Visual Guidance for Fixed-Wing Unmanned Aerial Vehicles using Feature Tracking

240 Pages·2014·9.69 MB·English

Visual Guidance for
Fixed-Wing Unmanned Aerial
Vehicles using Feature
Tracking
Application to Power Line Inspection
by
Steven John Mills
B. Eng (AeroAv) (Hons)
A thesis submitted to the Queensland University of Technology
for the degree of Doctor of Philosophy
Faculty of Science and Engineering
September 2013
ii
Keywords
Locally Linear Infrastructure Automated Inspection Processes
· · ·
Fixed Wing Unmanned Aerial Vehicles
· ·
Skid-to-Turn Manoeuvres Forward-Slip Manoeuvres
· · ·
Feature Tracking Vision Based Control
· · ·
Position Based Visual Servoing Image Based Visual Servoing
· · ·
C 2013 Steven John Mills
Typeset in LATEX2ε.
iii
Theworkcontainedinthisthesishasnotbeenpreviously
submitted to meet requirements for an award at this or
any other higher education institution. To the best of
my knowledge and belief, the thesis contains no material
previously published or written by another person except
where due reference is made.
Steven John Mills
iv
Acknowledgements
I would first like to acknowledge the contribution and support of my supervisors,
Dr Luis Mejias, Dr Jason Ford, Prof. Wageeh Boles and Prof. Rodney Walker;
your guidance and support over the course of my candidature was much appre-
ciated. I would especially like to extend my sincere gratitude to Dr Luis Mejias
for his time and patience in seeing me through to the end.
A special thanks to Dr Nabil Aouf whom I am indebted for his support and
guidance during my time in the UK and to my fellow colleagues Diego Rodr´ıguez,
Dom Li and Redouane Kaibou, for their valuable input and warm hospitality
during my stay at Cranfield.
I would also like to extend my gratitude to my fellow colleagues at ARCAA,
many of whom have contributed to this work with helpful ideas, moral support
and constructive criticism. A special mention to John Lai, Paul Westall and
Richard Glassock; I enjoyed sharing this journey with you guys.
I am grateful to the Queensland University of Technology for offering me
both the opportunity to undertake this degree and scholarships in the form of an
Australian Postgraduate Award, QUT Vice Chancellor’s Initiative Scholarship
and Faculty Top-Up. I would also like to acknowledge the Cooperative Research
Centre for Spatial Information who supported this research; a collaboration that
lead to an exciting and fulfilling line of research.
Another thanks to Dr Luis Mejias for securing me a place in the International
Research Staff Exchange Scheme offered by the Australian Academy of Science
that lead to my research visit at Cranfield; this experience was invaluable to me.
Last, but not least, I would like to thank my family for their support and
encouragement throughout. This would not have been possible without you.
v
vi Acknowledgements
Abstract
As the use of Unmanned Aerial Vehicles (UAVs) grows within the civilian sector,
one application that is likely to attract the attention of industry is the inspec-
tion of infrastructure, in particular, those situated in rural and remote regions.
Automating the process of data collection would appear to be a task well suited
to the UAV and one that can draw upon years of research in areas of machine
vision, guidance and control, and automated data processing. Fixed-wing UAVs
can be expected to play a crucial role in this, particularly for tasks covering large
areas, due to the platforms inherent efficiency and generous payload capabilities
that directly contribute to long range.
Successful completion of these tasks introduces the challenge of performing
guidance and control in a manner that establishes favourable conditions for data
collection. While various tracking solutions exist, a common approach is to guide
the vehicle directly over the feature that inevitably sees data collection controlled
indirectly as a by-product of aircraft position. In particular, these solutions
overlook sensor line-of-sight that is directly affected by aircraft attitude that
varies as a result of rotation induced by manoeuvres used to maintain track. In
the context of downward facing sensors that are likely to be fitted to fixed-wing
UAVs, the impact is most evident through Bank-to-Turn manoeuvres that form
the predominant means of altering heading.
Current solutions addressing these issues are limited and generally seek to
addresstheproblemthroughpathplanningandfollowingthatassumesknowledge
of infrastructure location. Obtaining this information at a level of accuracy that
can take advantage of these techniques however is not always possible. In this
work, solutions are presented in the form of vision based control, offering real-
time control capable of actively tracking infrastructure. Guidance and control is
developed on the principal of providing ideal conditions for data collection from
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viii Abstract
body-fixed sensors, removing the need for gimballed mounts and thus alleviating
payload requirements that are crucial on small UAV systems. Utilising Image
Based Visual Servo (IBVS) techniques, data collection is controlled directly as
viewed from an inspection sensor; a technique that is then extended to provide
coverage as the UAV transitions between segments of locally linear infrastructure.
