Jeffrey Cares is an author, entrepreneur and thought-leader in military innovation. He consults to the most senior levels of the international defense industry and is a leading researcher in collective robotics and networked warfare. He lectures internationally at senior service colleges on the future of combat, and he develops and conducts military and business war games. Harvard Business Review selected Jeff's research to the Top 20 list of "Breakthrough Ideas for 2006," and he has been featured in such Information Age bellwethers as Wired and Fast Company.A combat veteran of the first Gulf War, Jeff's military career included multiple command tours, over a decade of service on four-star staffs, service in the Pentagon and all Fleet Headquarters, and joint and combined operations worldwide. He is a retired Navy Captain.John Dickmann, Jr is a retired U.S. Navy submarine officer. A graduate of the U.S. Naval Academy, he served on active duty for 22 years in both attack and ballistic missile submarines, with shore assignments in both technical and policy organizations. Following his Navy career, he attended the Massachusetts Institute of Technology, earning a Ph.D. in Engineering Systems. His research focuses on architectures of complex socio-technical systems, emphasizing operational flexibility. He has conducted studies and analysis for the Naval Sea Systems Command, DARPA, the Office of the Secretary of Defense and numerous commercial customers.

• About the contributors xiiiAcknowledgements xix1 Introduction 11.1 Introduction 11.2 Background and Scope 31.3 About the Chapters 4References 62 The In‐Transit Vigilant Covering Tour Problem for Routing Unmanned Ground Vehicles 72.1 Introduction 72.2 Background 82.3 CTP for UGV Coverage 92.4 The In‐Transit Vigilant Covering Tour Problem 92.5 Mathematical Formulation 112.6 Extensions to Multiple Vehicles 142.7 Empirical Study 152.8 Analysis of Results 212.9 Other Extensions 242.10 Conclusions 25Author Statement 25References 253 Near‐Optimal Assignment of UAVs to Targets Using a Market‐Based Approach 273.1 Introduction 273.2 Problem Formulation 293.2.1 Inputs 293.2.2 Various Objective Functions 293.2.3 Outputs 313.3 Literature 313.3.1 Solutions to the MDVRP Variants 313.3.2 Market‐Based Techniques 333.4 The Market‐Based Solution 343.4.1 The Basic Market Solution 363.4.2 The Hierarchical Market 373.4.2.1 Motivation and Rationale 373.4.2.2 Algorithm Details 403.4.3 Adaptations for the Max‐Pro Case 413.4.4 Summary 413.5 Results 423.5.1 Optimizing for Fuel‐Consumption (Min‐Sum) 433.5.2 Optimizing for Time (Min‐Max) 443.5.3 Optimizing for Prioritized Targets (Max‐Pro) 473.6 Recommendations for Implementation 513.7 Conclusions 52Appendix 3.A A Mixed Integer Linear Programming (MILP) Formulation 533.A.1 Sub-tour Elimination Constraints 54References 554 Considering Mine Countermeasures Exploratory Operations Conducted by Autonomous Underwater Vehicles 594.1 Background 594.2 Assumptions 614.3 Measures of Performance 624.4 Preliminary Results 644.5 Concepts of Operations 644.5.1 Gaps in Coverage 644.5.2 Aspect Angle Degradation 644.6 Optimality with Two Different Angular Observations 654.7 Optimality with N Different Angular Observations 664.8 Modeling and Algorithms 674.8.1 Monte Carlo Simulation 674.8.2 Deterministic Model 674.9 Random Search Formula Adapted to AUVs 684.10 Mine Countermeasures Exploratory Operations 704.11 Numerical Results 714.12 Non‐uniform Mine Density Distributions 724.13 Conclusion 74Appendix 