Informatics in Control, Automation and Robotics II (eBook)

Joaquim Filipe, Jean-Louis Ferrier, Juan A. Cetto, Marina Carvalho (Herausgeber)

eBook Download: PDF

2007 | 2007
XV, 243 Seiten
Springer Netherland (Verlag)
978-1-4020-5626-0 (ISBN)

Infos

This book is a collection of the best papers presented at the 2nd International Conference on Informatics in Control, Automation and Robotics (ICINCO). ICINCO brought together researchers, engineers and practitioners interested in the application of informatics to Control, Automation and Robotics. The research papers focused on real world applications, covering three main themes: Intelligent Control Systems, Optimization, Robotics and Automation and Signal Processing, Systems Modeling and Control.

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Informatics in Control, Automation and Robotics II is a collection of the best papers presented at the 2nd International Conference on Informatics in Control, Automation and Robotics (ICINCO). The purpose of ICINCO was to bring together researchers, engineers and practitioners interested in the application of informatics to Control, Automation and Robotics. The research papers focused on real world applications, covering three main themes: Intelligent Control Systems, Optimization, Robotics and Automation and Signal Processing, Systems Modeling and Control.Informatics applications are pervasive in many areas of Control, Automation and Robotics. This book will be of interest to professionals working on the control and robotics area, especially those who need to maintain knowledge about current trends in development methods and applications.

