MAX: Collaborative Unmanned Air Vehicles Recent Progress at UM
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MAX: Collaborative Unmanned Air Vehicles
Recent Progress at UM
Anouck Girard & Pierre Kabamba
Baro Hyun, Justin Jackson, Jonathan Las Fargeas, Jinwoo Seok
Department of Aerospace Engineering
University of Michigan
Ann Arbor, Michigan
September 2012
ARCLAB (UM) Collaborative Unmanned Air Vehicles September 2012 1 / 60
Introduction
ARCLAB in Numbers
Graduated Students:
Current People:
4 PhD:
3 PhD Justin Jackson, 2012, Llamasoft.
2 MS Baro Hyun, 2011, Hyundai Motors.
Christopher Orlowski, 2011, US Army, TACOM/TARDEC.
Andrew Klesh, 2009, JPL.
Publications:
10 peer reviewed 6 MS:
journal articles Zahid Hasan, 2012, Raytheon Company.
accepted Calvin Park, 2012, North American Bancard.
Clarence Hanson, 2011, Rockwell Collins.
41 conference
Jonathan White, 2008, US Coast Guard.
papers accepted
John Baker, 2007, Systems Engineering, HDT Robotics.
2 book chapters Amir Matlock, 2007, JHU Applied Physics Lab, Ballistic
published
Missile Defense Test and Evaluation Group.
ARCLAB (UM) Collaborative Unmanned Air Vehicles September 2012 2 / 60
New Members of the ARCLab
Moritz Niendorf
Work Experience and Education
02/2011 - 07/2012: DLR (German Aerospace
Center) - Department for Unmanned Aircraft
11/2010: Diploma in Aerospace Engineering -
University of Stuttgart, Germany
09/2008 - 05/2009: Exchange Student -
Aeronautical and Astronautical Engineering -
Purdue University
Research Interests
Mission and path planning for unmanned
aircraft under motion constraints.
Task assignment for unmanned aircraft
considering path planning aspects.
ARCLAB (UM) Collaborative Unmanned Air Vehicles September 2012 3 / 60
New Members of the ARCLab
Dave Oyler
Work Experience and Education
Texas A&M University
05/2012: B.S. Electrical Engineering
NASA Johnson Space Center
05/2012-08/2012: Robotic Operations
01/2011-08/2011: Robotic Systems Technology
01/2010-05/2010: Integrated Communications
06/2008-08/2008: Electromagnetic Systems
Research Interests
Robotic planetary exploration
Cooperation of heterogeneous robotic teams
ARCLAB (UM) Collaborative Unmanned Air Vehicles September 2012 4 / 60
Overview
Mixed-Initiative Nested Classification
Themes
From ... Sensor, To ... Information
Trusted highly-autonomous decision-making systems
Objectives
Improve the classification performance in mixed-initiative system
ARCLAB (UM) Collaborative Unmanned Air Vehicles September 2012 5 / 60
Overview
Persistent Visitation, Detection, and Capture
Themes
Coherent change detection for persistent surveillance systems
Increased operational efficiency and autonomy
Objectives/Results
Formulation of the Persistent Visitation problem for a single UAV.
Proof of the existence of periodic paths for single UAVs performing
persistent visitation.
Complete algorithm to find minimal cost paths when fuel constraints
are considered.
Formulation of the Persistent Visitation, Detection, and Capture
problem for multiple UAVs.
Algorithm to generate paths for UAVs that perform persistent
visitation while attempting to image intruders.
