Billingsley / Brett Machine Vision and Mechatronics in Practice

1. Auflage 2015
ISBN: 978-3-662-45514-2
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 343 Seiten

The contributions for this book have been gathered over several years from conferences held in the series of Mechatronics and Machine Vision in Practice, the latest of which was held in Ankara, Turkey. The essential aspect is that they concern practical applications rather than the derivation of mere theory, though simulations and visualization are important components.The topics range from mining, with its heavy engineering, to the delicate machining of holes in the human skull or robots for surgery on human flesh. Mobile robots continue to be a hot topic, both from the need for navigation and for the task of stabilization of unmanned aerial vehicles. The swinging of a spray rig is damped, while machine vision is used for the control of heating in an asphalt-laying machine. Manipulators are featured, both for general tasks and in the form of grasping fingers. A robot arm is proposed for adding to the mobility scooter of the elderly. Can EEG signals be a means to control a robot? Can face recognition be achieved in varying illumination?'

Autoren/Hrsg.

Billingsley, John

Brett, Peter

Inhaltsverzeichnis

1;Introduction;5
2;Contents;7
3;Control of Automated Mining Machinery Using Aided Inertial Navigation;10
3.1;1 Introduction;10
3.1.1;1.1 Continuous Miners;10
3.1.2;1.2 Remotely Supervised CM;11
3.2;2 Inertial Navigation;11
3.2.1;2.1 Strapdown Navigation Concepts;11
3.2.2;2.2 Non Contact VMS Sensing;13
3.2.3;2.3 Doppler Radar;13
3.3;3 Experimental Trials;13
3.3.1;3.1 Phoenix Test Platform;14
3.3.2;3.2 Design of System Validation Campaigns;15
3.4;4 Analysis of Results;16
3.5;5 Discussion;17
3.6;6 Summary;18
3.7;References;18
4;Scanning Radar System for Machine Guidance;19
4.1;1 Radar Sensor Selection and Development;19
4.2;2 Application;21
4.3;3 Data Acquisition;22
4.4;4 Data Processing;24
4.5;5 Conclusions and Future Work;28
4.6;References;29
5;Comparison of Scanning Laser Range-Findersand Millimeter-Wave Radarfor Creating a Digital Terrain Map;30
5.1;1 Introduction;30
5.2;2 Background;31
5.2.1;2.1 Sensor Performance Characteristics;32
5.3;3 Field Testing on Mining Equipment;33
5.3.1;3.1 Sensors Performance Characteristics;34
5.3.2;3.2 System Performance for Creating Digital Model;39
5.3.3;3.3 System Performance for Pose Estimation of Waiting Haul Truck;42
5.4;4 Conclusions;43
5.5;References;44
6;Distributed Collaborative Immersive Virtual Reality Framework for the Mining Industry;46
6.1;1 Introduction;46
6.2;2 Human-Computer System for Collaborative Environments;48
6.2.1;2.1 User Interfaces, Gestures and Feedback;49
6.2.2;2.2 Virtual and Augmented Realities, Computer Vision;50
6.2.3;2.3 Knowledge Base;52
6.2.4;2.4 Logical Flow Chart of the System;52
6.3;3 Experiment;53
6.4;4 Summary;54
6.5;References;55
7;Longwall Shearer Automation: From Research to Reality;56
7.1;1 Introduction;56
7.2;2 The Longwall Mining Process and the Need for Automation;57
7.3;3 New Enabling Technologies for Underground MiningAutomation;58
7.4;4 Technology Uptake and Commercial Reality;63
