Advanced Technologies in Modern Robotic Applications (eBook)

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2016 | 1st ed. 2016
XIV, 419 Seiten
Springer Singapore (Verlag)
978-981-10-0830-6 (ISBN)

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Advanced Technologies in Modern Robotic Applications - Chenguang Yang, Hongbin Ma, Mengyin Fu
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This book presents in a systematic manner the advanced technologies used for various modern robot applications. By bringing fresh ideas, new concepts, novel methods and tools into robot control, robot vision, human robot interaction, teleoperation of robot and multiple robots system, we are to provide a state-of-the-art and comprehensive treatment of the advanced technologies for a wide range of robotic applications. Particularly, we focus on the topics of advanced control and obstacle avoidance techniques for robot to deal with unknown perturbations, of visual servoing techniques which enable robot to autonomously operate in a dynamic environment, and of advanced techniques involved in human robot interaction. The book is primarily intended for researchers and engineers in the robotic and control community. It can also serve as complementary reading for robotics at the both graduate and undergraduate levels.


Chenguang Yang is a young expert in robotics and control. He has made a number of significant achievements in robot control and was awarded the IEEE Transactions on Robotics Best Paper Award in 2011,  WCICA conference Steve and Rosalind Hsia Best Biomedical Paper Award in 2014, and the IEEE ICIA Conference Best Paper Award in 2015.

Hongbin Ma has a strong academic background in control theory and application, and has been an active expert in robotics research and education. He is a recipient of the 13th Huo Ying Dong Young Teacher Award for Higher Education in 2012. His research interest covers adaptation, learning and recognition, especially adaptive estimation ,as well as their applications in robots and autonomous systems.

Mengyin Fu is a professor of Cheung Kong Scholars Program, and famous expert in integrated navigation and intelligent navigation system. His research areas also cover image processing, machine learning and pattern recognition.

This book presents in a systematic manner the advanced technologies used for various modern robot applications. By bringing fresh ideas, new concepts, novel methods and tools into robot control, robot vision, human robot interaction, teleoperation of robot and multiple robots system, we are to provide a state-of-the-art and comprehensive treatment of the advanced technologies for a wide range of robotic applications. Particularly, we focus on the topics of advanced control and obstacle avoidance techniques for robot to deal with unknown perturbations, of visual servoing techniques which enable robot to autonomously operate in a dynamic environment, and of advanced techniques involved in human robot interaction. The book is primarily intended for researchers and engineers in the robotic and control community. It can also serve as complementary reading for robotics at the both graduate and undergraduate levels.

Chenguang Yang is a young expert in robotics and control. He has made a number of significant achievements in robot control and was awarded the IEEE Transactions on Robotics Best Paper Award in 2011,  WCICA conference Steve and Rosalind Hsia Best Biomedical Paper Award in 2014, and the IEEE ICIA Conference Best Paper Award in 2015.Hongbin Ma has a strong academic background in control theory and application, and has been an active expert in robotics research and education. He is a recipient of the 13th Huo Ying Dong Young Teacher Award for Higher Education in 2012. His research interest covers adaptation, learning and recognition, especially adaptive estimation ,as well as their applications in robots and autonomous systems.Mengyin Fu is a professor of Cheung Kong Scholars Program, and famous expert in integrated navigation and intelligent navigation system. His research areas also cover image processing, machine learning and pattern recognition.