4.A Optimal Observation Angle between Two AUV Legs 75Appendix 4.B Probabilities of Detection 78References 795 Optical Search by Unmanned Aerial Vehicles: Fauna Detection Case Study 815.1 Introduction 815.2 Search Planning for Unmanned Sensing Operations 825.2.1 Preliminary Flight Analysis 845.2.2 Flight Geometry Control 855.2.3 Images and Mosaics 865.2.4 Digital Analysis and Identification of Elements 885.3 Results 915.4 Conclusions 92Acknowledgments 94References 946 A Flight Time Approximation Model for Unmanned Aerial Vehicles: Estimating the Effects of Path Variations and Wind 95Nomenclature 956.1 Introduction 966.2 Problem Statement 976.3 Literature Review 976.3.1 Flight Time Approximation Models 976.3.2 Additional Task Types to Consider 986.3.3 Wind Effects 996.4 Flight Time Approximation Model Development 996.4.1 Required Mathematical Calculations 1006.4.2 Model Comparisons 1016.4.3 Encountered Problems and Solutions 1026.5 Additional Task Types 1036.5.1 Radius of Sight Task 1036.5.2 Loitering Task 1056.6 Adding Wind Effects 1086.6.1 Implementing the Fuel Burn Rate Model 1106.7 Computational Expense of the Final Model 1116.7.1 Model Runtime Analysis 1116.7.2 Actual versus Expected Flight Times 1136.8 Conclusions and Future Work 115Acknowledgments 117References 1177 Impacts of Unmanned Ground Vehicles on Combined Arms Team Performance 1197.1 Introduction 1197.2 Study Problem 1207.2.1 Terrain 1207.2.2 Vehicle Options 1227.2.3 Forces 1227.2.3.1 Experimental Force 1237.2.3.2 Opposition Force 1237.2.3.3 Civilian Elements 1237.2.4 Mission 1247.3 Study Methods 1257.3.1 Closed‐Loop Simulation 1257.3.2 Study Measures 1267.3.3 System Comparison Approach 1287.4 Study Results 1287.4.1 Basic Casualty Results 1287.4.1.1 Low Density Urban Terrain Casualty Only Results 1287.4.1.2 Dense Urban Terrain Casualty‐Only Results 1307.4.2 Complete Measures Results 1317.4.2.1 Low Density Urban Terrain Results 1317.4.2.2 Dense Urban Terrain Results 1327.4.2.3 Comparison of Low and High Density Urban Results 1337.4.3 Casualty versus Full Measures Comparison 1357.5 Discussion 136References 1378 Processing, Exploitation and Dissemination: When is Aided/Automated Target Recognition “Good Enough” for Operational Use? 1398.1 Introduction 1398.2 Background 1408.2.1 Operational Context and Technical Issues 1408.2.2 Previous Investigations 1418.3 Analysis 1438.3.1 Modeling the Mission 1448.3.2 Modeling the Specific Concept of Operations 1458.3.3 Probability of Acquiring the Target under the Concept of Operations 1468.3.4 Rational Selection between Aided/Automated Target Recognition and Extended Human Sensing 1478.3.5 Finding the Threshold at which Automation is Rational 1488.3.6 Example 1488.4 Conclusion 149Acknowledgments 151Appendix 8.A 151References 1529 Analyzing a Design Continuum for Automated Military Convoy Operations 1559.1 Introduction 1559.2 Definition Development 1569.2.1 Human Input Proportion (H) 1569.2.2 Interaction Frequency 1579.2.3 Complexity of Instructions/Tasks 1579.2.4 Robotic Decision‐Making Ability (R) 1579.3 Automation Continuum 1579.3.1 Status Quo (SQ) 1589.3.2 Remote Control (RC) 1589.3.3 Tele‐Operation (TO) 1589.3.4 Driver Warning (DW) 1589.3.5 Driver Assist (DA) 1589.3.6 Leader‐Follower (LF) 1599.3.6.1 Tethered Leader‐Follower (LF1) 1599.3.6.2 Un‐tethered Leader‐Follower (LF2) 1599.3.6.3 Un‐tethered/Unmanned/Pre‐driven Leader‐Follower (LF3) 1599.3.6.4 Un‐tethered/Unmanned/Uploaded Leader‐Follower (LF4) 1599.3.7 Waypoint (WA) 1599.3.7.1 Pre‐recorded “Breadcrumb” Waypoint (WA1) 1609.3.7.2 Uploaded “Breadcrumb” Waypoint (WA2) 1609.3.8 Full Automation (FA) 1609.3.8.1 Uploaded “Breadcrumbs” with Route Suggestion Full Automation (FA1) 1609.3.8.2 Self‐Determining Full Automation (FA2) 1609.4 Mathematically Modeling Human Input Proportion (H) versus System Configuration 1619.4.1 Modeling H versus System Configuration Methodology 1619.4.2 Analyzing the Results of Modeling H versus System Configuration 1659.4.3 Partitioning the Automation Continuum for H versus System Configuration into Regimes and Analyzing the Results 1689.5 Mathematically Modeling Robotic Decision‐Making Ability (R) versus System Configuration 1699.5.1 Modeling R versus System Configuration Methodology 1699.5.2 Mathematically Modeling R versus System Configuration When Weighted by H 1719.5.3 Partitioning the Automation Continuum for R (Weighted by H) versus System Configuration into Regimes 1759.5.4 Summarizing the Results of Modeling H versus System Configuration and R versus System Configuration When Weighted by H 1779.6 Mathematically Modeling H and R 1789.6.1 Analyzing the Results of Modeling H versus R 1789.7 Conclusion 1809.a System Configurations 18010 Experimental Design for Unmanned Aerial Systems Analysis: Bringing Statistical Rigor to UAS Testing 18710.1 Introduction 18710.2 Some UAS History 18810.3 Statistical Background for Experimental Planning 18910.4 Planning the UAS Experiment 19210.4.1 General Planning Guidelines 19210.4.2 Planning Guidelines for UAS Testing 19310.4.2.1 Determine Specific Questions to Answer 19410.4.2.2 Determine Role of the Human Operator 19410.4.2.3 Define and Delineate Factors of Concern for the Study 19510.4.2.4 Determine and Correlate Response Data 19610.4.2.5 Select an Appropriate Design 19610.4.2.6 Define the Test Execution Strategy 19810.5 Applications of the UAS Planning Guidelines 19910.5.1 Determine the Specific Research Questions 19910.5.2 Determining the Role of Human Operators 19910.5.3 Determine the Response Data 20010.5.4 Define the Experimental Factors 20010.5.5 Establishing the Experimental Protocol 20110.5.6 Select the Appropriate Design 20210.5.6.1 Verifying Feasibility and Practicality of Factor Levels 20210.5.6.2 Factorial Experimentation 20210.5.6.3 The First Validation Experiment 20310.5.6.4 Analysis: Developing a Regression Model 20410.5.6.5 Software Comparison 20410.6 Conclusion 205Acknowledgments 205Disclaimer 205References 20511 Total Cost of Ownership (TOC): An Approach for Estimating UMAS Costs 20711.1 Introduction 20711.2 Life Cycle Models 20811.2.1 DoD 5000 Acquisition Life Cycle 20811.2.2 ISO 15288 Life Cycle 20811.3 Cost Estimation Methods 21011.3.1 Case Study and Analogy 21011.3.2 Bottom‐Up and Activity Based 21111.3.3 Parametric Modeling 21211.4 UMAS Product Breakdown Structure 21211.4.1 Special Considerations 21211.4.1.1 Mission Requirements 21411.4.2 System Capabilities 21411.4.3 Payloads 21411.5 Cost Drivers and Parametric Cost Models 21511.5.1 Cost Drivers for Estimating Development Costs 21511.5.1.1 Hardware 21511.5.1.2 Software 21811.5.1.3 Systems Engineering and Project Management 21811.5.1.4 Performance‐Based Cost Estimating Relationship 22011.5.1.5 Weight‐Based Cost Estimating Relationship 22311.5.2 Proposed Cost Drivers for DoD 5000.02 Phase Operations and Support 22411.5.2.1 Logistics – Transition from Contractor Life Support (CLS) to Organic Capabilities 22411.5.2.2 Training 22411.5.2.3 Operations – Manned Unmanned Systems Teaming (MUM‐T) 22511.6 