Potential Impacts
ARCLAB (UM) Collaborative Unmanned Air Vehicles September 2012 6 / 60
Overview
Mixed-Initiative Nested Classification
for n Team Members
Baro Hyun, Songya Pan, Pierre Kabamba, Anouck Girard
Department of Aerospace Engineering
University of Michigan, Ann Arbor, MI
Annual MACCCS Review
September 2012
B. Hyun et al. (UM) Inverting the ratio September 2012 7 / 60
Mixed Initiative Nested Classification
Motivated by military operations
Intelligence, Surveillance, and Reconnaissance missions
Objects of interests
threat or friend
Unmanned aerial vehicles
(UAVs)
carry a suite of sensors and a
communication device
Human operators
direct the UAVs
inspect data and make
classification decisions
Need high quality classification
decisions in the presence of
uncertainties
B. Hyun et al. (UM) Inverting the ratio September 2012 8 / 60Mixed Initiative Nested Classification
Motivated by military operations
Intelligence, Surveillance, and Reconnaissance missions
Objects of interests
threat or friend
Unmanned aerial vehicles
(UAVs)
carry a suite of sensors and a
communication device
Human operators
direct the UAVs
inspect data and make
classification decisions
Need high quality classification
decisions in the presence of
uncertainties
B. Hyun et al. (UM) Inverting the ratio September 2012 8 / 60Mixed Initiative Nested Classification
Information overflow
Multiple views from a wide angle camera (Gorgon Stare)
Figure: from Cummings and Bertuccelli, MAX review 10’
“... man power requirements to deal with these data are burdensome”
[AF/ST, Report on Tech Horizon ’10]
B. Hyun et al. (UM) Inverting the ratio September 2012 9 / 60Mixed Initiative Nested Classification
Objectives of the research
Global Objective
Improving the classification performance in mixed-initiative systems
Year 2-3 (2008-2010)
A mobile classifier taking multiple measurements while seeking
maximum information
Discrete Event System (DES) modeling of human operator in
classification task
A mobile classifier making sequential decisions while seeking
minimum risk
Year 4-5 (2010-2012)
Information-classification performance, classification mechanism by
thresholding, team classification
Inverting the human-to-machine ratio
B. Hyun et al. (UM) Inverting the ratio September 2012 10 / 60Mixed Initiative Nested Classification
Motivational questions
How do we leverage the complementary strengths of human/machine
collaboration in a mixed-initiative system?
How can we invert the current human-to-machine ratio in the ISR
mission?
Mixed-initiative system
1 Classifiers with workload-independent performance (machines)
2 Classifiers with workload-dependent performance (humans)
- “First-order” models to capture the features of machines and
humans, respectively
B. Hyun et al. (UM) Inverting the ratio September 2012 11 / 60Mixed Initiative Nested Classification
Motivational questions
How do we leverage the complementary strengths of human/machine
collaboration in a mixed-initiative system?
How can we invert the current human-to-machine ratio in the ISR
mission?
Mixed-initiative system
1 Classifiers with workload-independent performance (machines)
2 Classifiers with workload-dependent performance (humans)
- “First-order” models to capture the features of machines and
humans, respectively
B. Hyun et al. (UM) Inverting the ratio September 2012 11 / 60Mixed Initiative Nested Classification
Technical relevance to the A.F.
Air Force relevance (Highlights from [Tech. Horizon])
From ... Sensor, To ... Information
“The volume of sensor data from current-generation sensors ... has
become overwhelming, as manpower requirements to deal with these
data have placed enormous burden on the Air Force.”
“... systems that can reliably make wide-ranging autonomous decisions
at cyber speeds to allow reactions in time-critical roles far exceeding
what humans can possibly achieve.”
Grand Challenges for Air Force S&T
Challenge #2: Trusted highly-autonomous decision-making systems
“... demonstrate technologies that enable current human-intensive
functions to be replaced, in whole or in part, by more highly
autonomous decision-making systems, ...”