Preface 5
Contents 8
1 Introduction of Robot Platforms and Relevant Tools 14
1.1 Robot Platforms 14
1.1.1 Baxter® Robot 14
1.1.2 iCub Robot 15
1.2 Visual Sensors and Haptic Devices 16
1.2.1 Microsoft Kinect Sensor 16
1.2.2 Point Grey Bumblebee2 Stereo Camera 17
1.2.3 Leap Motion Sensor 18
1.2.4 SensAble Omni 18
1.2.5 Novint Falcon Joystick 19
1.3 Software Toolkits 20
1.3.1 MATLAB Robotics Toolbox 20
1.3.2 Official SDK of Leap Motion 27
1.4 V-REP Based Robot Modeling and Simulations 28
1.4.1 V-REP Simulator 29
1.4.2 Examples of V-REP Simulation 30
1.5 ROS Based Robot System Design 33
1.5.1 Main Characteristics of ROS 34
1.5.2 ROS Level Concepts 35
References 38
2 Robot Kinematics and Dynamics Modeling 40
2.1 Kinematics Modeling of the Baxter® Robot 40
2.1.1 Introduction of Kinematics 40
2.1.2 Kinematics Modeling Procedure 42
2.1.3 Experimental Tests on Kinematics Modeling 48
2.2 Lagrange--Euler Dynamics Modeling of the Baxter Robot 51
2.2.1 Introduction of Dynamics 51
2.2.2 Dynamics Modeling Procedure 52
2.2.3 Experimental Studies 56
References 60
3 Intelligent Control of Robot Manipulator 62
3.1 Dual-Adaptive Control of Bimanual Robot 62
3.1.1 Preliminaries 63
3.1.2 Adaptive Control 68
3.1.3 Simulation Studies 71
3.2 Biomimetic Hybrid Adaptive Control of Bimanual Robot 73
3.2.1 Preliminaries and Problem Formulation 74
3.2.2 Adaptive Bimanual Control with Impedance and Force 76
3.2.3 Adaptive Control with Internal Interaction 79
3.2.4 Adaptive Control with Both Internal and External Interaction 81
3.3 Optimized Motion Control of Robot Arms with Finite Time Tracking 85
3.3.1 Robot Dynamics and Optimal Reference Model 87
3.3.2 Adaptive Model Reference Control Design 91
3.4 Discrete-Time Adaptive Control of Manipulator with Uncertain Payload 96
3.4.1 Problem Formulation 96
3.4.2 Discrete-Time Adaptive Control 97
3.4.3 Simulation Studies 101
References 108
4 Object Detection and Tracking 110
4.1 Introduction of Machine Vision Recognition 110
4.1.1 Tools for Machine Vision 112
4.1.2 Blob/Edge Detection 113
4.1.3 Feature Point Detection, Description, and Matching 114
4.2 JavaScript Object Notation (JSON)-Based Vision Recognition Framework 118
4.2.1 JSON in Image Labels 120
4.2.2 JSON in Application Tuning 123
4.2.3 Vision Recognition Framework 125
4.3 Deep Learning-Based Object Recognition 128
4.3.1 Logistic Regression-Based Classification 128
4.3.2 Convolutional Neural Network (CNN)-Based Classification 130
4.3.3 Detection 134
4.4 Tracking a Single Moving Object 137
4.4.1 Data Collection 137
4.4.2 Recognition Algorithm 138
4.4.3 Analysis of Results 141
4.5 Tracking Multiple Moving Objects 143
4.5.1 PSO Algorithms 143
4.5.2 Objective Function of the Irregular Shape Target 147
4.5.3 Locating Multiple Targets by Adaptive PSO Method 148
4.5.4 Tracking Multiple Targets by Swarm Optimization 152
4.5.5 Experiments Studies 157
References 167
5 Visual Servoing Control of Robot Manipulator 170
5.1 Introduction of Visual Servoing 170
5.2 Kinect Sensor Based Visual Servoing for Human--Robot Cooperation 173
5.2.1 System Architecture 173
5.2.2 Experimental Equipments 174
5.2.3 Implementation with V-REP 175
5.2.4 Experiment Studies 184
5.3 Visual Servoing Control Using Stereo Camera 186
5.3.1 System Integration 187
5.3.2 Preprocessing 188
5.3.3 Algorithm Implementation 190
5.3.4 Results 196
References 196
6 Robot Teleoperation Technologies 199
6.1 Teleoperation Using Body Motion Tracking 199
6.1.1 Introduction of Robot Teleoperation 199
6.1.2 Construction of Teleoperation System 200
6.1.3 Design Principles 202
6.1.4 Experiment Study 206
6.2 Fuzzy Inference Based Adaptive Control for Teleoperation 207
6.2.1 System Modeling and Problem Formulation 207
6.2.2 Fuzzy Inference Based Control 213
6.2.3 Simulation Studies 216
6.3 Haptic Interaction Between Human and Robot 218
6.3.1 Tools Selection and System Description 219
6.3.2 Implementation with CHAI3D 223
6.3.3 Implementation with MATLAB 226
6.4 Teleoperation Using Haptic Feedback 230
6.4.1 System Description 230
