Recent Developments in Cooperative Control and Optimization

1 A Hybrid Projected Gradient-Evolutionary Search Algorithm for Capacitated Multi-Source Multi-UAVs Scheduling with Time Windows.- 1.1 Introduction.- 1.2 Mathematical Programming Formulation For Capacitated Multi-UAV Routing With Time Windows.- 1.3 Hybrid Projected Gradient-Evolutionary Search Algorithm.- 1.4 Simulation Results.- 1.5 Conclusion.- 2 Progress in Cooperative Volume Holographic Imaging.- 2.1 Introduction.- 2.2 Formation of Volume Holographic Images.- 2.3 Volume Holographic Imaging with Planar Reference holograms.- 2.4 Cooperative Processing of Volume Holographic Images Using the Pseudo-Inverse Method.- 2.5 Conclusions and Future Work.- 3 Properties of No-Depot Min-?ax 2-Traveling-Salesmen Problem.- 3.1 Introduction.- 3.2 Characteristic Function for No-Depot Min-Max 2-TSP.- 3.3 Threshold Characteristic Function.- 3.4 Constant Graphs.- 3.5 Interpretation of Threshold Self-Dual Monotonic Boolean Functions.- 3.6 Conclusion.- 4 A New Heuristic for the Minimum Connected Dominating Set Problem on Ad Hoc Wireless Networks.- 4.1 Introduction.- 4.2 Algorithm for the MCDS Problem.- 4.3 A Distributed Implementation.- 4.4 Numerical Experiments.- 4.5 Concluding Remarks.- 5 A Platform for Cooperative and Coordinated Control of Multiple Vehicles: The Caltech Multi-Vehicle Wireless Testbed.- 5.1 Introduction.- 5.2 Vehicle Hardware.- 5.3 Lab Positioning System.- 5.4 Onboard Sensing.- 5.5 Electronics.- 5.6 Software Environment.- 5.7 Communications.- 5.8 Modeling and Control.- 5.9 Future Directions.- 5.10 Conclusion.- 6 Churning: Repeated Optimization and Cooperative Instability.- 6.1 Introduction.- 6.2 Problem Formulation.- 6.3 Receding Horizon Instability and Churning.- 6.4 Churning Instability.- 6.5 Limiting Churn.- 6.6 Example.- 6.7 Discussion and Conclusion.- 7 A Hospitability Map Approach for Estimating a Mobile Targets Location.- 7.1 Introduction.- 7.2 Approach.- 7.3 Simulation Results.- 7.4 Future Research.- 8 Information Theoretic Organization Principles for Autonomous Multiple-Agents.- 8.1 Introduction.- 8.2 Background on Information Theory.- 8.3 Nonparametric Estimation of Renyi's Entropy.- 8.4 Information Particles.- 8.5 Self-Organization of Multiple Agents Using Particle Interaction Principles.- 8.6 Case Study Using a Particular Implementation.- 8.7 Conclusions.- 9 Distributed Agreement Strategies for Cooperative Control: Modeling and Scalability Analysis.- 9.1 Multi-UAV Cooperative Control Problem Model.- 9.2 Cooperative Agreement Strategies.- 9.3 Concluding Remarks.- 10 An Integer Programming Model for Assigning Unmanned Air Vehides to Tasks.- 10.1 Model.- 10.2 Model Characteristics.- 10.3 Solution Method.- 10.4 Computational Experiments.- 11 A Theoretical Foundation for Cooperative Search, Classification, and Target Attack.- 11.1 Introduction.- 11.2 Modelling.- 11.3 Scenario 1.- 11.4 Scenario 2.- 11.5 Scenario 3.- 11.6 Scenario 4.- 11.7 Scenario 5.- 11.8 Scenario 6.- 11.9 Cooperative Control.- 11.10 Conclusion.- 12 Cooperative Real-Time Task Allocation Among Groups of UAVs.- 12.1 Introduction.- 12.2 Algorithm Description.- 12.3 Performance Measures.- 12.4 Simulation Results.- 12.5 Decentralization Approach.- 12.6 Conclusion and Future Work.- Appendix: Derivation of TOP Update Equations.- 13 Use of Conditional Value-at-Risk in Stochastic Programs with Poorly Defined Distributions.- 13.1 Deterministic Weapon-Target Assignment Problem.- 13.2 Two-Stage Stochastic WTA Problem.- 13.3 Two-Stage WTA Problem with Uncertainties in Specified Distributions.- 13.4 Case Study.- 14 Sensitivity Analysis of Partially Deployed Slowdown Warning Mechanisms for Vehicle Platoons.- 14.1 Introduction.- 14.2 Notation and Problem Formulation.- 14.3 DP and NDP Formulations.- 14.4 Main Results.- 14.5 Complexity Reduction: Multilevel Path Planning.- 14.6 Discussion.- 14.7 Conclusion and Future Directions.- Appendix: Proof of Lemma 4.1.- 15 Multi-Target Assignment and Path Planning for Groups of UAVs.- 15.1 Introduction.- 15.2 Simulation Results.- 15.3 Conclusion and Future Work.- 16 Objective Functions for Bayesian Control-Theoretic Sensor Management, II: MHC-Like Approximation.- 16.1 Introduction.- 16.2 Single-Sensor, Single-Target Bayesian Control.- 16.3 Multisensor-Multitarget Bayesian Control.- 16.4 Single-Step Objective Functions.- 16.5 Multistep Objective Functions.- 16.6 Sensor Management With MHC-Like Filters.- 16.7 Mathematical Proofs.- 16.8 Conclusions.- 17 Tracking Environmental Level Sets with Autonomous Vehicles.- 17.1 Introduction.- 17.2 Energy Minimizing Curves in Image Processing.- 17.3 Agent Based Motion via "Virtual" Contours.- 17.4 Implementation and Communication.- 17.5 Cooperative Motion Simulations.- 17.6 Boundary Tracking without Communication.- 17.7 Robustness under Sensor Noise.- 17.8 Conclusions and Future Work.- 18 Cyclic Linearization and Decomposition of Team Game Models.- 18.1 Introduction.- 18.2 General Properties.- 18.3 The Cyclic Linearization Algorithm.- 18.4 Inaccurate Linearized Realizations of the Cyclic Decomposition.- 19 Optimal Path Planning in a Threat Environment.- 19.1 Introduction.- 19.2 Model Development.- 19.3 Calculus of Variations Approach.- 19.4 Network Flow Optimization Approach.- 19.5 Numerical Experiments.- 19.6 Analysis of Computational Results.- 19.7 Conclusions.- Appendix: Minimization of a Functional with Nonholonomic Constraint and Movable End Point.- 20 Nonlinear Dynamics of Sea Clutters and Detection of Small Targets.- 20.1 Introduction.- 20.2 Method.- 20.3 Simulation Results.- 20.4 Experimental Results.- 20.5 Mathematical and Physical Models of Sea Clutter.- 20.6 Discussion and Conclusion.- 21 Tree-Based Algorithms for the Multidimensional Assignment Problem.- 21.1 Introduction.- 21.2 Tree Representations.- 21.3 Branch and Bound Algorithms.- 21.4 Greedy Randomized Adaptive Search Procedure.- 21.5 Concluding Remarks.- 22 Predicting Pop Up Threats From An Adaptive Markov Model.- 22.1 Introduction.- 22.2 Modeling of Pop Up Targets.- 22.3 Adaptive Markov Model.- 22.4 Modeling of UAVs.- 22.5 Generating Data for Red Pop Up Locations.- 22.6 Experimental Results.- 22.7 Conclusion.