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MAX: Collaborative Unmanned Air VehiclesRecent Progress at UM
Anouck Girard & Pierre KabambaBaro Hyun, Justin Jackson, Jonathan Las Fargeas, Jinwoo Seok
Department of Aerospace EngineeringUniversity of MichiganAnn Arbor, Michigan
September 2012
ARCLAB (UM) Collaborative Unmanned Air Vehicles September 2012 1 / 60
Introduction
ARCLAB in Numbers
Current People:
3 PhD
2 MS
Publications:
10 peer reviewedjournal articlesaccepted
41 conferencepapers accepted
2 book chapterspublished
Graduated Students:
4 PhD:Justin Jackson, 2012, Llamasoft.Baro Hyun, 2011, Hyundai Motors.Christopher Orlowski, 2011, US Army, TACOM/TARDEC.Andrew Klesh, 2009, JPL.
6 MS:Zahid Hasan, 2012, Raytheon Company.Calvin Park, 2012, North American Bancard.Clarence Hanson, 2011, Rockwell Collins.Jonathan White, 2008, US Coast Guard.John Baker, 2007, Systems Engineering, HDT Robotics.
Amir Matlock, 2007, JHU Applied Physics Lab, Ballistic
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 AerospaceCenter) - 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 unmannedaircraft under motion constraints.
Task assignment for unmanned aircraftconsidering 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 performingpersistent visitation.
Complete algorithm to find minimal cost paths when fuel constraintsare considered.
Formulation of the Persistent Visitation, Detection, and Captureproblem for multiple UAVs.
Algorithm to generate paths for UAVs that perform persistentvisitation while attempting to image intruders.
Potential ImpactsARCLAB (UM) Collaborative Unmanned Air Vehicles September 2012 6 / 60
Overview
Mixed-Initiative Nested Classificationfor n Team Members
Baro Hyun, Songya Pan, Pierre Kabamba, Anouck Girard
Department of Aerospace EngineeringUniversity 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 acommunication device
Human operators
direct the UAVsinspect data and makeclassification decisions
Need high quality classificationdecisions in the presence ofuncertainties
B. Hyun et al. (UM) Inverting the ratio September 2012 8 / 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 acommunication device
Human operators
direct the UAVsinspect data and makeclassification decisions
Need high quality classificationdecisions in the presence ofuncertainties
B. Hyun et al. (UM) Inverting the ratio September 2012 8 / 60
Mixed 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 / 60
Mixed Initiative Nested Classification
Objectives of the researchGlobal Objective
Improving the classification performance in mixed-initiative systems
Year 2-3 (2008-2010)
A mobile classifier taking multiple measurements while seekingmaximum information
Discrete Event System (DES) modeling of human operator inclassification task
A mobile classifier making sequential decisions while seekingminimum risk
Year 4-5 (2010-2012)
Information-classification performance, classification mechanism bythresholding, team classification
Inverting the human-to-machine ratio
B. Hyun et al. (UM) Inverting the ratio September 2012 10 / 60
Mixed Initiative Nested Classification
Motivational questions
How do we leverage the complementary strengths of human/machinecollaboration in a mixed-initiative system?
How can we invert the current human-to-machine ratio in the ISRmission?
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 andhumans, respectively
B. Hyun et al. (UM) Inverting the ratio September 2012 11 / 60
Mixed Initiative Nested Classification
Motivational questions
How do we leverage the complementary strengths of human/machinecollaboration in a mixed-initiative system?
How can we invert the current human-to-machine ratio in the ISRmission?
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 andhumans, respectively
B. Hyun et al. (UM) Inverting the ratio September 2012 11 / 60
Mixed 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 ... hasbecome overwhelming, as manpower requirements to deal with thesedata have placed enormous burden on the Air Force.”“... systems that can reliably make wide-ranging autonomous decisionsat cyber speeds to allow reactions in time-critical roles far exceedingwhat humans can possibly achieve.”
