer, first generates setting velocities [ ]T

(Delay, Loss, Out of Order)

Figure 2.3: Timing of signals in the NRS

At time k, the controller sends a control input u to the actuator Due to the

net-work delay or out-of-order n, the control signal arrives to the actuator at time k+n After one sampling period to the time k+n+1, the system state changes The sensor

updates this by taking the measurement z This measurement is transmitted over the

network and arrived the controller at time k+n+m+1 The controller employs the

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measurement as feedback information to start a new control cycle During the ation, there always exist probabilities that the control input and measurement are lost

oper-From the analysis, the robot state at time k is driven by the control input at time

k-n-1 while the received measurement at time k is actually taken at time k-m The

system (2.1) – (2.2) becomes time-varying and can be rewritten as follows:

communica-2.2 The communications network

Industrial communications networks were introduced in the 1970’s in car industry in order to reduce cost for cabling, ease the system setup, and modularize the system design Since then, various types of network have been developed to serve numerous demands of applications In NRSs, networks used 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

net-works (e.g IEEE LAN’s and ATM-LAN) and the Internet A short summary is now

given to describe some popular networks and their characteristics

2.2.1 Network types

Four types of network including the Foundation Fieldbus, CAN, Ethernet, and net will be briefly presented They are commonly used in NRSs

Inter-23

A Foundation fieldbus

The Foundation Fieldbus was developed by the organization of Fieldbus Foundation

in 1994 [83] It was intended for replacement of traditional 4 − 20 mA standard, but today it is widely installed in many heavy process applications such as oil refineries, chemical plants, paper mills and power generations Two communications protocols

of Foundation Fieldbus have been released to meet different needs of control and monitoring: H1 and HSE The first, H1, works at 31.25 Kbit/s and is used to connect the field devices such as sensors, actuators, and controllers The second, HSE (High-Speed Ethernet), uses 10 or 100 Mbps Ethernet as the physical layer and provides a high-speed backbone for the network A typical Foundation Fieldbus network topol-ogy has HSE connections between computers, and runs slower H1 links between the devices themselves By using bridges, more devices can be connected and a hierar-chical network topology can be built Access control in a Foundation Fieldbus net-work is controlled by link masters The link master that is currently controlling the bus is called the Link Active Scheduler (LAS) During runtime, the LAS will send commands to other devices, telling them when to broadcast data to the bus using a schedule The LAS also publishes time information and grants permission to devices

to allow them to broadcast unscheduled messages such as alarms and events

B CAN (Controller Area Network)

Development of CAN started originally in 1983 by the German company Bosch [84] It was designed specifically for automotive industry, but now also used in other areas such as aerospace, industrial automation and medical equipment CAN is de-fined in the ISO standards 11898 and 11519-1 The bit rate can be 1 Mbit/s if the network length is not longer than 40m, and 500 Kbit/s if the network length is longer than 40m In a CAN network, devices such as sensors and actuators are not directly connected to the bus, but through a node Each node requires three components: a transceiver to receive or transmit signals at bit level, a CAN controller to convert bits into messages and vice versa, and a host processor to process messages There is no

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limit to the number of nodes Each node is able to send and receive messages, but not simultaneously If the bus is free, any node may begin to transmit If two or more nodes begin sending messages at the same time, the message with the highest priori-

ty gets the right to use the bus There are 229 different priority levels for messages Messages with numerically smaller values of IDs have higher priority and are trans-mitted first

C Ethernet

Ethernet was standardized in 1985 as IEEE 802.3 [73] Since then, it has constantly evolved to meet new requirements and become one of the most used local area net-work (LAN) technologies Ethernet can transmit data with the speed from 10 Mbit/s

to 100 Gbit/s, but the most commonly used speeds are 10 Mbit/s, 100 Mbit/s, and

1000 Mbit/s An Ethernet segment is usually limited to 100 m in length Multiple Ethernet segments can be joined together by hubs or switches An almost unlimited number of stations can be connected to an Ethernet Each station has a unique Ether-net address defined as a sequence of six numbers separated by colons Each number corresponds to 1 byte The first three bytes are used as a vendor ID, and the last three bytes are defined by the vendor Ethernet uses a bus access method called CSMA/CD, Carrier Sense Multiple Access with Collision Detection The "carrier sense" means that all the stations can distinguish between an idle and a busy link The "collision detection" means that a station can detect when a transmitting frame has interfered with a frame transmitted by another, and if this happens, the colliding stations will back off, and try a retransmission after a random wait An Ethernet frame, or packet, is between 64 and roughly 1500 bytes in length A frame begins with 64-bit preamble for delimitation and synchronization, followed by a header fea-turing source and destination MAC addresses The middle section of the frame con-tains payload data which is between 46 and 1500 bytes The frame ends with a 32-bit cyclic redundancy check

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D Internet

Starting from ARPANET, the Internet was brought online in 1969 which initially connected four major computers at universities in the southwestern United State

[28] Through developments and evolutions, today, the Internet became a global data

communications system that links millions of private, public, academic and business networks via an international telecommunications backbone that consists of various

electronic and optical networking technologies As a network of networks, the

Inter-net is heterogeneous; for instance, data transfer rates and physical characteristics of connections vary widely The Internet has no centralized governance in either tech-nological implementation or policies for access and usage Every device on the In-ternet, such as a computer or mobile phone, is identified by a unique number called

IP address Each IP address consists of 6 bytes for IP version 6, and 4 bytes divided into five classes A, B, C, D, and E for IP version 4 In order to transmit data between hosts, the Internet uses the Internet protocol suite, also known as TCP/IP It is a model architecture that divides methods into a layered system including the applica-tion, transport, Internet, and link layers As the user data is processed down through the protocol stack, each layer adds an encapsulation at the sending host At the re-ceiving host, the encapsulation procedure is reversed

2.2.2 Network characteristics

Though networks are different in design and structure, and are in general complex in implementation, they share several common characteristics when being used for NRSs In this study, the network delay, message loss, and out-of-order delivery are investigated Addressing those characteristics allows control algorithms to be effec-tive for not only a specific type of network but also generic communications net-works