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E-raamat: Advanced Technologies in Modern Robotic Applications

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  • Formaat: PDF+DRM
  • Ilmumisaeg: 18-May-2016
  • Kirjastus: Springer Verlag, Singapore
  • Keel: eng
  • ISBN-13: 9789811008306
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Advanced Technologies in Modern Robotic ApplicationsSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 18-May-2016
  • Kirjastus: Springer Verlag, Singapore
  • Keel: eng
  • ISBN-13: 9789811008306
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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.
1 Introduction of Robot Platforms and Relevant Tools
1(26)
1.1 Robot Platforms
1(2)
1.1.1 Baxter® Robot
1(1)
1.1.2 iCub Robot
2(1)
1.2 Visual Sensors and Haptic Devices
3(4)
1.2.1 Microsoft Kinect Sensor
3(1)
1.2.2 Point Grey Bumblebee2 Stereo Camera
4(1)
1.2.3 Leap Motion Sensor
5(1)
1.2.4 SensAble Omni
5(1)
1.2.5 Novint Falcon Joystick
6(1)
1.3 Software Toolkits
7(8)
1.3.1 MATLAB Robotics Toolbox
7(7)
1.3.2 Official SDK of Leap Motion
14(1)
1.4 V-REP Based Robot Modeling and Simulations
15(5)
1.4.1 V-REP Simulator
16(1)
1.4.2 Examples of V-REP Simulation
17(3)
1.5 ROS Based Robot System Design
20(7)
1.5.1 Main Characteristics of ROS
21(1)
1.5.2 ROS Level Concepts
22(3)
References
25(2)
2 Robot Kinematics and Dynamics Modeling
27(22)
2.1 Kinematics Modeling of the Baxter® Robot
27(11)
2.1.1 Introduction of Kinematics
27(2)
2.1.2 Kinematics Modeling Procedure
29(6)
2.1.3 Experimental Tests on Kinematics Modeling
35(3)
2.2 Lagrange-Euler Dynamics Modeling of the Baxter Robot
38(11)
2.2.1 Introduction of Dynamics
38(1)
2.2.2 Dynamics Modeling Procedure
39(4)
2.2.3 Experimental Studies
43(4)
References
47(2)
3 Intelligent Control of Robot Manipulator
49(48)
3.1 Dual-Adaptive Control of Bimanual Robot
49(11)
3.1.1 Preliminaries
50(5)
3.1.2 Adaptive Control
55(3)
3.1.3 Simulation Studies
58(2)
3.2 Biomimetic Hybrid Adaptive Control of Bimanual Robot
60(12)
3.2.1 Preliminaries and Problem Formulation
61(2)
3.2.2 Adaptive Bimanual Control with Impedance and Force
63(3)
3.2.3 Adaptive Control with Internal Interaction
66(2)
3.2.4 Adaptive Control with Both Internal and External Interaction
68(4)
3.3 Optimized Motion Control of Robot Arms with Finite Time Tracking
72(11)
3.3.1 Robot Dynamics and Optimal Reference Model
74(4)
3.3.2 Adaptive Model Reference Control Design
78(5)
3.4 Discrete-Time Adaptive Control of Manipulator with Uncertain Payload
83(14)
3.4.1 Problem Formulation
83(1)
3.4.2 Discrete-Time Adaptive Control
84(4)
3.4.3 Simulation Studies
88(7)
References
95(2)
4 Object Detection and Tracking
97(60)
4.1 Introduction of Machine Vision Recognition
97(8)
4.1.1 Tools for Machine Vision
99(1)
4.1.2 Blob/Edge Detection
100(1)
4.1.3 Feature Point Detection, Description, and Matching
101(4)
4.2 JavaScript Object Notation (JSON)-Based Vision Recognition Framework
105(10)
4.2.1 JSON in Image Labels
107(3)
4.2.2 JSON in Application Tuning
110(2)
4.2.3 Vision Recognition Framework
112(3)
4.3 Deep Learning-Based Object Recognition
115(9)
4.3.1 Logistic Regression-Based Classification
115(2)
4.3.2 Convolutional Neural Network (CNN)-Based Classification
117(4)
4.3.3 Detection
121(3)
4.4 Tracking a Single Moving Object
124(6)
4.4.1 Data Collection
124(1)
4.4.2 Recognition Algorithm
125(3)
4.4.3 Analysis of Results
128(2)
4.5 Tracking Multiple Moving Objects
130(27)
4.5.1 PSO Algorithms
130(4)
4.5.2 Objective Function of the Irregular Shape Target
134(1)
4.5.3 Locating Multiple Targets by Adaptive PSO Method
135(4)
4.5.4 Tracking Multiple Targets by Swarm Optimization
139(5)
4.5.5 Experiments Studies
144(10)
References
154(3)
5 Visual Servoing Control of Robot Manipulator
157(30)
5.1 Introduction of Visual Servoing
157(3)
5.2 Kinect Sensor Based Visual Servoing for Human--Robot Cooperation
160(13)
5.2.1 System Architecture
160(1)
5.2.2 Experimental Equipments
161(1)
5.2.3 Implementation with V-REP
162(9)
5.2.4 Experiment Studies
171(2)
5.3 Visual Servoing Control Using Stereo Camera
173(14)
5.3.1 System Integration
174(1)
5.3.2 Preprocessing
175(2)
5.3.3 Algorithm Implementation
177(6)
5.3.4 Results
183(1)
References
183(4)
6 Robot Teleoperation Technologies
187(44)
6.1 Teleoperation Using Body Motion Tracking
187(8)