Grand Challenges for Air Force S&TChallenge #2: Trusted highly-autonomous decision-making systems“... demonstrate technologies that enable current human-intensivefunctions to be replaced, in whole or in part, by more highlyautonomous decision-making systems, ...”
B. Hyun et al. (UM) Inverting the ratio September 2012 12 / 60
Mixed Initiative Nested Classification
Literature survey
ClassificationTheory 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, Holsappleet al. ’08]
Statistical decision makingHypothesis testing [Lehmann and Romano ’10]Bayesian decision theory [Berger ’85]Sequential Probability Ratio Test (SPRT) [Wald ’45]
Human-machine collaborationInverting 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 / 60
Mixed Initiative Nested Classification
Technical contributions
We extended our work on mixed-initiative nested thresholding, aclassification architecture that uses a primary workload-independentclassifier and a secondary workload-dependent classifier, for a generalnumber n of classifiers in the architecture, formally pose the problem,and solve it.
We identified the optimal ratio of mixed-initiative team members, thecorresponding minimal probability of misclassification, and theindividual workload applied to the workload-dependent classifier as afunction of the total workload applied to the architecture.
We performed a sensitivity analysis of the aforementioned results withrespect to the peak performance of the workload-dependent classifier.
B. Hyun et al. (UM) Inverting the ratio September 2012 14 / 60
Mixed 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 “informationcollection and decision making” for ACC’12
B. Hyun et al. (UM) Inverting the ratio September 2012 15 / 60
Theoretical background
What is a classifier?
A decider D is a deterministic mapping defined on a set of data intotruth values
D : {data} → {T, F}A classifier C is a decider with the domain of the mapping being aspecific realization of a random variable
The difference between a decider and a classifier is that the latteraccounts for the randomness of the data being classified
Important parameters
Processing of the data requires two abilities1 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 / 60
Theoretical 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 / 60
Theoretical background
Theoretical background - Maximum likelihood classification
Bayes rule
Provides posterior probability of the object category on the basis theobject property
P (X = T |Y = {Y1, Y2}) =P (Y = {Y1, Y2}|X = T )P (X = T )
P (Y = {Y1, Y2})(2)
Let Os ∈ {T, F} be the decision variable
Likelihood-ratio rule
Makes classification decisions by comparing the posterior probability
Os =
{T if P (X=T |Y={Y1,Y2})
P (X=F |Y={Y1,Y2}) > λ
F if P (X=T |Y={Y1,Y2})P (X=F |Y={Y1,Y2}) ≤ λ.
(3)
where λ ∈ R.
B. Hyun et al. (UM) Inverting the ratio September 2012 18 / 60
Theoretical 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 probabilityof false positives and the probability of false negatives
B. Hyun et al. (UM) Inverting the ratio September 2012 19 / 60
Theoretical background
Thresholding problem
Assumptions
A continuous measurable property w ∈ RObject categories are known a priori
Distribution of w for each object category is known a priori
w F T €
σF
€
σT
€
FN
€
FP
Figure: Dichotomous thresholding
w F T €
σF
€
σT
€
FN
€
FP
Unknown
Figure: Trichotomous thresholding
B. Hyun et al. (UM) Inverting the ratio September 2012 20 / 60
Theoretical background
Mixed-initiative nested thresholding
Start
End
Prior
Workload-IndependentTrichotomous Classifier
Workload-DependentDichotomous Classifier
Good?
u
T, F Pm
T, F Pm
WundecidablesYes
No
B. Hyun et al. (UM) Inverting the ratio September 2012 21 / 60
Theoretical background
Mixed-initiative nested thresholding
Start
End
Prior
Workload-IndependentTrichotomous Classifier
Workload-DependentDichotomous Classifier
Good?
u
T, F Pm
T, F Pm
WundecidablesYes
No
w F T !
"F
!
"T
!
FN
!
FP
!"!" #"!
"F
!
"T
!
FN
!