Considerations for Estimating Unmanned Ground Vehicle Costs 22511.7 Additional Considerations for UMAS Cost Estimation 23011.7.1 Test and Evaluation 23011.7.2 Demonstration 23011.8 Conclusion 230Acknowledgments 231References 23112 Logistics Support for Unmanned Systems 23312.1 Introduction 23312.2 Appreciating Logistics Support for Unmanned Systems 23312.2.1 Logistics 23412.2.2 Operations Research and Logistics 23612.2.3 Unmanned Systems 24012.3 Challenges to Logistics Support for Unmanned Systems 24212.3.1 Immediate Challenges 24212.3.2 Future Challenges 24212.4 Grouping the Logistics Challenges for Analysis and Development 24312.4.1 Group A – No Change to Logistics Support 24312.4.2 Group B – Unmanned Systems Replacing Manned Systems and Their Logistics Support Frameworks 24412.4.3 Group C – Major Changes to Unmanned Systems Logistics 24712.5 Further Considerations 24812.6 Conclusions 251References 25113 Organizing for Improved Effectiveness in Networked Operations 25513.1 Introduction 25513.2 Understanding the IACM 25613.3 An Agent‐Based Simulation Representation of the IACM 25913.4 Structure of the Experiment 26013.5 Initial Experiment 26413.6 Expanding the Experiment 26513.7 Conclusion 269Disclaimer 270References 27014 An Exploration of Performance Distributions in Collectives 27114.1 Introduction 27114.2 Who Shoots How Many? 27214.3 Baseball Plays as Individual and Networked Performance 27314.4 Analytical Questions 27514.5 Imparity Statistics in Major League Baseball Data 27714.5.1 Individual Performance in Major League Baseball 27814.5.2 Interconnected Performance in Major League Baseball 28114.6 Conclusions 285Acknowledgments 286References 28615 Distributed Combat Power: The Application of Salvo Theory to Unmanned Systems 28715.1 Introduction 28715.2 Salvo Theory 28815.2.1 The Salvo Equations 28815.2.2 Interpreting Damage 28915.3 Salvo Warfare with Unmanned Systems 29015.4 The Salvo Exchange Set and Combat Entropy 29115.5 Tactical Considerations 29215.6 Conclusion 293References 294Index 295

Jeffrey Cares is an author, entrepreneur and thought-leader in military innovation. He consults to the most senior levels of the international defense industry and is a leading researcher in collective robotics and networked warfare. He lectures internationally at senior service colleges on the future of combat, and he develops and conducts military and business war games. Harvard Business Review selected Jeff's research to the Top 20 list of "Breakthrough Ideas for 2006," and he has been featured in such Information Age bellwethers as Wired and Fast Company.A combat veteran of the first Gulf War, Jeff's military career included multiple command tours, over a decade of service on four-star staffs, service in the Pentagon and all Fleet Headquarters, and joint and combined operations worldwide. He is a retired Navy Captain.John Dickmann, Jr is a retired U.S. Navy submarine officer. A graduate of the U.S. Naval Academy, he served on active duty for 22 years in both attack and ballistic missile submarines, with shore assignments in both technical and policy organizations. Following his Navy career, he attended the Massachusetts Institute of Technology, earning a Ph.D. in Engineering Systems. His research focuses on architectures of complex socio-technical systems, emphasizing operational flexibility. He has conducted studies and analysis for the Naval Sea Systems Command, DARPA, the Office of the Secretary of Defense and numerous commercial customers.