B. Hyun et al. (UM) Inverting the ratio September 2012 12 / 60Mixed Initiative Nested Classification
Literature survey
Classification
Theory of classification [Gupta and Leu ’89, Widrow ’63]
Applications of classification [Jain et al. ’00, Chang et al. ’06]
Classification with human inputs [Cebron and Berthold ’06, Holsapple
et al. ’08]
Statistical decision making
Hypothesis testing [Lehmann and Romano ’10]
Bayesian decision theory [Berger ’85]
Sequential Probability Ratio Test (SPRT) [Wald ’45]
Human-machine collaboration
Inverting the ratio [Cummings et al. ’08-’10]
Adjustable autonomy [Goodrich et al. ’09-’10]
B. Hyun et al. (UM) Inverting the ratio September 2012 13 / 60Mixed Initiative Nested Classification
Technical contributions
We extended our work on mixed-initiative nested thresholding, a
classification architecture that uses a primary workload-independent
classifier and a secondary workload-dependent classifier, for a general
number n of classifiers in the architecture, formally pose the problem,
and solve it.
We identified the optimal ratio of mixed-initiative team members, the
corresponding minimal probability of misclassification, and the
individual workload applied to the workload-dependent classifier as a
function of the total workload applied to the architecture.
We performed a sensitivity analysis of the aforementioned results with
respect to the peak performance of the workload-dependent classifier.
B. Hyun et al. (UM) Inverting the ratio September 2012 14 / 60Mixed Initiative Nested Classification
Recent achievements by numbers (year 5)
1 accepted and 3 submitted journal papers
7 accepted or submitted conference papers
Co-organizer and session chair for an invited session on “information
collection and decision making” for ACC’12
B. Hyun et al. (UM) Inverting the ratio September 2012 15 / 60Theoretical background
What is a classifier?
A decider D is a deterministic mapping defined on a set of data into
truth values
D : {data} → {T, F }
A classifier C is a decider with the domain of the mapping being a
specific realization of a random variable
The difference between a decider and a classifier is that the latter
accounts for the randomness of the data being classified
Important parameters
Processing of the data requires two abilities
1 recognizing truth out of truth (rate of true positives)
2 recognizing falsehood out of falsehood (rate of true negatives)
Characterized by two independent parameters σT and σF
B. Hyun et al. (UM) Inverting the ratio September 2012 16 / 60Theoretical background
Theoretical background - Probabilistic modeling
Let X ∈ {T, F } be the object category variable
Let Y ∈ {Y1 , Y2 } be the object property variable
The likelihood is modeled by the following conditional probabilities,
P (Y = Y2 |X = T ) = σT ,
P (Y = Y1 |X = F ) = σF ,
P (Y = Y1 |X = T ) = 1 − σT ,
P (Y = Y2 |X = F ) = 1 − σF , (1)
where σi ∈ [0.5, 1], i ∈ {T, F }.
u: proportion of sub-population T among the entire population
B. Hyun et al. (UM) Inverting the ratio September 2012 17 / 60Theoretical background
Theoretical background - Maximum likelihood classification
Bayes rule
Provides posterior probability of the object category on the basis the
object property
P (Y = {Y1 , Y2 }|X = T )P (X = T )
P (X = T |Y = {Y1 , Y2 }) = (2)
P (Y = {Y1 , Y2 })
Let Os ∈ {T, F } be the decision variable
Likelihood-ratio rule
Makes classification decisions by comparing the posterior probability
(X=T |Y ={Y1 ,Y2 })
T if PP (X=F
(
|Y ={Y1 ,Y2 }) > λ
Os = P (X=T |Y ={Y1 ,Y2 }) (3)
F if P (X=F |Y ={Y1 ,Y2 }) ≤ λ.
where λ ∈ R.
B. Hyun et al. (UM) Inverting the ratio September 2012 18 / 60Theoretical background
Theoretical background - Classification performance
Probability of misclassification
The performance measure is the probability of misclassification, Pm ,
Pm = P (Os = T ∧ X = F ) + P (Os = F ∧ X = T ) (4)
Pm is the sum of probabilities of two faulty outcomes: the probability
of false positives and the probability of false negatives
B. Hyun et al. (UM) Inverting the ratio September 2012 19 / 60Theoretical background
Thresholding problem
Assumptions
A continuous measurable property w ∈ R
Object categories are known a priori
Distribution of w for each object category is known a priori
σF σT σF σT
FN FP FN FP
w w
€ F € T € F
€
Unknown
T
€ €
€ €
Figure: Dichotomous thresholding Figure: Trichotomous thresholding
B. Hyun et al. (UM) Inverting the ratio September 2012 20 / 60Theoretical background
Mixed-initiative nested thresholding
Start Prior
u
Workload-Independent
Trichotomous Classifier
T, F Pm
Yes
undecidables W Good?