7.5;5 Summary;64
7.6;References;64
8;Robotic Orthopaedic Surgery:From Research to Spin-Off to Acquisition;65
8.1;1 Introduction;65
8.2;2 The Acrobot Robot;66
8.3;3 The Acrobot System;68
8.4;4 The Procedure;69
8.5;5 Spin-Off;69
8.6;6 Acquisition;70
8.7;7 Conclusions;71
8.8;References;72
9;Innovative Mechatronic Techniquesfor Contrasting Pressure Disturbancesin the Closed Space of Cochlea;73
9.1;1 Introduction;73
9.1.1;1.1 Description of the Cochlea;74
9.1.2;1.2 Creation of Membrane Windows Using the Mechatronic Drilling Technique;74
9.2;2 Experimental Measurement of Membrane Motion;77
9.3;3 Results;78
9.3.1;3.1 Cochlear Dynamic Behaviour;78
9.3.2;3.2 Manual and Robotic Cochleostomy;79
9.3.3;3.3 Electrode Insertion;80
9.4;4 Conclusion;81
9.5;References;81
10;A QBall UAV and Open TLD Integrationfor Autonomous Recognitionof Stationary and Moving Targets;83
10.1;1 Introduction;83
10.2;2 Test Equipment and System Integration;84
10.2.1;2.1 Quadrotor System and Its Dynamics;84
10.2.2;2.2 Control System;89
10.2.3;2.3 Wireless Video Acquisition System;89
10.2.4;2.4 OpenCV Based Recognition System;90
10.3;3 First Experiment: Aerial Surveillance of a Stationary Target;91
10.4;4 Second Experiment: Aerial Surveillance of a Moving Target;92
10.5;5 Conclusions;93
10.6;References;94
11;Disturbance Rejection Controlof a Quadrotor Equipped with a 2 DOF Manipulator;96
11.1;1 Introduction;96
11.2;2 Modelling of the System;97
11.3;3 Controller Design;101
11.4;4 Experiment Setup;102
11.5;5 Simulation;103
11.6;6 Conclusions and Future Works;107
11.7;References;107
12;Experimental Investigation of a Magneto-RheologicalFluid Damper with Permanent Magnetfor Haptic Finger Grasping;109
12.1;1 Introduction;109
12.2;2 Experimental Setup;110
12.3;3 Experimental Results;111
12.4;4 Conclusion;115
12.5;References;115
13;Game Development Tools for Simulating Robotsand Creating Interactive Learning Experiences;117
13.1;1 Brief History of 3D Gaming Graphics;117
13.2;2 Features of Modern Game Engines;119
13.3;3 Tools Used for Making 3D Games;124
13.4;4 Using Engineering CAD Tools to Create 3D Simulations;126
13.5;5 Example of 3D Simulation and Motion Control Code;128
13.6;6 Conclusions;135
13.7;References;136
14;A Mobile Manipulator Armfor Assisting the Frail Elderly and Infirm;139
14.1;1 Background;139
14.2;2 ESRA – Electric Scooter Robot Arm;141
14.3;3 Wrist Leveling Mechanism;144
14.4;4 Gripper Design;145
14.5;5 Stress and Deflection Analysis;146
14.6;6 Performance and Testing;148
14.7;7 Conclusions and Future Work;148
14.8;References;150
15;Dynamic Modeling and Control of a Novel ParallelManipulator Using Supervisory Approach;152
15.1;1 Introduction;152
15.2;2 Architecture Description;154
15.3;3 Dynamic Modeling;155
15.3.1;3.1 Simplification Hypothesis;155
15.3.2;3.2 Dynamic Modeling;156
15.4;4 The Control System Structure;157
15.4.1;4.1 Computed Torque Control via PD and PID Controller;157
15.4.2;4.2 Computed Torque Control via Fuzzy Supervisory Control;159
15.5;5 Simulation Results;161
15.6;6 Conclusions;165
15.7;References;165
16;Trajectory Control and Sway Suppressionof a Rotary Crane System;167
16.1;1 Introduction;167
16.2;2 The Rotary Crane System;168
