# 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 / 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 / 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 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 / 60

Mixed 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 / 60

Mixed 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 / 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 ... 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 / 60

Mixed 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 / 60

Mixed 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 / 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 “information collection 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 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 / 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 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 / 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 probability of 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 ∈ 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 / 60

Theoretical 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 / 60

Theoretical 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 / 60

Theoretical 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 / 60

Theoretical 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 / 60

Inverting 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 / 60

Inverting 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 / 60

Inverting 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 / 60

Inverting 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 / 60

Conclusion 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 / 60

Conclusion 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 / 60

Conclusion 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 / 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 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 / 60

Introduction 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 / 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 problems Clinical 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 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 / 60

Technical 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 / 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 = 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 / 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 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 / 60

Simulation 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 / 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 task scheduling 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 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 / 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 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 / 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 Thermals Observability Estimation (m) Leaning Chimney (n) Bubble Thermal Thermal ARC Lab Team (UM) Other Research September 2012 51 / 60

Perpetual 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 / 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 Shear Estimation (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 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 / 60

Perpetual Flight in Flow Field Wind Shear Soaring Models Estimation - 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 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 / 60

Perpetual 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 / 60

Perpetual 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 / 60

Perpetual Flight in Flow Field Thank You! ARCLAB (UM) Collaborative Unmanned Air Vehicles 60 / 60

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