Luận án Developing efficient localization and motion planning systems for a wheeled mobile robot in a dynamic environment

Chapter 1

INTRODUCTION

1.1. Motivation

Mobility is an essential navigation issue for autonomous mobile robots.

To allow the mobile robots to navigate safely in a real-world environment,

the mobile robots must deal with four typical functional blocks of the

navigation system [1], as shown in Fig. 1.1, including: (i) perception {

the mobile robots must interpret its sensors to extract meaningful information; (ii) localization { the mobile robots must determine their position and orientation in the environment. In other words, it answers the

question \Where am I?"; (iii) motion planning { includes path planning

techniques and obstacle avoidance methods. The mobile robots utilize it

to decide how to act to achieve its goals; and (iv) motor control { the

mobile robots must modulate their motor outputs to achieve the desired

trajectory, i.e. PID control

It has been known that, the success in robot navigation requires the

1success of the four aforementioned fundamental processes, and to improve the performance of the robot’s navigation system, the performance

of all processes need to be improved.

In recent years, several domestic researches in the field of robotics

have been published in recent years, such as the publications of Vietnam Academy of Science and Technology, Institute of Information Technology, Hanoi University of Science and Technology, Vietnam National

University, Le Quy Don Technical University and Ho Chi Minh City University of Technology. The domestic works mainly focus on trajectory

tracking systems [2], [3], [4] and [5]. Specifically, the authors propose

control laws which enable the mobile robots to follow predefined trajectories. In this research, the authors also stated that, using these control

laws the systems can overcome uncertainties caused by dynamic parameter variations and external disturbances. In other research direction,

some studies [6] and [7] propose algorithms based on extended Kalman

filter (EKF) to improve the localization system for mobile robots in unknown environments. And a few works [8], [9] develop adaptive control

algorithms for tracking moving targets by using image features of the

target which get from a camera system. Finally, a little researches introduce a trajectory planning method in a static environment with known

start point and target point [10]. As a result, the mobile robot navigation system, especially localization and motion planning systems has

not been focused and adequately researched.

Therefore, in this research we only focuses on two interesting systems

including localization and motion planning systems, which are the scope

2of the thesis.

Localization is the problem of estimating a robot’s pose relative to

its environment from sensor observations. It has been referred as the

most fundamental problem to provide the mobile robot with autonomous

competences. The challenges of localization are from the inaccuracy and

inadequacies of sensors and effects of noise. Firstly, the errors of the measurement model, or sensor noise, due to the structural characteristics,

resolution and error tolerance of different types of sensors or dynamic

environments, such as light conditions, obstacles. Clearly, the solution

here is to take multiple readings into account or multi-sensor fusion to

increase the overall information from inputs. Secondly, the errors also

can be caused by systematic errors (deterministic) such as the size of

uneven wheels, the distance between two unbalance wheels, and they

can be eliminated by proper calibration of the system. However, there

are still a number of non-systematic (random) errors that remain, such

as slipping on the surface, changes in the contact points of the wheel are

uneven [1], leading to uncertainties in position estimation over time. In

addition, when the mobile robot navigates in the harsh environmental

conditions, the information can be interrupted in a short or long interval

of time. Therefore, the mobile robot might have insufficient information

for estimating the pose during its navigation.