No
Workload-Dependent
Dichotomous Classifier
T, F Pm
End
B. Hyun et al. (UM) Inverting the ratio September 2012 21 / 60Theoretical background
Mixed-initiative nested thresholding
Start Prior
"F "T u
FN FP
!"
! !" ! #"
! $%&%'(%"
! Workload-Independent
Trichotomous Classifier Workload
T, F Pm
Yes
undecidables W Good?
No
Workload-Dependent
Dichotomous Classifier
T, F Pm
"F "T End
FN FP
w
! F ! T
! !
Workload
B. Hyun et al. (UM) Inverting the ratio September 2012 22 / 60Theoretical background
Problem formulation
Workload
We define a workload variable, W ∈ [0, 1], with 0 indicating idle and 1
2 2
indicating fully loaded. Let fi (w) = ai e−(w+bi ) /ci with i ∈ {T, F }, then
the workload variable is defined as
Z τ2
W = ufT (w) + (1 − u)fF (w)dw. (5)
τ1
Note that the range of W is [0, 1] for any τ1 and τ2 .
The region of indecision, i.e., [τ1 , τ2 ], of the primary trichotomous
classifier determines the workload applied to the secondary classifier.
Optimization problem
2
min Pm ,
τ1 ,τ2
subject to some inequality constraints.
B. Hyun et al. (UM) Inverting the ratio September 2012 23 / 60Theoretical background
Comparison of performance
0 Minimal probability of misclassification vs. classifiability Workload vs. Classifiability
10 0.5
−1
10 0.45
−2 0.4
10
0.35
−3
10
0.3
−4
P*m
10
W
0.25
−5
10
0.2
2
−6
10 Pm with τ0 = [mT, mF]
0.15
P1m
W with τ0 = [mT, mF]
−7
10 0.1
−8
10 −1 0 1 2 3
0.05 −1 0 1 2 3
10 10 10 10 10 10 10 10 10 10
Cl Cl
(a) The minimal probability of misclassi- (b) Workload vs. classifiability
fication vs. classifiability. The blue solid
line indicates the mixed-initiative nested
thresholding while the red dashed line in-
dicates the dichotomous thresholding.
Figure: The minimal probability of misclassification and the workload for the
mixed-initiative nested thresholding as a function of the classifiability
B. Hyun et al. (UM) Inverting the ratio September 2012 24 / 60Inverting the ratio
Inverting the ratio
M1 M1 M2 ··· M10
H1 H2 H3 ··· H10 H1
Figure: Mixed-initiative nested thresholding with more than two team members.
(M denotes a workload-independent classifier and H denotes a
workload-dependent classifier)
Point of interest
Given a workload W provided by a workload-independent classifier (M ),
What’s the optimal ratio of the mixed-initiative team members?
What’s the reachable performance?
What’s the individual workload applied to each workload-dependent
classifiers (H)?
B. Hyun et al. (UM) Inverting the ratio September 2012 25 / 60Inverting the ratio
Setup
1 1
Ratio variable n ∈ { m , m−1 , · · · , 12 , 1, 2, · · · , m}
the ratio of the number of workload-dependent classifiers to the
number of workload-independent classifiers in the system with m ∈ N.
n = 0.1 means a single workload-dependent classifier (human) and 10
workload-independent classifiers (machines).