Inhalt

TABLE OF CONTENTS 5
PREFACE 9
CONFERENCE COMMITTEE 11
INVITED SPEAKERS 15
Invited Speakers 17
COMBINING HUMAN & MACHINE BRAINS
1 INTRODUCTION 18
2 AUGMENTATION 20
3 EXPERIMENTATION 21
4 CONCLUSIONS 22
ACKNOWLEDGEMENTS 23
REFERENCES 23
BRIEF BIOGRAPHY 23
REDUNDANCY: THE MEASUREMENT CROSSING CUTTING-EDGE TECHNOLOGIES 26
1 WIDE ASSORTMENT OF MATHEMATICAL EXPRESSIONS 26
2 PRELIMINARIES 27
3 REDUNDANT IS ABUNDANT 28
4 REDUNDANCY IN DIGITAL TECHNIQUES 29
5 CONCLUSIONS 30
REFERENCES 31
BRIEF BIOGRAPHY 31
HYBRID DYNAMIC SYSTEMS 32
1 INTRODUCTION 32
2 VERIFICATION OF HYBRID SYSTEMS 33
3 HYBRID REACHABILITY 35
4 DISCRETE EVENT ABSTRACTION 37
5 CONTINUOUS EXPANSION COMPUTATION 38
6 CONCLUSIONS 39
REFERENCES 40
BRIEF BIOGRAPHY 41
TARGET LOCALIZATION USING MACHINE LEARNING 42
1 INTRODUCTION TO SENSOR NETWORKS 42
2 LOCALIZATION IN SENSOR NETWORKS 43
REFERENCES 47
BRIEF BIOGRAPHY 48
PART 1 Intelligent Control Systems and Optimization 50
MODEL PREDICTIVE CONTROL FOR DISTRIBUTED PARAMETER SYSTEMS USING RBF NEURAL NETWORKS 52
1 INTRODUCTION 52
2 RBF NEURAL NETWORKS FOR MODELING DISTRIBUTED PARAMETER SYSTEMS 53
3 NONLINEAR MPC FOR DPS 54
4 APPLICATION 54
5 CONCLUSIONS 56
REFERENCES 57
FUZZY DIAGNOSIS MODULE BASED ON INTERVAL FUZZY LOGIC: OIL ANALYSIS APPLICATION 58
1 INTRODUCTION 58
2 THE FUZZY CONDITION MONITORING MODULE 59
3 OIL ANALYSIS APPLICATION 62
4 CONCLUSIONS 64
ACKNOWLEDGEMENTS 64
REFERENCES 64
DERIVING BEHAVIOR FROM GOAL STRUCTURE FOR THE INTELLIGENT CONTROL OF PHYSICAL SYSTEMS 66
1 INTRODUCTION 66
2 FOUNDATIONS FOR THE MODELLING OF CONTROL SYSTEMS RELATED TO ENGINEERING PROCESSES 67
3 THE TARGET APPLICATION 69
4 THE CONCEPTUAL GOAL HIERARCHY 69
5 BEHAVIOR REPRESENTATION 70
6 RELATED WORK 72
7 CONCLUSIONS 72
REFERENCES 72
EVOLUTIONARY COMPUTATION FOR DISCRETE AND CONTINUOUS TIME OPTIMAL CONTROL PROBLEMS 74
1 INTRODUCTION 74
2 OPTIMAL CONTROL OF DISCRETE TIME NONLINEAR SYSTEMS 75
3 VELOCITY DIRECTION CONTROL OF A BODY IN A VISCOUS FLUID 77
4 EVOLUTIONARY APPROACH TO OPTIMAL CONTROL 79
5 NONLINEAR CONTINUOUS TIME OPTIMAL CONTROL 80
6 GODDARD’S OPTIMAL CONTROL PROBLEM IN ROCKET DYNAMICS 81
7 CONCLUSIONS 82
REFERENCES 83
CONTRIBUTORS TO A SIGNAL FROM AN ARTIFICIAL CONTRAST 86
1 INTRODUCTION 86
2 CONTROL REGION DESIGN 87
3 CONTRIBUTORS TO A SIGNAL 88
4 ILLUSTRATIVE EXAMPLE 89
5 MANUFACTURING EXAMPLE 91
6 CONCLUSIONS 92
REFERENCES 93
REAL-TIME TIME-OPTIMAL CONTROL FOR A NONLINEAR CONTAINER CRANE USING A NEURAL NETWORK 94
1 INTRODUCTION 94
2 CRANE MODEL 94
3 TIME-OPTIMAL CONTROL 95
4 NEURAL NETWORK 97
5 DISCUSSION 98
REFERENCES 98
PART 2 Robotics and Automation 101
IMAGE-BASED AND INTRINSIC-FREE VISUAL NAVIGATION OF A MOBILE ROBOT DEFINED AS A GLOBAL VISUAL SERVOING TASK 102
1 INTRODUCTION 102
2 AUTONOMOUS NAVIGATION USING VISUAL SERVOING TECHNIQUES 102
3 DISCONTINUITIES IN VISUAL NAVIGATION 103
4 CONTINUOUS CONTROL LAW FOR NAVIGATION 104
5 EXPERIMENTS IN A VIRTUAL INDOOR ENVIRONMENT 106
6 CONCLUSIONS 107
ACKNOWLEDGEMENTS 107
REFERENCES 108
SYNTHESIZING DETERMINISTIC CONTROLLERS IN SUPERVISORY CONTROL 110
1 INTRODUCTION 110
2 SUPERVISORY CONTROL THEORY 111
3 CONTROLLER SYNTHESIS 113
4 CONTROLLER SYNTHESIS ALGORITHM 114
5 CONCLUSION 117
REFERENCES 117
AN UNCALIBRATED APPROACH TO TRACK TRAJECTORIES USING VISUAL–FORCE CONTROL 118
1 INTRODUCTION 118
2 NOTATION 119
3 VISUAL TRACKING OF TRAJECTORIES 119
4 FUSION VISUAL-FORCE CONTROL 119
5 MANAGING CONTRADICTORY CONTROL ACTIONS 120
6 AUTOCALIBRATION 121
7 RESULTS 122
8 CONCLUSIONS 123
REFERENCES 123
A STRATEGY FOR BUILDING TOPOLOGICAL MAPS THROUGH SCENE OBSERVATION 124
1 INTRODUCTION 124
2 OVERALL LEARNING SYSTEM 125
3 EXPERIMENTAL RESULTS 128
4 ENLARGING THE MAP 129
5 CONCLUSIONS AND FUTURE WORK 129
REFERENCES 130