6.4.2 Workspace Mapping 230
6.4.3 Command Strategies 235
6.4.4 Experiment Studies 237
References 239
7 Obstacle Avoidance for Robot Manipulator 242
7.1 Introduction of Kinematic Redundancy 242
7.2 Shared Controlled Teleoperation with Obstacle Avoidance 244
7.2.1 System Components 245
7.2.2 Preprocessing 246
7.2.3 Obstacle Avoidance Strategy 247
7.2.4 Experiment Studies 253
7.3 Robot Self-Identification for Obstacle Avoidance 256
7.3.1 Kinect® Sensor and 3D Point Cloud 258
7.3.2 Self-Identification 261
7.3.3 Collision Predication 262
7.3.4 Experiments Studies 266
References 266
8 Human--Robot Interaction Interface 268
8.1 Introduction of Human--Robot Interfaces 268
8.2 Hand Gesture-Based Robot Control Using Leap Motion 270
8.2.1 Hardware and Software 271
8.2.2 Control System 272
8.2.3 Experiment and Result 278
8.3 Hand Gesture-Based Control with Parallel System 280
8.3.1 Platform and Software 280
8.3.2 Hand Gesture Recognition System Based on Vision for Controlling the iCub Simulator 280
8.3.3 Teleoperation Platform and Parallel System 285
8.4 BCI Controlled Mobile Robot Using Emotiv Neuroheadset 290
8.4.1 EEG and Brain--Computer Interface (BCI) System 291
8.4.2 Experimental System 294
8.4.3 Training and Control Strategy 297
8.4.4 Results and Discussions 300
8.5 EEG Signal-Based Control of Robot Manipulator 301
8.5.1 Hardware and Software 302
8.5.2 Experimental Methodology 302
8.5.3 Discussion 308
References 309
9 Indoor/Outdoor Robot Localization 313
9.1 Localization with Wireless Sensor Networks 313
9.1.1 Problem Formulation 313
9.1.2 Algorithm Design 315
9.1.3 Theoretical Analysis 321
9.1.4 Simulation Studies 323
9.2 RFID-based Indoor Localization Using Interval Kalman Filter 327
9.2.1 Interval Kalman Filter for RFID Indoor Positioning 327
9.2.2 Mathematical Model and Positioning Algorithm 329
9.2.3 Simulation Studies 331
9.3 Particle Filter-Based Simultaneous Localization and Mapping (PF-SLAM) 334
9.3.1 Model of Particle Filter (PF) SLAM Using Landmarks 335
9.3.2 Particle Filter Matching Algorithm 337
9.3.3 Landmark Set Selection Method 338
9.3.4 Advanced Position Calculation Method 339
9.3.5 Experiment Study 340
9.4 Integrated INS/VMS Navigation System 343
9.4.1 Introduction of INS/VMS Navigation System 343
9.4.2 Analysis of VMS Errors 345
9.4.3 Loosely Coupled INS/VMS 346
9.4.4 Tightly Coupled INS/VMS 349
9.4.5 Experiment Study 352
References 356
10 Multiagent Robot Systems 358
10.1 Introduction to Multiagent System 358
10.2 Optimal Multirobot Formation 360
10.2.1 Concepts and Framework of Multirobot Formation 361
10.2.2 Minimum-Time Three-Robot Line Formation 365
10.2.3 Simulation Results 370
10.3 Multirobot Cooperative Pursuit 372
10.3.1 Preliminary Concepts 373
10.3.2 Hunting Strategy 376
10.3.3 Simulation Study 379
10.4 Multirobot Cooperative Lifting 381
10.4.1 Problem Formation 382
10.4.2 PD Feedforward Compensation Control 383
10.4.3 Adaptive Control 389
References 400
11 Technologies for Other Robot Applications 402
11.1 Investigation of Robot Kicking 402
11.1.1 Kinematics 402
11.1.2 Ballistics 405
11.1.3 Structure of the Robot 408
11.1.4 MATLAB Simulation 409
11.1.5 Implementation and Tests 409
11.2 Reference Trajectory Adaptation 414
11.2.1 Interaction Dynamics 415
11.2.2 Adaptation Model 416
11.2.3 Convergence Analysis 417
11.2.4 Simulation Studies 422
References 427

Erscheint lt. Verlag 18.5.2016
Zusatzinfo XIV, 419 p. 269 illus., 233 illus. in color.
Verlagsort Singapore
Sprache englisch
Themenwelt Informatik Grafik / Design Digitale Bildverarbeitung
Informatik Software Entwicklung User Interfaces (HCI)
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Technik Elektrotechnik / Energietechnik
Technik Maschinenbau
Schlagworte Advanced Robotics • Biomimetic Robot Control • human robot interaction • intelligent robots • Multiple Robots • Robot Localization • Robot Teleportation • Robot vision • Shared Control of Robot
ISBN-10 981-10-0830-2 / 9811008302
ISBN-13 978-981-10-0830-6 / 9789811008306
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