Search and Pursuit with Unmanned Aerial Vehicles in Road Networks

228 Pages·2013·19.81 MB·English

Search and Pursuit
with Unmanned Aerial Vehicles
in Road Networks
Michael Dille
CMU-RI-TR-13-30
Submitted in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy in Robotics.
The Robotics Institute
Carnegie Mellon University
Pittsburgh, Pennsylvania 15213
November 2013
Thesis Committee:
Sanjiv Singh, Chair
Benjamin Grocholsky
Maxim Likhachev
Paul Scerri
Thanasis Kehagias, Aristotle University, Greece
Copyright (cid:13)c 2013 Michael Dille

Abstract
Across many rescue, surveillance, and scientific applications, there
exists a broad need to perform wide-area reconnaissance and terrain sur-
veys, for which unmanned aerial vehicles (UAVs) are increasingly popu-
lar. This thesis considers the task of using one or more UAVs to locate
an object of interest, provide continuous viewing, and rapidly re-acquire
tracking should it be lost for any reason.
For both the common class of small field-launched UAVs considered
as well as larger UAVs, this is a difficult task due to a small available
sensor field of view, uncertain estimates of UAV pose, and limited ma-
neuverability relative to the scale of the environment, requiring constant
processing of observations and recomputation of flight paths or sensor
aiming to best find the object or keep it in view. Existing strategies for
accomplishing this provide poor estimates of the object’s location and
rely on grossly heuristic or computationally intensive trajectory genera-
tion for both pursuit and search.
This thesis proposes careful representation of observation uncertainty
and exploitation of environmental structure—with particular focus on
road networks typical of urban-like areas—as means to simplify and bet-
ter model the problem. For the case of actively tracked objects, greatly
improved location estimates are demonstrated through filter representa-
tions designed for high-uncertainty observations, as is increased pursuit
performance by modeling terrain-constrained space reduction in object
location and motion. Objects having no or only roughly known prior lo-
cation require an initial search, for which both classical Bayesian proba-
bilistic search (for stochastically-modeled moving objects) and novel road
network coverage strategies (for stationary or slow-moving objects) are
considered. Finally, this is extended to search and local recapture of
evasive adversaries in road networks through novel mappings of pursuit-
evasion tactics that are well-studied in abstract or ground-based domains
but have yet to see use in physical, particularly aerial, applications.
Estimation and tracking aspects have been validated in extensive field
trials using widely-fielded air vehicles, and other components have been
evaluated in realistic simulation using similarly parameterized vehicle
models and control interfaces, laying the groundwork to directly apply
the demonstrated algorithms on real aircraft.
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Acknowledgements
First and foremost, I owe a great debt of thanks to family and friends for their
encouragement and patience throughout this long endeavor.
The support and guidance of the thesis committee in the preparation and improve-
ment of this document and the underlying work, especially the always-enthusiastic
efforts by Thanasis and many thoughtful discussions with Ben, are deeply appreci-
ated.
MuchoftheworkpresentedinChapter4isjointworkaspartofseveralprojectsinthe
FieldRoboticsCenterattheCarnegieMellonUniversityRoboticsInstitute. Portions
of the described visual tracking and static geolocation effort were led by Stephen
Nuske, and nonlinear estimation strategies were led by Ben Grocholsky. Additional
thanks are owed to Ben and Steve for entertaining and stimulating discourse on all
manner of topics.
Portions of this work received the support of US Army RDECOM-ARDEC (in col-
laboration with iRobot Corporation), the United Technologies Research Center, and
a DoD National Defense Science and Engineering Graduate (NDSEG) fellowship.
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Contents
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 General Problem Statement . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Summary of Contributions . . . . . . . . . . . . . . . . . . . . . . . . 9
1.4 Document Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2 Problem Study 13
2.1 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Problem Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3 Related Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.4 Effect of Environment on Problem Approach . . . . . . . . . . . . . . 20
2.5 Motivation for Road-network Constraints . . . . . . . . . . . . . . . . 23
2.6 Generalization to Mixed Environments . . . . . . . . . . . . . . . . . 24
2.7 Observer Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3 Related Strategies for Search and Tracking 33
3.1 Common Abstractions for Search and Pursuit . . . . . . . . . . . . . 34
3.2 Related Aspects not Considered . . . . . . . . . . . . . . . . . . . . . 37