FP
$%&%'(%"
Workload
Workload
B. Hyun et al. (UM) Inverting the ratio September 2012 22 / 60
Theoretical background
Problem formulation
Workload
We define a workload variable, W ∈ [0, 1], with 0 indicating idle and 1indicating fully loaded. Let fi(w) = aie
−(w+bi)2/c2i with i ∈ {T, F}, then
the workload variable is defined as
W =
∫ τ2
τ1
ufT (w) + (1− u)fF (w)dw. (5)
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 trichotomousclassifier determines the workload applied to the secondary classifier.
Optimization problem
minτ1,τ2
P 2m,
subject to some inequality constraints.
B. Hyun et al. (UM) Inverting the ratio September 2012 23 / 60
Theoretical background
Comparison of performance
10−1
100
101
102
103
10−8
10−7
10−6
10−5
10−4
10−3
10−2
10−1
100
Cl
Pm*
Minimal probability of misclassification vs. classifiability
Pm2 with τ
0 = [m
T, m
F]
Pm1
(a) The minimal probability of misclassi-fication vs. classifiability. The blue solidline indicates the mixed-initiative nestedthresholding while the red dashed line in-dicates the dichotomous thresholding.
10−1
100
101
102
103
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Cl
W
Workload vs. Classifiability
W with τ0 = [m
T, m
F]
(b) Workload vs. classifiability
Figure: The minimal probability of misclassification and the workload for themixed-initiative nested thresholding as a function of the classifiability
B. Hyun et al. (UM) Inverting the ratio September 2012 24 / 60
Inverting the ratio
Inverting the ratio
H1 · · ·H2 H3 H10
M1 M1 M10M2
H1
· · ·
Figure: Mixed-initiative nested thresholding with more than two team members.(M denotes a workload-independent classifier and H denotes aworkload-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-dependentclassifiers (H)?
B. Hyun et al. (UM) Inverting the ratio September 2012 25 / 60
Inverting the ratio
Setup
Ratio variable n ∈ { 1m ,
1m−1 , · · · , 1
2 , 1, 2, · · · ,m}the ratio of the number of workload-dependent classifiers to thenumber of workload-independent classifiers in the system with m ∈ N.n = 0.1 means a single workload-dependent classifier (human) and 10workload-independent classifiers (machines).
Total workload Wt ∈ [0, ∞)
the workload applied to the whole secondary layer in the architecture
Individual workload Wn ∈ [0, 1], Wn = Wtn
the workload applied to the individual classifier in the secondary layerassume uniform distribution of Wt to the secondary layer
Problem formulation
The objective of the problem is to minimize the probability ofmisclassification by choosing the ratio number n, i.e.,
minnP 2m(Wt, n).
B. Hyun et al. (UM) Inverting the ratio September 2012 26 / 60
Inverting the ratio
Analytical results
Theorem
Suppose that Wt is fixed and let n∗ = arg minn P2m(Wt, n). The optimal
ratio n∗ is monotonically increasing with respect to Wt, specifically thatn∗ = 2Wt.
†Proof by the necessary condition for optimality
Corollary
limWt→∞
W ∗n = 0.5
B. Hyun et al. (UM) Inverting the ratio September 2012 27 / 60
Inverting the ratio
10−1
100
0.20.25
0.33
0.5
1
2
Total workload (W)
Opt
imal
rat
io (
n* )
(a) Optimal ratio (abscissain logarithmic scale)
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.01
0.02
0.03
0.04
0.05
0.06
Total workload (W)
Min
imal
pro
babi
lity
of m
iscl
assi
ficat
ion
(Pm2
*(n* ))
(b) Minimal probability ofmisclassification
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
Total workload (W)
Indi
vidu
al w
orkl
oad
(Wn)
(c) Individual workload
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50
1
2
3
4
5
6
7
8
9
10
X: 0.1Y: 0.2 Total workload (W)
Opt
imal
rat
io (
n* )
X: 0.2Y: 0.3333
X: 0.3Y: 0.5
(d) Optimal ratio
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
Total workload (W)
Min
imal
pro
babi
lity
of m
iscl
assi
ficat
ion
(Pm2
*(n* ))
(e) Minimal probability ofmisclassification
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
0.35
0.4
0.45
0.5
0.55
0.6
0.65
Total workload (W)
Indi
vidu
al w
orkl
oad
(Wn)
(f) Individual workload
B. Hyun et al. (UM) Inverting the ratio September 2012 28 / 60
Conclusion
Conclusion
Implications
Guidelines to design a mixed-initiative system that autonomouslydetermines the optimal human-to-machine ratio
Relevant publications - available in MACCCS Ctools website1 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, AmericanControl Conference, Montreal, Canada, 2012. (invited paper)
3 B. Hyun, M. Faied, P. Kabamba, A. Girard, Optimal Classification by Mixed-Initiative Nested Thresholding, IEEETransactions 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 SignalProcessing Letters, 2012, Submitted.