• About the contributors xiiiAcknowledgements xix1 Introduction 11.1 Introduction 11.2 Background and Scope 31.3 About the Chapters 4References 62 The In‐Transit Vigilant Covering Tour Problem for Routing Unmanned Ground Vehicles 72.1 Introduction 72.2 Background 82.3 CTP for UGV Coverage 92.4 The In‐Transit Vigilant Covering Tour Problem 92.5 Mathematical Formulation 112.6 Extensions to Multiple Vehicles 142.7 Empirical Study 152.8 Analysis of Results 212.9 Other Extensions 242.10 Conclusions 25Author Statement 25References 253 Near‐Optimal Assignment of UAVs to Targets Using a Market‐Based Approach 273.1 Introduction 273.2 Problem Formulation 293.2.1 Inputs 293.2.2 Various Objective Functions 293.2.3 Outputs 313.3 Literature 313.3.1 Solutions to the MDVRP Variants 313.3.2 Market‐Based Techniques 333.4 The Market‐Based Solution 343.4.1 The Basic Market Solution 363.4.2 The Hierarchical Market 373.4.2.1 Motivation and Rationale 373.4.2.2 Algorithm Details 403.4.3 Adaptations for the Max‐Pro Case 413.4.4 Summary 413.5 Results 423.5.1 Optimizing for Fuel‐Consumption (Min‐Sum) 433.5.2 Optimizing for Time (Min‐Max) 443.5.3 Optimizing for Prioritized Targets (Max‐Pro) 473.6 Recommendations for Implementation 513.7 Conclusions 52Appendix 3.A A Mixed Integer Linear Programming (MILP) Formulation 533.A.1 Sub-tour Elimination Constraints 54References 554 Considering Mine Countermeasures Exploratory Operations Conducted by Autonomous Underwater Vehicles 594.1 Background 594.2 Assumptions 614.3 Measures of Performance 624.4 Preliminary Results 644.5 Concepts of Operations 644.5.1 Gaps in Coverage 644.5.2 Aspect Angle Degradation 644.6 Optimality with Two Different Angular Observations 654.7 Optimality with N Different Angular Observations 664.8 Modeling and Algorithms 674.8.1 Monte Carlo Simulation 674.8.2 Deterministic Model 674.9 Random Search Formula Adapted to AUVs 684.10 Mine Countermeasures Exploratory Operations 704.11 Numerical Results 714.12 Non‐uniform Mine Density Distributions 724.13 Conclusion 74Appendix 4.A Optimal Observation Angle between Two AUV Legs 75Appendix 4.B Probabilities of Detection 78References 795 Optical Search by Unmanned Aerial Vehicles: Fauna Detection Case Study 815.1 Introduction 815.2 Search Planning for Unmanned Sensing Operations 825.2.1 Preliminary Flight Analysis 845.2.2 Flight Geometry Control 855.2.3 Images and Mosaics 865.2.4 Digital Analysis and Identification of Elements 885.3 Results 915.4 Conclusions 92Acknowledgments 94References 946 A Flight Time Approximation Model for Unmanned Aerial Vehicles: Estimating the Effects of Path Variations and Wind 95Nomenclature 956.1 Introduction 966.2 Problem Statement 976.3 Literature Review 976.3.1 Flight Time Approximation Models 976.3.2 Additional Task Types to Consider 986.3.3 Wind Effects 996.4 Flight Time Approximation Model Development 996.4.1 Required Mathematical Calculations 1006.4.2 Model Comparisons 1016.4.3 Encountered Problems and Solutions 1026.5 Additional Task Types 1036.5.1 Radius of Sight Task 1036.5.2 Loitering Task 1056.6 Adding Wind Effects 1086.6.1 Implementing the Fuel Burn Rate Model 1106.7 Computational Expense of the Final Model 1116.7.1 Model Runtime Analysis 1116.7.2 Actual versus Expected Flight Times 1136.8 Conclusions and Future Work 115Acknowledgments 117References 1177 Impacts of Unmanned Ground Vehicles on Combined Arms Team Performance 1197.1 Introduction 1197.2 Study Problem 1207.2.1 Terrain 1207.2.2 Vehicle Options 1227.2.3 Forces 1227.2.3.1 Experimental Force 1237.2.3.2 Opposition Force 1237.2.3.3 Civilian Elements 1237.2.4 Mission 1247.3 Study Methods 1257.3.1 Closed‐Loop Simulation 1257.3.2 Study Measures 1267.3.3 System