3.3 Tracking and Pursuit with UAVs . . . . . . . . . . . . . . . . . . . . 41
3.3.1 General approaches to geolocation . . . . . . . . . . . . . . . . 41
3.3.2 Common geolocation filtering strategies . . . . . . . . . . . . . 43
3.3.3 Control for pursuit . . . . . . . . . . . . . . . . . . . . . . . . 46
3.4 Search with UAVs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
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3.4.1 Search in regular areas with uniform prior . . . . . . . . . . . 52
3.4.2 Search in irregular areas or with non-uniform prior . . . . . . 53
3.5 Recapture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4 Geolocation and Pursuit 57
4.1 Experimental Motivation and Methodology . . . . . . . . . . . . . . . 57
4.1.1 Unified control infrastructure for fielded vehicles . . . . . . . . 59
4.1.2 Geolocation observations from visual tracking . . . . . . . . . 60
4.1.3 Initial experimental conclusions . . . . . . . . . . . . . . . . . 62
4.2 Geolocation Under High Uncertainty . . . . . . . . . . . . . . . . . . 64
4.2.1 Evidence-grid filter . . . . . . . . . . . . . . . . . . . . . . . . 66
4.2.2 Range-bearing particle filter . . . . . . . . . . . . . . . . . . . 71
4.2.3 Over-parameterized bounded filter . . . . . . . . . . . . . . . 74
4.3 Pursuit of Constrained Targets . . . . . . . . . . . . . . . . . . . . . 79
4.3.1 Single-road pursuit . . . . . . . . . . . . . . . . . . . . . . . . 79
4.3.2 Cellular road-network decomposition . . . . . . . . . . . . . . 85
4.3.3 Extension to continuous road networks . . . . . . . . . . . . . 88
4.4 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . 90
5 Coverage and Efficient Search 93
5.1 Limited-Maneuverability Open Area Coverage . . . . . . . . . . . . . 94
5.2 Probabilistic Search in Road Networks . . . . . . . . . . . . . . . . . 97
5.2.1 Classical formulation . . . . . . . . . . . . . . . . . . . . . . . 97
5.2.2 Field-of-view approximation . . . . . . . . . . . . . . . . . . . 99
5.3 Efficient Road Network Coverage . . . . . . . . . . . . . . . . . . . . 102
5.3.1 Relevant abstractions . . . . . . . . . . . . . . . . . . . . . . . 102
5.3.2 Visibility model . . . . . . . . . . . . . . . . . . . . . . . . . . 104
5.3.3 Basic tessellated node coverage strategy . . . . . . . . . . . . 105
5.3.4 Coverage strategy extensions . . . . . . . . . . . . . . . . . . . 109
5.3.5 Experimental results . . . . . . . . . . . . . . . . . . . . . . . 114
5.4 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . 125
6 Guaranteed Search and Recapture 129
6.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
6.2 Infinite-speed Targets in Bounded Environments . . . . . . . . . . . . 132
6.2.1 Basic strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
6.2.2 Extensions and demonstration . . . . . . . . . . . . . . . . . . 136
6.2.3 Experimental results . . . . . . . . . . . . . . . . . . . . . . . 138
6.3 Bounded-speed Targets . . . . . . . . . . . . . . . . . . . . . . . . . . 161
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6.3.1 Strategy A: live search . . . . . . . . . . . . . . . . . . . . . . 161
6.3.2 Strategy B: bound and search . . . . . . . . . . . . . . . . . . 164
6.3.3 Experimental results . . . . . . . . . . . . . . . . . . . . . . . 166
6.4 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . 185
7 Conclusion 189
7.1 Possible Extensions and Future Work . . . . . . . . . . . . . . . . . . 191
Appendices 195
A Robust Automatic Visual Tracking from Small UAVs 197
Bibliography 206
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1
CHAPTER
Introduction
1.1 Motivation
Wide-area reconnaissance and survey missions are a key element of many search and
rescue, disaster response, military reconnaissance, law enforcement surveillance, and
scientific exploration scenarios. Examples include searching for missing persons in
the wilderness or at sea, detecting and tracking the spread of wildfires, locating and
pursuing fleeing suspects, protecting a perimeter for a moving convoy, finding mete-
orites, mapping floating ocean debris, and identifying and tracking the movement of
wildlife.
The character of such missions has evolved with social and computing trends,
greatly affecting approach methodology. Typically, these are extremely data-driven
and information rich, since a terrain map (e.g. from satellite imagery) and prior
information on the relative priority of area regions are likely to be available, and
mission results are frequently aggregated—often in real-time—for visualization or
pattern detection. These missions are also extremely dynamic in that additional
information—from witness reports, external sensors, or other responding agencies—
may arrive at any time, and a well-executed mission plan must be able to react and
adapt as necessary. Time-sensitivity is another probable characteristic, both because
lives may be at stake and due to large operational costs, implying a need for rapidly
field-deployed sensing, some form of optimal mission planning to minimize execution
time, and automatic information passing. Finally, considerable risk-aversion is likely
1