Total workload Wt ∈ [0, ∞)
the workload applied to the whole secondary layer in the architecture
Wt
Individual workload Wn ∈ [0, 1], Wn = n
the workload applied to the individual classifier in the secondary layer
assume uniform distribution of Wt to the secondary layer
Problem formulation
The objective of the problem is to minimize the probability of
misclassification by choosing the ratio number n, i.e.,
2
min Pm (Wt , n).
n
B. Hyun et al. (UM) Inverting the ratio September 2012 26 / 60Inverting the ratio
Analytical results
Theorem
Suppose that Wt is fixed and let n∗ = arg minn Pm 2 (W , n). The optimal
t
∗
ratio n is monotonically increasing with respect to Wt , specifically that
n∗ = 2Wt .
†Proof by the necessary condition for optimality
Corollary
lim Wn∗ = 0.5
Wt →∞
B. Hyun et al. (UM) Inverting the ratio September 2012 27 / 60Inverting the ratio
2 0.06 0.7
0.65
0.05
Minimal probability of misclassification (Pm
2
*(n*))
0.6
0.04
Individual workload (Wn)
0.55
Optimal ratio (n*)
0.03 0.5
1
0.45
0.02
0.4
0.5 0.01
0.35
0.33
0.25
0.2 −1 0
0
10 10 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Total workload (W) Total workload (W) Total workload (W)
(a) Optimal ratio (abscissa (b) Minimal probability of (c) Individual workload
in logarithmic scale) misclassification
10
0.045 0.65
9
*(n*))
0.04
0.6
8
Minimal probability of misclassification (Pm
2
0.035
7 0.55
Individual workload (Wn)
0.03
Optimal ratio (n*)
6
0.5
0.025
5
0.02 0.45
4
0.015
3
0.4
2 0.01
0.35
X: 0.2 1 0.005
Y: 0.3333
0 X: 0.3 0
X: 0.10 0.5
Y: 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
Y: 0.2 Total workload (W) Total workload (W) Total workload (W)
(d) Optimal ratio (e) Minimal probability of (f) Individual workload
misclassification
B. Hyun et al. (UM) Inverting the ratio September 2012 28 / 60Conclusion
Conclusion
Implications
Guidelines to design a mixed-initiative system that autonomously
determines the optimal human-to-machine ratio
Relevant publications - available in MACCCS Ctools website
1 B. Hyun, M. Faied, P. Kabamba, A. Girard, Mixed-Initiative Nested Classification for n Team Members, IEEE
Conference on Decision and Control, Maui, HI, 2012.
2 B. Hyun, M. Faied, P. Kabamba, A. Girard, Optimal Multivariate Classification by Linear Thresholding, American
Control Conference, Montreal, Canada, 2012. (invited paper)
3 B. Hyun, M. Faied, P. Kabamba, A. Girard, Optimal Classification by Mixed-Initiative Nested Thresholding, IEEE
Transactions on Systems, Man, and Cybernetics - Part A, 2012, Submitted.
4 B. Hyun, M. Faied, P. Kabamba, A. Girard, On Minimizing Classification Error by Maximizing Information, IEEE Signal
Processing Letters, 2012, Submitted.
B. Hyun et al. (UM) Inverting the ratio September 2012 29 / 60Conclusion
Optimal strategies for team classification
Problem
Given: a number of decision makers, their individual performances
and prior information.
Find: the best fusion rules under different decision structures with
respect to a performance metric.
A1 B1 B2 B3
A1 B1 A2 B2
A2
A3 B3
A3
(g) Incremental pairing (h) Tournament-like pairing
B. Hyun et al. (UM) Inverting the ratio September 2012 30 / 60Conclusion
The misclassification of four−team classifier with incremental pairing The misclassifaction of Four−team classifier with Touramnet−like Pairing
0.35 0.35
Fused Result for A2 Fused Result for B3
Fused Result for A3 Fused Result for A3
Final Fused Result Final Fused Result
0.3 0.3
0.25 0.25
0.2 0.2
Minimal Pm
Minimal Pm
0.15 0.15
0.1 0.1
0.05 0.05
0 0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
u u
(i) Incremental pairing (j) Tournament-like pairing
We propose a decision structure that exploits a moderator, i.e., an
entity that exploits Bayesian inference from individual classifiers’
decisions and makes final decisions based on maximum likelihood
classification.