B. Hyun et al. (UM) Inverting the ratio September 2012 29 / 60
Conclusion
Optimal strategies for team classification
Problem
Given: a number of decision makers, their individual performancesand prior information.
Find: the best fusion rules under different decision structures withrespect to a performance metric.
A1 B1
A2
B2
A3
B3
(g) Incremental pairing
A1 B1
A3
A2 B2
B3
(h) Tournament-like pairing
B. Hyun et al. (UM) Inverting the ratio September 2012 30 / 60
Conclusion
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.05
0.1
0.15
0.2
0.25
0.3
0.35
u
Min
imal
Pm
The misclassification of four−team classifier with incremental pairing
Fused Result for A2Fused Result for A3Final Fused Result
(i) Incremental pairing
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.05
0.1
0.15
0.2
0.25
0.3
0.35
u
Min
imal
Pm
The misclassifaction of Four−team classifier with Touramnet−like Pairing
Fused Result for B3Fused Result for A3Final Fused Result
(j) Tournament-like pairing
We propose a decision structure that exploits a moderator, i.e., anentity that exploits Bayesian inference from individual classifiers’decisions and makes final decisions based on maximum likelihoodclassification.
Two pairing schemes, i.e., incremental and tournament-like, areproposed and we show that the incremental pairing is the mosteffective 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 / 60
Conclusion
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 / 60
Conclusion
Automated Classification Systemfor 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 / 60
Introduction
Motivation
Hand X-ray Image
Importance of Bone Age(BA)
The assessment of growth and pubertalmaturation is central to the practice ofpediatric endocrinology and BA is keyreferenceGreulich and Pyle (GP) atlas is a keyclinical indicator in pediatric endocrinologyTo determine BA, radiologist compares thepatient’s x-ray to those contained in thereference atlas and determines which imagein the atlas the patient’s x-ray is closest to
J. Seok et al. (UM) Automated Classification System September 2012 34 / 60
Introduction
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 problemsClinical Age (CA) and BA relationship is unclear from the publications
J. Seok et al. (UM) Automated Classification System September 2012 35 / 60
Introduction
Original Contributions
Image morphing
Greulich and Pyle (1959)
Radiographic data
More radiographic data
−100 −80 −60 −40 −20 0 20 40 60 80 1000
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
v
f(v)
u = 0.5
ufT(w), mT = −10, sT = 10(1−u)fNT(w), mNT = 10, sNT = 15ufT(w)+(1−u)fNT(w)
Optimal Threshold
Thresholding classifier
Predicted bone age!
Bone age?