16.3;3 Dynamic Modelling of the Rotary Crane;170
16.4;4 PD Control Scheme;171
16.5;5 Input Shaping Control Schemes;172
16.6;6 Implementation and Results;173
16.7;7 Conclusion;176
16.8;References;176
17;Holistic Control System Designfor Large Mobile Irrigation Machines;178
17.1;1 Introduction;178
17.2;2 Holistic Irrigation Control;180
17.3;3 Limited Applicability of Classical Control Approaches;181
17.3.1;3.1 Slow Speed of Crop Dynamics;181
17.3.2;3.2 In-Field Variability Sensing;182
17.3.3;3.3 Characteristics of the Irrigation Machine;182
17.3.4;3.4 Fundamental Resource Constraints;182
17.3.5;3.5 Unknown Process Dynamics;183
17.4;4 Practical Implementation;183
17.4.1;4.1 Adaptive Interpolation of System Inputs;184
17.4.2;4.2 Adaptive Spatially-Varied Identification;184
17.5;5 Conclusion;185
17.6;References;185
18;Controlled Damping of a 48-Metre Wide Spray Rig;186
18.1;1 Introduction;186
18.2;2 Background;186
18.3;3 Alternative Techniques;188
18.4;4 The Simple Concept;188
18.5;5 Practical Details;189
18.6;6 Spray Rig State Variables and Simulation;190
18.7;7 Experimental Measurement of the Parameters;191
18.8;8 Experimental Results;192
18.9;9 Simulation Code;194
18.10;10 Outline Strategy;195
18.11;11 Conclusions;196
18.12;References;196
19;Machine Vision Aided Locating for Microwave HeatingControl of the Asphalt Pavement Maintenance Vehicle;198
19.1;1 Introduction;198
19.2;2 Micro-Wave Heating Control System;199
19.3;3 Geometric Distortion Rectification of the Defects Image;201
19.3.1;3.1 Barrel Distortion Rectification;201
19.3.2;3.2 Oblique Distortion Rectification;202
19.3.3;3.3 Camera Calibration Tests and Results;202
19.4;4 Methods to Create Control Parameters;203
19.4.1;4.1 Automatic Defects Extraction by Computer;203
19.4.2;4.2 Manual Defects Extraction;206
19.5;5 The Driving Circuit Design;206
19.6;6 Conclusions;207
19.7;References;208
20;Design Concepts for an Energy-Efficient AmphibiousUnmanned Underwater Vehicle;209
20.1;1 Introduction;209
20.2;2 Advantages of Oscillating Foils over Rotary Propellers;211
20.3;3 Underwater Vehicles That Use Articulated Fins for Propulsion;212
20.4;4 Design of the Turtle UUV;216
20.5;5 Conclusions;222
20.6;References;223
21;Advanced Dynamic Path Controlof a 3-DOF Spatial Parallel RobotUsing Adaptive Neuro Fuzzy Inference System;224
21.1;1 Introduction;224
21.2;2 Kinematics Analysis;226
21.2.1;2.1 Architecture Description;226
21.2.2;2.2 Mobility of the Manipulator;227
21.2.3;2.3 Inverse Kinematics;227
21.2.4;2.4 Jacobian Matrix Generation;229
21.3;3 Dynamic Modeling;230
21.3.1;3.1 Dynamics Analysis;230
21.3.2;3.2 Simulation Study;231
21.4;4 Design of ANFIS Controller for TPM;233
21.5;5 Simulation Results;234
21.6;6 Conclusions;235
21.7;References;235
22;Low-Cost, Non-centralized, Vehicle CollisionPrevention System;237
22.1;1 Introduction;237
22.2;2 System Setup;239
22.3;3 Inertial Measurement Unit;240
22.4;4 GPS/INS Measurements Fusion Algorithm;243
22.5;5 Experimental Testing;244
22.6;6 Collision Forecasting;248
22.7;7 Conclusion;252
22.8;References;252
23;Encoding/Decoding Expressive EEG Signal VariabilityUsing IAF/ASDM Technique towards EEG-ControlledRobotic System Development;254
23.1;1 Introduction;254