Two pairing schemes, i.e., incremental and tournament-like, are
proposed and we show that the incremental pairing is the most
effective decision structure among the proposed ones.
S. Pan, B. Hyun, P. Kabamba, A. Girard, Optimal Fusion Rules in Team Classification under Three Decision Structures,
American Control Conference, Washington, DC, USA, 2013, Submitted.
B. Hyun et al. (UM) Inverting the ratio September 2012 31 / 60Conclusion
Future work
Analysis under different performance measures
- Addressing time-criticality by queueing theory
- Confidence level
Kinematic classification (free measurements)
- Costly kinematic classification (costly measurements)
Classification with learning
Strategies for uncertain prior information
Deceptive strategies
B. Hyun et al. (UM) Inverting the ratio September 2012 32 / 60Conclusion
Automated Classification System
for Bone Age X-ray Images
Jinwoo Seok, Baro Hyun, Josephine Kasa-Vubu*, and Anouck Girard
Department of Aerospace Engineering and Pediatric Endocrinology*
University of Michigan, Ann Arbor, MI
Annual MACCCS Review
September 2012
J. Seok et al. (UM) Automated Classification System September 2012 33 / 60Introduction
Motivation
Importance of Bone Age(BA)
The assessment of growth and pubertal
maturation is central to the practice of
pediatric endocrinology and BA is key
reference
Greulich and Pyle (GP) atlas is a key
clinical indicator in pediatric endocrinology
To determine BA, radiologist compares the
patient’s x-ray to those contained in the
reference atlas and determines which image
in the atlas the patient’s x-ray is closest to
Hand X-ray Image
J. Seok et al. (UM) Automated Classification System September 2012 34 / 60Introduction
Literature Review
There have been attempts at automated BA detection
CASAS [Tanner ’92]
Peitka [Pietka et al. ’01]
BoneXpert [Thodberg et al. ’01 and ’09]
BoneXpert has been developed recently
Active Appearance Model (AAM) [Cootes et al. ’01]
Better performance than previous work [Martin et al. ’09]
(Root mean square deviation 0.72 years)
Problems of BoneXpert
Validating problems
Clinical Age (CA) and BA relationship is unclear from the publications
J. Seok et al. (UM) Automated Classification System September 2012 35 / 60Introduction
Original Contributions
More
radiographic
data
i. Create a modified atlas that has
Image
morphing
Radiographic
data
images regularly spaced at
three month intervals in the
clinically significant ranges
Greulich
and
Pyle
(1959)
Training
ii. Propose a novel Singular Value
Thresholding
classifier
Decomposition (SVD)-based
u = 0.5
Bone
age?
feature extractor to create a
0.04
ufT(w), mT = −10, sT = 10
0.035 (1−u)fNT(w), mNT = 10, sNT = 15
ufT(w)+(1−u)fNT(w)
0.03 Optimal Threshold
Predicted
0.025
bone
age!
f(v)
0.02
0.015
0.01
0.005
0
−100 −80 −60 −40 −20 0 20 40 60 80 100
Feature
extrac=on
feature vector out of the
v
descriptors obtained from SIFT
Schematic overview of the automated classification system iii. Develop image classifier based
on SIFT - SVD
J. Seok et al. (UM) Automated Classification System September 2012 36 / 60Technical Section
Image Feature Extraction
Scale Invariant Feature Transform (SIFT)
Introduced by David G. Lowe in 1999
Local-based feature extraction method
Invariant to scaling and rotation, and
partially invariant to viewpoint and
illumination changes
Algorithm
Detection of scale-space extrema
Accurate keypoint localization
Orientation assignment
100 200 300 400 500 600 The local image descriptor
Feature descriptors using VL-SIFT
J. Seok et al. (UM) Automated Classification System September 2012 37 / 60Technical Section
Image Feature Extraction
Singular Value Decomposition (SVD)
Matrix factorization method
Reduces the size while keeping the characteristics of a matrix
Given an m × m matrix A, the expression of its SVD is
A = U ΣV T (6)
where U is an m × m matrix, V is an n × n matrix and Σ is the singular values of matrix
A which is an m × n non-negative real diagonal matrix.