6.1.1 Introduction of Robot Teleoperation
187(1)
6.1.2 Construction of Teleoperation System
188(2)
6.1.3 Design Principles
190(4)
6.1.4 Experiment Study
194(1)
6.2 Fuzzy Inference Based Adaptive Control for Teleoperation
195(11)
6.2.1 System Modeling and Problem Formulation
195(6)
6.2.2 Fuzzy Inference Based Control
201(3)
6.2.3 Simulation Studies
204(2)
6.3 Haptic Interaction Between Human and Robot
206(12)
6.3.1 Tools Selection and System Description
207(4)
6.3.2 Implementation with CHAI3D
211(3)
6.3.3 Implementation with MATLAB
214(4)
6.4 Teleoperation Using Haptic Feedback
218(13)
6.4.1 System Description
218(1)
6.4.2 Workspace Mapping
218(5)
6.4.3 Command Strategies
223(2)
6.4.4 Experiment Studies
225(2)
References
227(4)
7 Obstacle Avoidance for Robot Manipulator
231(26)
7.1 Introduction of Kinematic Redundancy
231(2)
7.2 Shared Controlled Teleoperation with Obstacle Avoidance
233(12)
7.2.1 System Components
234(1)
7.2.2 Preprocessing
235(1)
7.2.3 Obstacle Avoidance Strategy
236(6)
7.2.4 Experiment Studies
242(3)
7.3 Robot Self-Identification for Obstacle Avoidance
245(12)
7.3.1 Kinect® Sensor and 3D Point Cloud
247(3)
7.3.2 Self-Identification
250(1)
7.3.3 Collision Predication
251(4)
7.3.4 Experiments Studies
255(1)
References
255(2)
8 Human--Robot Interaction Interface
257(46)
8.1 Introduction of Human--Robot Interfaces
257(2)
8.2 Hand Gesture-Based Robot Control Using Leap Motion
259(10)
8.2.1 Hardware and Software
260(1)
8.2.2 Control System
261(6)
8.2.3 Experiment and Result
267(2)
8.3 Hand Gesture-Based Control with Parallel System
269(10)
8.3.1 Platform and Software
269(1)
8.3.2 Hand Gesture Recognition System Based on Vision for Controlling the iCub Simulator
269(5)
8.3.3 Teleoperation Platform and Parallel System
274(5)
8.4 BCI Controlled Mobile Robot Using Emotiv Neuroheadset
279(11)
8.4.1 EEG and Brain--Computer Interface (BCI) System
280(3)
8.4.2 Experimental System
283(3)
8.4.3 Training and Control Strategy
286(3)
8.4.4 Results and Discussions
289(1)
8.5 EEG Signal-Based Control of Robot Manipulator
290(13)
8.5.1 Hardware and Software
291(1)
8.5.2 Experimental Methodology
291(6)
8.5.3 Discussion
297(1)
References
298(5)
9 Indoor/Outdoor Robot Localization
303(46)
9.1 Localization with Wireless Sensor Networks
303(14)
9.1.1 Problem Formulation
303(2)
9.1.2 Algorithm Design
305(6)
9.1.3 Theoretical Analysis
311(2)
9.1.4 Simulation Studies
313(4)
9.2 RFTD-based Indoor Localization Using Interval Kalman Filter
317(7)
9.2.1 Interval Kalman Filter for RFID Indoor Positioning
317(2)
9.2.2 Mathematical Model and Positioning Algorithm
319(2)
9.2.3 Simulation Studies
321(3)
9.3 Particle Filter-Based Simultaneous Localization and Mapping (PF-SLAM)
324(9)
9.3.1 Model of Particle Filter (PF) SLAM Using Landmarks
325(2)
9.3.2 Particle Filter Matching Algorithm
327(1)
9.3.3 Landmark Set Selection Method
328(1)
9.3.4 Advanced Position Calculation Method
329(1)
9.3.5 Experiment Study
330(3)
9.4 Integrated INS/VMS Navigation System
333(16)
9.4.1 Introduction of INS/VMS Navigation System
333(2)
9.4.2 Analysis of VMS Errors
335(1)
9.4.3 Loosely Coupled INS/VMS
336(3)
9.4.4 Tightly Coupled INS/VMS
339(3)
9.4.5 Experiment Study
342(4)
References
346(3)
10 Multiagent Robot Systems
349(44)
10.1 Introduction to Multiagent System
349(2)
10.2 Optimal Multirobot Formation
351(12)
10.2.1 Concepts and Framework of Multirobot Formation
352(4)
10.2.2 Minimum-Time Three-Robot Line Formation
356(5)
10.2.3 Simulation Results
361(2)
10.3 Multirobot Cooperative Pursuit
363(9)
10.3.1 Preliminary Concepts
364(3)
10.3.2 Hunting Strategy
367(3)
10.3.3 Simulation Study
370(2)
10.4 Multirobot Cooperative Lifting
372(21)
10.4.1 Problem Formation
373(1)
10.4.2 PD Feedforward Compensation Control
374(6)
10.4.3 Adaptive Control
380(11)
References
391(2)
11 Technologies for Other Robot Applications
393
11.1 Investigation of Robot Kicking
393(12)
11.1.1 Kinematics
393(3)
11.1.2 Ballistics
396(3)
11.1.3 Structure of the Robot
399(1)
11.1.4 MATLAB Simulation
400(1)
11.1.5 Implementation and Tests
400(5)
11.2 Reference Trajectory Adaptation
405
11.2.1 Interaction Dynamics
406(1)
11.2.2 Adaptation Model
407(1)
11.2.3 Convergence Analysis
408(5)
11.2.4 Simulation Studies
413(5)
References
418
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.
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