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Luận án Developing efficient localization and motion planning systems for a wheeled mobile robot in a dynamic environment
MINISTRY OF EDUCATION AND TRAINING MINISTRY OF NATIONAL DEFENCE
MILITARY TECHNICAL ACADEMY
NGUYEN THI LAN ANH
DEVELOPING EFFICIENT LOCALIZATION
AND MOTION PLANNING SYSTEMS
FOR A WHEELED MOBILE ROBOT
IN A DYNAMIC ENVIRONMENT
DOCTORAL DISSERTATION: CONTROL ENGINEERING AND AUTOMATION
HA NOI - 2021
MINISTRY OF EDUCATION AND TRAINING MINISTRY OF NATIONAL DEFENCE
MILITARY TECHNICAL ACADEMY
NGUYEN THI LAN ANH
DEVELOPING EFFICIENT LOCALIZATION
AND MOTION PLANNING SYSTEMS
FOR A WHEELED MOBILE ROBOT
IN A DYNAMIC ENVIRONMENT
DOCTORAL DISSERTATION
Major: CONTROL ENGINEERING AND AUTOMATION
Code: 092520216
SUPERVISOR: Assoc. Prof. Dr. Pham Trung Dung
HA NOI - 2021
ASSURANCE
I certify that this dissertation is a research work done by the author
under the guidance of the research supervisors. The dissertation has
used citation information from many different references, and the ci-
tation information is clearly stated. Experimental results presented in
the dissertation are completely honest and not published by any other
author or work.
Author
Nguyen Thi Lan Anh
ACKNOWLEDGEMENTS
First of all, I would like to express my sincere gratitude to my advisor,
Assistant Professor Pham Trung Dung, who has been directly guiding
me through the PhD progress. His passionate enthusiasm, unwavering
dedication to research, and insightful advice have motivated me to carry
out this research. I do appreciate all support and opportunities that he
has provided to me.
Then, I wish to thank my co-supervisor my co-supervisor, Dr. Truong
Xuan Tung, for his valuable advices on my research. He has given and
discussed a lot of new issues with me. Working with Dr. Tung, I have
learnt how to do research systematically. His support have motivated
me to overcome all challenges in during my PhD journey.
Next, I also would like to thank the leaders and all lecturers of the Fac-
ulty of Control Engineering, Military Technical Academy for supporting
me with favorable conditions and cheerfully helping me in the study and
research process.
Finally, I must express my very profound gratitude to my parents,
to my husband for unfailing support me and always encouraging, to my
daughter, Tran Nguyen Khanh An, and my son, Tran Duc Anh for trying
to grow up by themselves. This accomplishment would not have been
possible without them.
Author
Nguyen Thi Lan Anh
CONTENTS
Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
List of figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
List of tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
Chapter 1. INTRODUCTION. . . . . . . . . . . . . . . . . . . . . . . . 1
1.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2. Objectives. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3. Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.4. Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.5. Dissertation outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Chapter 2. BACKGROUND . . . . . . . . . . . . . . . . . . . . . . . . 12
2.1. Mobile robot models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.1.1. Mobile robot platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.1.2. Kinematic model of differential-drive robot . . . . . . . . . . . . . 15
2.2. Bayesian filters for localization systems . . . . . . . . . . . . . . . . . . . . . 17
2.2.1. Extended Kalman filter algorithm . . . . . . . . . . . . . . . . . . . . . . 18
2.2.2. The particle filter algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.3. Typical obstacle avoidance algorithms . . . . . . . . . . . . . . . . . . . . . 24
2.3.1. The dynamic window approach algorithm. . . . . . . . . . . . . . . 25
2.3.2. Hybrid reciprocal velocity obstacle model . . . . . . . . . . . . . . . 30
i
2.3.3. Timed elastic band technique . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.4. Conclusions of the chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Chapter 3. SENSOR DATA FUSION-BASED LO-
CALIZATION ALGORITHMS . . . . . . . . . . . . . . . . . . . . . . . .
39
3.1. Extended Kalman filter-based localization algorithm. . . . . . . . 40
3.1.1. Construction of EKF-based localization algorithm . . . . . . 42
3.1.2. Results and discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.2. Particle filter-based localization algorithm . . . . . . . . . . . . . . . . . . 55
3.2.1. Construction of PF-based localization algorithm . . . . . . . . 57
3.2.2. Results and discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.3. Remarks and discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
Chapter 4. DEVELOPING EFFICIENT MOTION
PLANNING SYSTEMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.1. Proposed enhanced dynamic window approach algorithm . . . 70