SIFT - SVD based feature extractor
By applying SVD to the feature descriptors obtained from SIFT, we
produce a novel feature vector for the classifier.
J. Seok et al. (UM) Automated Classification System September 2012 38 / 60Simulation
Simulation
Data set
24 GP female standard images for training: 1 through 27 excluding 13,
21 and 27, 13 and 27 because of poor image conditions.
Generated 19 morphing images for validation.
Classification decision step
Import images to Matlab
Apply the SIFT algorithm to get key points and local image descriptors
Apply SVD to get reduced feature vectors
Train the neural network
Validate
In progress: gathering larger data set for statistical analysis
Future work: compare Hyun approach to current (neural network)
J. Seok et al. (UM) Automated Classification System September 2012 39 / 60Simulation
Results
Test result 1, marked with circles
Classifier works well as most the 25
Correct Answer
answers are closely aligned to the 20
Test result1
Test result2
diagonal line.
Output (GP standard number)
Only one result shows radical 15
misclassification. 10
Three results showing moderate
5
errors, and some round-off errors.
Test result 2, marked with crosses 0
0 5 10 15
Input (GP standard number)
20 25
Classifier performs less well: There SIFT - SVD classifier results
was only one training data per class;
this is generally not considered
sufficient to train classifiers. (Proof of
concept).
J. Seok et al. (UM) Automated Classification System September 2012 40 / 60Simulation
Highlights of Other Relevant Research
Justin Jackson, Eric Sihite, Ricardo Bencatel
Annual MACCCS Review
September 2012
ARC Lab Team (UM) Other Research September 2012 41 / 60Relevant Research
Highlights of Other Relevant Research
Distributed Task Assignment and Scheduling
VRP Heuristics Comparison
Persistent Flight on Flow Fields
ARC Lab Team (UM) Other Research September 2012 42 / 60Relevant Research
Task Assignment and Scheduling: Original Contributions
Contributions in two categories
Centralized minimum-time, precedence-constrained, vehicle routing
Distributed minimum-time, constrained, task assignment and task
scheduling
ARC Lab Team (UM) Other Research September 2012 43 / 60Relevant Research
Centralized Task Assignment and Scheduling
Minimum-time, precedence-constrained vehicle routing
1 Low complexity algorithm for AFRL-relevant vehicle routing problem
2 Analysis of algorithm optimality and complexity
3 Solution quality measurement technique, useful in absence of
analytical bounds
Comparison of tabu/2-opt heuristic and optimal tree search method for assignment problems, International Journal of
Robust and Nonlinear Control, 2011
A New Measure of Solution Quality for Combinatorial Task Assignment Problems, Conference on Decision and Control,
2010
A Combined Tabu Search and 2-opt Heuristic for Multiple Vehicle Routing, Automatic Controls Conference, 2010
ARC Lab Team (UM) Other Research September 2012 44 / 60Relevant Research
Distributed Task Assignment and Scheduling
Minimum-time constrained distributed task assignment and scheduling
1 Communication-constraints satisfy operational needs
2 Scheduling constraints express relevant operational constraints
3 Stochastic Bidding and the OptDNSB Algorithms for assignment and
scheduling
4 Correctness, completeness, optimality, complexity characterization
5 Characterization and utilization of problem separation
Distributed Constrained Minimum-Time Schedules in Networks of Arbitrary Topology, IEEE Transactions on Robotics,
2011 (Submitted)
Communication-Constrained Distributed Assignment on Networks of Arbitrarily Topology, IEEE Transactions on
Robotics, 2011 (Submitted)