Feature extrac=on
Training
Schematic overview of the automated classification system
i. Create a modified atlas that hasimages regularly spaced atthree month intervals in theclinically significant ranges
ii. Propose a novel Singular ValueDecomposition (SVD)-basedfeature extractor to create afeature vector out of thedescriptors obtained from SIFT
iii. Develop image classifier basedon SIFT - SVD
J. Seok et al. (UM) Automated Classification System September 2012 36 / 60
Technical Section
Image Feature Extraction
0 100 200 300 400 500 600
100
200
300
400
500
600
Feature descriptors using VL-SIFT
Scale Invariant Feature Transform (SIFT)
Introduced by David G. Lowe in 1999
Local-based feature extraction method
Invariant to scaling and rotation, andpartially invariant to viewpoint andillumination changes
Algorithm
Detection of scale-space extremaAccurate keypoint localizationOrientation assignmentThe local image descriptor
J. Seok et al. (UM) Automated Classification System September 2012 37 / 60
Technical 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 = UΣV 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, weproduce a novel feature vector for the classifier.
J. Seok et al. (UM) Automated Classification System September 2012 38 / 60
Simulation
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 MatlabApply the SIFT algorithm to get key points and local image descriptorsApply SVD to get reduced feature vectorsTrain the neural networkValidateIn progress: gathering larger data set for statistical analysisFuture work: compare Hyun approach to current (neural network)
J. Seok et al. (UM) Automated Classification System September 2012 39 / 60
Simulation
Results
Test result 1, marked with circles
Classifier works well as most theanswers are closely aligned to thediagonal line.
Only one result shows radicalmisclassification.
Three results showing moderateerrors, and some round-off errors.
Test result 2, marked with crosses
Classifier performs less well: Therewas only one training data per class;this is generally not consideredsufficient to train classifiers. (Proof ofconcept).
0 5 10 15 20 250
5
10
15
20
25
Input (GP standard number)
Ou
tpu
t (G
P s
tan
da
rd n
um
be
r)
Correct Answer
Test result1
Test result2
SIFT - SVD classifier results
J. Seok et al. (UM) Automated Classification System September 2012 40 / 60
Simulation
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 / 60
Relevant 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 / 60
Relevant 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 taskscheduling
ARC Lab Team (UM) Other Research September 2012 43 / 60
Relevant 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 ofanalytical 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 / 60
Relevant 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 andscheduling
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 / 60
Relevant 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 ThermalsObservability
Estimation
(m) Leaning ChimneyThermal
(n) Bubble Thermal
ARC Lab Team (UM) Other Research September 2012 51 / 60
Perpetual Flight in Flow Field
Thermal Soaring
ModelsObservabilityEstimation
(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 thermalcenter.The aircraft should be flying around the thermal or turning
ARC Lab Team (UM) Other Research September 2012 52 / 60
Perpetual 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 / 60
Perpetual 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 / 60
Perpetual Flight in Flow Field
Wind Shear Soaring
Models - Surface, Layer & Ridge Wind ShearEstimation
(r) Surface Wind Shear
ARC Lab Team (UM) Other Research September 2012 55 / 60
Perpetual Flight in Flow Field
Wind Shear Soaring
Models - Surface, Layer & Ridge Wind ShearEstimation
(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
ModelsEstimation - Particle Filter
(u) Estimator initialization
ARC Lab Team (UM) Other Research September 2012 57 / 60
Perpetual Flight in Flow Field
Wind Shear Soaring
ModelsEstimation - Particle Filter
(v) Estimator final convergence
ARC Lab Team (UM) Other Research September 2012 58 / 60
Perpetual Flight in Flow Field
Formation Flight
Validation of airflow modelsCollection of spatially distributed samplesSafe flight at close distances
(w) Formation in a thermalARC Lab Team (UM) Other Research September 2012 59 / 60
Perpetual Flight in Flow Field
Formation Flight
Validation of airflow modelsCollection of spatially distributed samplesSafe flight at close distances
(x) Formation in a thermalARC Lab Team (UM) Other Research September 2012 59 / 60
Perpetual Flight in Flow Field
Formation Flight
Validation of airflow modelsCollection of spatially distributed samplesSafe flight at close distances
(y) Formation in a thermalARC Lab Team (UM) Other Research September 2012 59 / 60
Perpetual Flight in Flow Field
Thank You!
ARCLAB (UM) Collaborative Unmanned Air Vehicles 60 / 60