4.1.1. Problem description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.1.2. Construction of the EDWA algorithm. . . . . . . . . . . . . . . . . . . 75
4.1.3. The EDWA algorithm-based navigation framework . . . . . 78
4.1.4. Algorithm validation by simulations and experiments . . . 79
4.1.5. Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
4.2. Proposed proactive timed elastic band algorithm . . . . . . . . . . . 90
4.2.1. Problem description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.2.2. Construction of the PTEB algorithm . . . . . . . . . . . . . . . . . . . 94
4.2.3. The PTEB algorithm-based navigation framework . . . . . . 97
4.2.4. Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
ii
4.2.5. Remarks and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
4.3. Proposed extended timed elastic band algorithm . . . . . . . . . . 104
4.3.1. Problem description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
4.3.2. Construction of the ETEB algorithm . . . . . . . . . . . . . . . . . . 107
4.3.3. Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
4.3.4. Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
4.4. Proposed integrated navigation system . . . . . . . . . . . . . . . . . . . . 113
4.4.1. Completed navigation framework . . . . . . . . . . . . . . . . . . . . . . 114
4.4.2. Experimental setup and results . . . . . . . . . . . . . . . . . . . . . . . . 120
4.4.3. Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
4.5. Conclusions and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
Chapter 5. CONCLUSIONS AND FUTURE WORKS
125
5.1. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
5.2. Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
5.3. Future works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
PUBLICATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
iii
ABBREVIATIONS
No. Abbreviation Meaning
1 IMU Inertial Measurement Unit
2 GPS Global Position System
3 KF Kalman Filter
4 EKF External Kalman Filter
5 PF Particle Filter
6 VO Velocity Obstacle
7 RVO Reciprocal Velocity Obstacle
8 HRVO Hybrid Reciprocal Velocity Obstacle
9 DWA Dynamic Window Approach
10 EDWA Enhance Dynamic Window Approach
11 EB Elastic Band
12 TEB Time Elastic Band
13 PTEB Proactive Time Elastic Band
14 ETEB Extended Time Elastic Band
15 ROS Robot Operating System
16 PCL Point Cloud Library
iv
LIST OF FIGURES
1.1 A general control scheme for autonomous mobile robots. . . 1
2.1 Two mobile robot platforms under the study. . . . . . . . . . 13
2.2 The global reference frame and the robot reference frame. . . 15
2.3 The velocity space of the dynamic window approach model.
Vs, Va, Vd are the possible velocities, admissible velocities,
and dynamic window, respectively. . . . . . . . . . . . . . . 26
2.4 Procedure of the hybrid reciprocal velocity obstacles of a
robot and an obstacle. . . . . . . . . . . . . . . . . . . . . . 30
2.5 TEB trajectory representation with n=3 poses . . . . . . . . 33
2.6 The example of exploration graph (a). The block diagram
of parallel trajectory planning of time elastic bands (b). . . . 36
3.1 The block diagram of the proposed autonomous mobile
robot localization systems based on the multiple sensor
fusion methods. . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.2 The data flow from sensors into the EKF for robot localization.46
3.3 The extended Kalman filter-based mobile robot localiza-
tion system. . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.4 The proposed approaches . . . . . . . . . . . . . . . . . . . . 49
3.5 The sinusoidal trajectories of the mobile robot in three
approaches. . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.6 The circular trajectories of the mobile robot in three ap-
proaches. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.7 The mean error and mean square error of the robot’s po-
sition of three approaches in two simulations. . . . . . . . . . 55
v
3.8 The simulation results using PF localization . . . . . . . . . 64
4.1 The navigation framework for autonomous mobile robot. . . 68
4.2 The example scenario of the dynamic environments in-
cluding a mobile robot and two dynamic obstacles. . . . . . 74
4.3 The efficient navigation system based on the EDWA algorithm78
4.4 The trajectory of the mobile robot and obstacles in Sce-
nario 1 and 2. . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.5 The trajectory of the mobile robot and obstacles in Sce-
nario 3 and 4. . . . . . . . . . . . . . . . . . . . . . . . . . . 84
4.6 The minimum passing distance along the robot’s trajectory. 85
4.7 The robot’s velocity along the trajectory of mobile robot. . . 86
4.8 (a) The Eddie mobile robot platform equipped with a laser
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