In the first of two developments, Skid-to-Turn (STT) manoeuvres are utilised
through an IBVS control design to view the feature at a Desired Line Angle, cal-
culated as a function of Sensor Track Error, that allows recentring of the feature
in one smooth motion. The second development augments the interaction matrix
ofalinefeaturewiththeaircraftequationsofmotion. Thisallowsthedesignofan
optimal state feedback controller that enables tracking to be performed through
Forward-Slip(FS)manoeuvres. Thesemanoeuvresareshowntoimprovetracking
performance at reduced control effort compared to STT, while control through
state feedback provides a direct means to suppress unwanted motion that could
otherwise degrade data collection.
Another contribution is made to the direct management of data collection
through an analysis of visual tracking in the presence of wind. To track a de-
sired course in the presence of wind requires heading to be altered by a Wind
Correction Angle. This presents an issue for visual control formed on a desired
view of features that does not account for wind. The issue is investigated through
the inclusion of a wind model in the interaction matrix, linking relative motion
of image features with aircraft motion and wind. The effect of a steady wind
disturbance is found to introduce a constant term in the interaction matrix and
shown to be offset with desired line angle set to the Wind Correction Angle.
A final contribution extends these developments to negotiating transitions
between locally linear segments of infrastructure. Transitions present discrete
changes in the direction of infrastructure that require a UAV performing inspec-
tiontoaltercoursewhilstensuringcontinueddatacollection. BoththeSTTIBVS
and FS IBVS developments are extended to this task, the first using a smoothing
feature to manage the transition, while the latter switches between features at
a predetermined distance in the image frame. These provide separate solutions
with variations in overshoot, time to recentre and maximum transition angle.
Each of these developments is tested extensively through simulation, in an en-
vironmentdevelopedtogenerateimageryaswouldbecapturedduringinspection,
while allowing realistic test conditions including turbulence and wind gusts.
Contents
Acknowledgements v
Abstract vii
List of Figures xiii
List of Tables xvii
List of Abbreviations xix
1 Introduction 1
1.1 Research Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2 Contributions of Research . . . . . . . . . . . . . . . . . . . . . . 6
1.2.1 Publications . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.2.2 Significance of Research . . . . . . . . . . . . . . . . . . . 9
1.3 Scope and Key Assumptions . . . . . . . . . . . . . . . . . . . . . 10
1.4 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2 Literature Review 13
2.1 Path Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.1.1 Path Following . . . . . . . . . . . . . . . . . . . . . . . . 19
2.1.2 Field of View Control . . . . . . . . . . . . . . . . . . . . . 23
2.1.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.2 Vision Based Control . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.2.1 Position-Based Control . . . . . . . . . . . . . . . . . . . . 31
2.2.2 Image-Based Control . . . . . . . . . . . . . . . . . . . . . 32
2.2.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
ix
x Contents
2.3 Visual Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3 Skid-to-Turn IBVS 41
3.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.1.1 Bank-to-Turn Manoeuvres . . . . . . . . . . . . . . . . . . 42
3.1.2 Skid-to-Turn Manoeuvres . . . . . . . . . . . . . . . . . . 44
3.1.3 Visual Control Design . . . . . . . . . . . . . . . . . . . . 46
3.1.4 Parameter Tuning . . . . . . . . . . . . . . . . . . . . . . . 53
3.2 Simulation Environment . . . . . . . . . . . . . . . . . . . . . . . 54
3.2.1 BTT PBVS Controller . . . . . . . . . . . . . . . . . . . . 55
3.3 Test Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
4 Forward-Slip IBVS 69
4.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.1.1 Feature Representation . . . . . . . . . . . . . . . . . . . . 70
4.1.2 Interaction Matrix . . . . . . . . . . . . . . . . . . . . . . 73
4.1.3 Aircraft Model . . . . . . . . . . . . . . . . . . . . . . . . 77
4.1.4 Interaction Matrix Linearisation . . . . . . . . . . . . . . . 80
4.1.5 Full State Model . . . . . . . . . . . . . . . . . . . . . . . 85
4.2 Control Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
4.2.1 State Estimation . . . . . . . . . . . . . . . . . . . . . . . 93
4.3 Test Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
5 Mean Wind Compensation 107
5.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . 108
5.2 Atmospheric Disturbances . . . . . . . . . . . . . . . . . . . . . . 116
5.2.1 Turbulence Model . . . . . . . . . . . . . . . . . . . . . . . 117
5.2.2 Discrete Gust Model . . . . . . . . . . . . . . . . . . . . . 117
5.3 Test Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
5.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127