A SWITCHING ALGORITHM FOR TRACKING EXTENDED TARGETS 132
1 INTRODUCTION AND RELATED WORK 132
2 THE MATHEMATICAL BACKGROUD OF THE ALGORITHMS 133
3 EVALUATION OF THE ALGORITHMS 135
4 THE PROBLEM OF CROSSING TARGETS 138
5 A NEW SWITCHING ALGORITHM 140
6 THE SWITCHING ALGORITHM COMPARED TO THE SJPDAF 141
7 CONCLUSIONS 142
REFERENCES 143
SFM FOR PLANAR SCENES: A DIRECT AND ROBUST APPROACH 144
1 INTRODUCTION 144
2 BACKGROUND 144
3 A TWO-STEP APPROACH 145
4 A ONE-STEP APPROACH 147
5 THE DERIVATIVES 147
6 EXPERIMENTS 148
7 CONCLUSION 150
REFERENCES 150
COMBINING TWO METHODS TO ACCURATELY ESTIMATE DENSE DISPARITY MAPS 152
1 INTRODUCTION 152
2 GRAPH-CUTS METHOD 152
3 ENERGY BASED METHOD 153
4 COMBINING GRAPH-CUTS AND STEREOFLOWMETHOD 153
5 EXPERIMENTAL RESULTS 154
6 CONCLUSIONS 157
ACKNOWLEDGEMENTS 158
REFERENCES 158
PRECISE DEAD-RECKONING FOR MOBILE ROBOTS USING MULTIPLE OPTICAL MOUSE SENSORS 160
1 INTRODUCTION 160
2 OPTICAL MOUSE SENSOR 161
3 DEAD-RECKONING BASED ON OPTICAL MOUSE SENSORS 161
4 EXPERIMENTS 162
5 CONCLUSION 166
REFERENCES 166
IMAGE BINARISATION USING THE EXTENDED KALMAN FILTER 168
1 INTRODUCTION 168
2 BINARISATION TECHNIQUES 168
3 LINE TRACKING 170
4 TESTING AND RESULTS 172
5 CONCLUSION 177
ACKNOWLEDGEMENTS 177
REFERENCES 177
LOWER LIMB PROSTHESIS: FINAL PROTOTYPE RELEASE AND CONTROL SETTING METHODOLOGIES 178
1 INTRODUCTION 178
2 DESIGN METHOD OF THE M-LEG SYSTEM 179
3 ACTUAL RELEASE OF ARTIFICIAL LIMB PROSTHESIS. FROM DESIGN METHOD TO COMPONENT DESIGN 180
4 CONTROL STATEMENTS METHODOLOGY 182
5 CONCLUSIONS 187
ACKNOWLEDGEMENTS 187
REFERENCES 187
DIRECT GRADIENT-BASED REINFORCEMENT LEARNING FOR ROBOT BEHAVIOR LEARNING 190
1 INTRODUCTION 190
2 THE RLDPS ALGORITHM 191
3 CASE TO STUDY: TARGET FOLLOWING 193
4 SIMULATED RESULTS 194
ACKNOWLEDGEMENTS 196
REFERENCES 196
PART 3 Signal Processing, Systems Modeling and Control 199
PERFORMANCE ANALYSIS OF TIMED EVENT GRAPHS WITH MULTIPLIERS USING (Min, +) ALGEBRA 200
1 INTRODUCTION 200
2 RECURRENT EQUATIONS OF TEGM’s 201
3 LINEARIZATION OF TEGM’S 202
4 PERFORMANCE EVALUATION 203
5 CONCLUSION 204
REFERENCES 204
MODELING OF MOTOR NEURONAL STRUCTURES VIA TRANSCRANIAL MAGNETIC STIMULATION 206
1 INTRODUCTION 206
2 DATA ANALYSIS 207
3 NEURONAL MODELS 209
4 MODEL VALIDATION 210
5 CONCLUSIONS 211
ACKNOWLEDGEMENTS 212
REFERENCES 212
ANALYSIS AND SYNTHESIS OF DIGITAL STRUCTURE BY MATRIX METHOD 214
1 INTRODUCTION 214
2 ANALYSIS OF THE SECOND ORDER STATE-SPACE DIGITAL FILTER 215
3 DESIGN OF THE THIRD ORDER STATE-SPACE STRUCTURE 216
4 EXAMPLES 217
5 CONCLUSION 221
REFERENCES 221
ANN-BASED MULTIPLE DIMENSION PREDICTOR FOR SHIP ROUTE PREDICTION 222
1 INTRODUCTION 222
2 PRINCIPLE OF ANN-BASED PREDICTOR 223
3 DRNN PREDICTIVE MODELS 224
4 PDRNN BASED MULTIPLE DIMENSION PREDICTOR 225
5 THE LEARNING ALGORITHM 227
6 SIMULATIONS AND APPLICATION 228
7 CONCLUSIONS 229
REFERENCES 229
A PARAMETERIZED POLYHEDRA APPROACH FOR THE EXPLICIT ROBUST MODEL PREDICTIVE CONTROL 232
1 INTRODUCTION 232
2 ROBUST MPC FORMULATION 233
3 ROBUST MPC AS A MULTI-PARAMETRIC OPTIMIZATION 234
4 THE EXPLICIT SOLUTION 235
5 EXAMPLE 238
6 CONCLUSION 240
REFERENCES 240
A NEW HIERARCHICAL CONTROL SCHEME FOR A CLASS OF CYCLICALLY REPEATED DISCRETE-EVENT SYSTEMS 242
1 INTRODUCTION 242
2 SUPERVISORY LEVEL 243
3 C/D BLOCK 246
4 IMPLEMENTATION LEVEL 246
5 RAIL TRAFFIC CASE STUDY 247
6 CONCLUSION 248
REFERENCES 248
WAVELET TRANSFORM MOMENTS FOR FEATURE EXTRACTION FROM TEMPORAL SIGNALS 250
1 INTRODUCTION 250
2 PREHENSILE EMGS 251
3 EXPERIMENTAL SETUP 251
4 DISCRETE WAVELET TRANSFORM 251
5 WAVELET PACKET TRANSFORM 252
6 DWT AND WPT MOMENTS1 252
7 THE SVM CLASSIFIER 253
8 COMPUTATIONAL COMPLEXITY 253
9 EXPERIMENTAL EVALUATION 254
10 THE RESULTS 255
11 CONCLUSIONS 256
REFERENCES 256
AUTHOR INDEX 258