23.2;2 EEG Signal Processing Model;255
23.3;3 Adaptive EEG Digital Decoding/Coding;256
23.3.1;3.1 EEG Signal Noise Modelling;257
23.3.2;3.2 EEG Spectral Analysis Structure;258
23.3.3;3.3 Parametric EEG Data Modelling;259
23.4;4 Methodology;259
23.4.1;4.1 Signal Acquisition;260
23.4.2;4.2 Pre-processing;260
23.4.3;4.3 Artefact Identification;260
23.4.4;4.4 Power Spectral Measures;260
23.5;6 Conclusion;263
23.6;References;264
24;Illumination Invariant Face RecognitionUsing Principal Component Analysis – An Overview;265
24.1;1 Introduction;265
24.2;2 Phases of a Typical Face Recognition;266
24.3;3 Illumination Normalization Methods for Face Recognition;267
24.3.1;3.1 The Single Scale Retinex Method;267
24.3.2;3.2 The Multi Scale Retinex Method;268
24.3.3;3.3 The Gradientfaces Normalization Method;268
24.3.4;3.5 The Single Scale Weberfaces Normalization Method;268
24.3.5;3.6 The Multi Scale Weberfaces Normalization Method;268
24.4;4 Face Recognition by Using Principal Component Analysis;268
24.5;5 Experimental Results;272
24.6;6 Conclusions;280
24.7;References;280
25;Developing the Creativity and Design Skillsof Mechatronic Engineering Studentswith Labs and Robot Competitions;282
25.1;1 Introduction;282
25.2;2 Problem Based Learning (PBL) Approach;284
25.3;3 Formative Laboratory Work;286
25.4;4 Assessments;287
25.5;5 Mobile Robot Racing Competition;288
25.6;6 Box Collecting Competition;290
25.7;7 Robot Wars or Robot Sumo Competition;293
25.8;8 Student Evaluations;297
25.9;9 Observations;297
25.10;10 Conclusions;300
25.11;References;301
26;An Open Architecture Control Systemfor Reconfigurable Numerically Controlled Machinery;303
26.1;1 Introduction;303
26.2;2 OAC Requirements and Recent Developments;304
26.3;3 OAC Development for the MRMT;306
26.3.1;3.1 System Overview;306
26.3.2;3.2 Distributed Axis and Spindle Modules;307
26.3.3;3.3 Buffer Layers;308
26.3.4;3.4 Multiple Microcontroller Implementation;309
26.4;4 Software Reference Architecture and Algorithms;309
26.5;5 Graphical User Interface;311
26.6;6 Testing and Results;312
26.7;7 Challenges;314
26.8;8 Discussion;314
26.9;9 Conclusion;315
26.10;References;315
27;Desktop Scanner Metrology;317
27.1;1 Introduction;317
27.2;1.1 Previous Applications of Flatbed Scanners;317
27.3;2 Initial Experiment and Methodology;318
27.3.1;2.1 Introduction;318
27.3.2;2.2 Image Processing Software;319
27.3.3;2.3 Variables Considered;319
27.3.4;2.4 Initial Experiment Results;322
27.3.5;2.5 Initial Experiment Comments;322
27.4;3 Second Stage Experiment;323
27.4.1;3.1 Lighting of the Component [L];323
27.4.2;3.2 Other Factors;325
27.4.3;3.3 Experiment Plan and Results;325
27.5;4 Results and Discussion;326
27.5.1;4.1 Initial Experiment;326
27.5.2;4.2 Second Stage Experiment;327
27.6;5 Conclusions;328
27.7;References;328
28;A Soft Starting Applicationwith Fuzzy Type II for Hub Motors;330
28.1;1 Introduction;330
28.2;2 Closed Loop Soft Starting Method;331
28.3;3 Fuzzy Type II;331
28.4;4 Experimental Design;335
28.5;5 Experimental Results;337
28.5.1;5.1 No Load Experiments;337
28.5.2;5.2 50% Load Experiments;338
28.5.3;5.3 100% Load Experiments;339
28.6;6 Conclusion;340
28.7;References;340
29;Author Index;342