Comparison Approach 1287.4 Study Results 1287.4.1 Basic Casualty Results 1287.4.1.1 Low Density Urban Terrain Casualty Only Results 1287.4.1.2 Dense Urban Terrain Casualty‐Only Results 1307.4.2 Complete Measures Results 1317.4.2.1 Low Density Urban Terrain Results 1317.4.2.2 Dense Urban Terrain Results 1327.4.2.3 Comparison of Low and High Density Urban Results 1337.4.3 Casualty versus Full Measures Comparison 1357.5 Discussion 136References 1378 Processing, Exploitation and Dissemination: When is Aided/Automated Target Recognition “Good Enough” for Operational Use? 1398.1 Introduction 1398.2 Background 1408.2.1 Operational Context and Technical Issues 1408.2.2 Previous Investigations 1418.3 Analysis 1438.3.1 Modeling the Mission 1448.3.2 Modeling the Specific Concept of Operations 1458.3.3 Probability of Acquiring the Target under the Concept of Operations 1468.3.4 Rational Selection between Aided/Automated Target Recognition and Extended Human Sensing 1478.3.5 Finding the Threshold at which Automation is Rational 1488.3.6 Example 1488.4 Conclusion 149Acknowledgments 151Appendix 8.A 151References 1529 Analyzing a Design Continuum for Automated Military Convoy Operations 1559.1 Introduction 1559.2 Definition Development 1569.2.1 Human Input Proportion (H) 1569.2.2 Interaction Frequency 1579.2.3 Complexity of Instructions/Tasks 1579.2.4 Robotic Decision‐Making Ability (R) 1579.3 Automation Continuum 1579.3.1 Status Quo (SQ) 1589.3.2 Remote Control (RC) 1589.3.3 Tele‐Operation (TO) 1589.3.4 Driver Warning (DW) 1589.3.5 Driver Assist (DA) 1589.3.6 Leader‐Follower (LF) 1599.3.6.1 Tethered Leader‐Follower (LF1) 1599.3.6.2 Un‐tethered Leader‐Follower (LF2) 1599.3.6.3 Un‐tethered/Unmanned/Pre‐driven Leader‐Follower (LF3) 1599.3.6.4 Un‐tethered/Unmanned/Uploaded Leader‐Follower (LF4) 1599.3.7 Waypoint (WA) 1599.3.7.1 Pre‐recorded “Breadcrumb” Waypoint (WA1) 1609.3.7.2 Uploaded “Breadcrumb” Waypoint (WA2) 1609.3.8 Full Automation (FA) 1609.3.8.1 Uploaded “Breadcrumbs” with Route Suggestion Full Automation (FA1) 1609.3.8.2 Self‐Determining Full Automation (FA2) 1609.4 Mathematically Modeling Human Input Proportion (H) versus System Configuration 1619.4.1 Modeling H versus System Configuration Methodology 1619.4.2 Analyzing the Results of Modeling H versus System Configuration 1659.4.3 Partitioning the Automation Continuum for H versus System Configuration into Regimes and Analyzing the Results 1689.5 Mathematically Modeling Robotic Decision‐Making Ability (R) versus System Configuration 1699.5.1 Modeling R versus System Configuration Methodology 1699.5.2 Mathematically Modeling R versus System Configuration When Weighted by H 1719.5.3 Partitioning the Automation Continuum for R (Weighted by H) versus System Configuration into Regimes 1759.5.4 Summarizing the Results of Modeling H versus System Configuration and R versus System Configuration When Weighted by H 1779.6 Mathematically Modeling H and R 1789.6.1 Analyzing the Results of Modeling H versus R 1789.7 Conclusion 1809.a System Configurations 18010 Experimental Design for Unmanned Aerial Systems Analysis: Bringing Statistical Rigor to UAS Testing 18710.1 Introduction 18710.2 Some UAS History 18810.3 Statistical Background for Experimental Planning 18910.4 Planning the UAS Experiment 19210.4.1 General Planning Guidelines 19210.4.2 Planning Guidelines for UAS Testing 19310.4.2.1 Determine Specific Questions to Answer 19410.4.2.2 Determine Role of the Human Operator 19410.4.2.3 Define and Delineate Factors of Concern for the Study 19510.4.2.4 Determine and Correlate Response Data 19610.4.2.5 Select an Appropriate Design 19610.4.2.6 Define the Test Execution Strategy 19810.5 Applications of