Communication-Constrained Distributed Assignment, IEEE Conference on Decision and Control, 2011
Distributed Task Scheduling Subject to Arbitrary Constraints, 18th World Congress of the International Federation of
Automatic Control (IFAC), 2011
ARC Lab Team (UM) Other Research September 2012 45 / 60Relevant Research Heuristics Comparison for VRP ARC Lab Team (UM) Other Research September 2012 46 / 60
Relevant Research Heuristics Comparison for VRP ARC Lab Team (UM) Other Research September 2012 47 / 60
Relevant Research Heuristics Comparison for VRP ARC Lab Team (UM) Other Research September 2012 48 / 60
Relevant Research Heuristics Comparison for VRP E. Sihite, J. Jackson, A. Girard, VRP Heuristics Comparison, ACC 2013 (Submitted) ARC Lab Team (UM) Other Research September 2012 49 / 60
Relevant Research Perpetual Flight in Flow Fields Extension of UAV endurance Inspired by birds behaviors Harvest airflow energy ARC Lab Team (UM) Other Research September 2012 50 / 60
Perpetual Flight in Flow Field
Thermal Soaring
Models - Chimney & Bubble Thermals
Observability
Estimation
(m) Leaning Chimney (n) Bubble Thermal
Thermal
ARC Lab Team (UM) Other Research September 2012 51 / 60Perpetual Flight in Flow Field
Thermal Soaring
Models
Observability
Estimation
(o) Trapezoidal model
Theorem: The thermal position, velocity and updraft flow field planar parameters are
locally weakly observable by an aircraft flying trajectories with ϕ̇ 6= γ̇ tan2 (ϕ − γ), as
long as the trajectory is included in the area defined by r2 ≥ d ≥ r1 . This holds for the
trapezoidal model.
The aircraft cannot fly at a constant distance from the thermal
center.
The aircraft should be flying around the thermal or turning
ARC Lab Team (UM) Other Research September 2012 52 / 60Perpetual Flight in Flow Field
Thermal Soaring
Models
Observability
Estimation - Regularized Adaptive Particle Filter
(p) Estimator initialization
ARC Lab Team (UM) Other Research September 2012 53 / 60Perpetual Flight in Flow Field
Thermal Soaring
Models
Observability
Estimation - Regularized Adaptive Particle Filter
(q) Estimator convergence
ARC Lab Team (UM) Other Research September 2012 54 / 60Perpetual Flight in Flow Field
Wind Shear Soaring
Models - Surface, Layer & Ridge Wind Shear
Estimation
(r) Surface Wind Shear
ARC Lab Team (UM) Other Research September 2012 55 / 60Perpetual Flight in Flow Field Wind Shear Soaring Models - Surface, Layer & Ridge Wind Shear Estimation (s) Layer Wind Shear (t) Ridge Wind Shear ARC Lab Team (UM) Other Research September 2012 56 / 60
Perpetual Flight in Flow Field
Wind Shear Soaring
Models
Estimation - Particle Filter
(u) Estimator initialization
ARC Lab Team (UM) Other Research September 2012 57 / 60Perpetual Flight in Flow Field
Wind Shear Soaring
Models
Estimation - Particle Filter
(v) Estimator final convergence
ARC Lab Team (UM) Other Research September 2012 58 / 60Perpetual Flight in Flow Field
Formation Flight
Validation of airflow models
Collection of spatially distributed samples
Safe flight at close distances
(w) Formation in a thermal
ARC Lab Team (UM) Other Research September 2012 59 / 60Perpetual Flight in Flow Field
Formation Flight
Validation of airflow models
Collection of spatially distributed samples
Safe flight at close distances
(x) Formation in a thermal
ARC Lab Team (UM) Other Research September 2012 59 / 60Perpetual Flight in Flow Field
Formation Flight
Validation of airflow models
Collection of spatially distributed samples
Safe flight at close distances
(y) Formation in a thermal
ARC Lab Team (UM) Other Research September 2012 59 / 60Perpetual Flight in Flow Field
Thank You!
ARCLAB (UM) Collaborative Unmanned Air Vehicles 60 / 60You can also read