Textauszug

COMBINING HUMAN & MACHINE BRAINS (p. 3)

Practical Systems in Information & Control

Kevin Warwick
Department of Cybernetics, University of Reading, Reading, RG6 6AY, United Kingdom
Keywords:
Artificial intelligence, Biological systems, Implant technology, Feedback control.

Abstract:
In this paper a look is taken at how the use of implant technology can be used to either increase the range of the abilities of a human and/or diminish the effects of a neural illness, such as Parkinson’s Disease. The key element is the need for a clear interface linking the human brain directly with a computer.

The area of interest here is the use of implant technology, particularly where a connection is made between technology and the human brain and/or nervous system. Pilot tests and experimentation are invariably carried out apriori to investigate the eventual possibilities before human subjects are themselves involved. Some of the more pertinent animal studies are discussed here.

The paper goes on to describe human experimentation, in particular that carried out by the author himself, which led to him receiving a neural implant which linked his nervous system bi-directionally with the internet. With this in place neural signals were transmitted to various technological devices to directly control them. In particular, feedback to the brain was obtained from the fingertips of a robot hand and ultrasonic (extra) sensory input. A view is taken as to the prospects for the future, both in the near term as a therapeutic device and in the long term as a form of enhancement.

1 INTRODUCTION

Research is presently being carried out in which biological signals of some form are measured, are acted upon by some appropriate signal processing technique and are then employed either to control a device or as an input to some feedback mechanism (e.g. Penny et al., 2000).

In most cases the signals are measured externally to the body, thereby imposing errors into the situation due to problems in understanding intentions and removing noise – partly due to the compound nature of the signals being measured. Many problems also arise when attempting to translate electrical energy from the computer to the electronic signals necessary for stimulation within the human body.

For example, when only external stimulation is employed then it is extremely difficult, if not impossible, to select unique sensory receptor channels, due to the general nature of the stimulation.

Wearable computer and virtual reality techniques provide one route for creating a human-machine link. In the last few years items such as shoes and glasses have been augmented with microprocessors, but perhaps of most interest is research in which a miniature computer screen was fitted onto an otherwise standard pair of glasses in order to give the wearer a remote visual experience in which additional information about an external scene could be relayed (Mann, 1997).

In general though, despite being positioned adjacent to the human body, and even though indications such as stress and alertness can be witnessed, to an extent at least, wearable computers and virtual reality systems require significant signal conversion to take place in order to interface human sensory receptors with technology.

Produkt-Details

• Erscheint lt. Verlag: 2.6.2007
• Zusatzinfo: XV, 243 p.
• Verlagsort: Dordrecht
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Theorie / Studium
• Themenwelt: Naturwissenschaften
• Themenwelt: Technik ► Elektrotechnik / Energietechnik
• Schlagworte: algorithms • Automation • Fuzzy Logic • Information • Intelligent Control Systems and Optimization • machine learning • Mobile Robot • Model Predictive Control • Navigation • Peak • Performance • robot • Robotics • Robotics and Automation • Sensor • Signal Processing, Systems Modeling and Control • Tracking • Trend
• ISBN-10: 1-4020-5626-5 / 1402056265
• ISBN-13: 978-1-4020-5626-0 / 9781402056260