the UAS Planning Guidelines 19910.5.1 Determine the Specific Research Questions 19910.5.2 Determining the Role of Human Operators 19910.5.3 Determine the Response Data 20010.5.4 Define the Experimental Factors 20010.5.5 Establishing the Experimental Protocol 20110.5.6 Select the Appropriate Design 20210.5.6.1 Verifying Feasibility and Practicality of Factor Levels 20210.5.6.2 Factorial Experimentation 20210.5.6.3 The First Validation Experiment 20310.5.6.4 Analysis: Developing a Regression Model 20410.5.6.5 Software Comparison 20410.6 Conclusion 205Acknowledgments 205Disclaimer 205References 20511 Total Cost of Ownership (TOC): An Approach for Estimating UMAS Costs 20711.1 Introduction 20711.2 Life Cycle Models 20811.2.1 DoD 5000 Acquisition Life Cycle 20811.2.2 ISO 15288 Life Cycle 20811.3 Cost Estimation Methods 21011.3.1 Case Study and Analogy 21011.3.2 Bottom‐Up and Activity Based 21111.3.3 Parametric Modeling 21211.4 UMAS Product Breakdown Structure 21211.4.1 Special Considerations 21211.4.1.1 Mission Requirements 21411.4.2 System Capabilities 21411.4.3 Payloads 21411.5 Cost Drivers and Parametric Cost Models 21511.5.1 Cost Drivers for Estimating Development Costs 21511.5.1.1 Hardware 21511.5.1.2 Software 21811.5.1.3 Systems Engineering and Project Management 21811.5.1.4 Performance‐Based Cost Estimating Relationship 22011.5.1.5 Weight‐Based Cost Estimating Relationship 22311.5.2 Proposed Cost Drivers for DoD 5000.02 Phase Operations and Support 22411.5.2.1 Logistics – Transition from Contractor Life Support (CLS) to Organic Capabilities 22411.5.2.2 Training 22411.5.2.3 Operations – Manned Unmanned Systems Teaming (MUM‐T) 22511.6 Considerations for Estimating Unmanned Ground Vehicle Costs 22511.7 Additional Considerations for UMAS Cost Estimation 23011.7.1 Test and Evaluation 23011.7.2 Demonstration 23011.8 Conclusion 230Acknowledgments 231References 23112 Logistics Support for Unmanned Systems 23312.1 Introduction 23312.2 Appreciating Logistics Support for Unmanned Systems 23312.2.1 Logistics 23412.2.2 Operations Research and Logistics 23612.2.3 Unmanned Systems 24012.3 Challenges to Logistics Support for Unmanned Systems 24212.3.1 Immediate Challenges 24212.3.2 Future Challenges 24212.4 Grouping the Logistics Challenges for Analysis and Development 24312.4.1 Group A – No Change to Logistics Support 24312.4.2 Group B – Unmanned Systems Replacing Manned Systems and Their Logistics Support Frameworks 24412.4.3 Group C – Major Changes to Unmanned Systems Logistics 24712.5 Further Considerations 24812.6 Conclusions 251References 25113 Organizing for Improved Effectiveness in Networked Operations 25513.1 Introduction 25513.2 Understanding the IACM 25613.3 An Agent‐Based Simulation Representation of the IACM 25913.4 Structure of the Experiment 26013.5 Initial Experiment 26413.6 Expanding the Experiment 26513.7 Conclusion 269Disclaimer 270References 27014 An Exploration of Performance Distributions in Collectives 27114.1 Introduction 27114.2 Who Shoots How Many? 27214.3 Baseball Plays as Individual and Networked Performance 27314.4 Analytical Questions 27514.5 Imparity Statistics in Major League Baseball Data 27714.5.1 Individual Performance in Major League Baseball 27814.5.2 Interconnected Performance in Major League Baseball 28114.6 Conclusions 285Acknowledgments 286References 28615 Distributed Combat Power: The Application of Salvo Theory to Unmanned Systems 28715.1 Introduction 28715.2 Salvo Theory 28815.2.1 The Salvo Equations 28815.2.2 Interpreting Damage 28915.3 Salvo Warfare with Unmanned Systems 29015.4 The Salvo Exchange Set and Combat Entropy 29115.5 Tactical Considerations 29215.6 Conclusion 293References 294Index 295

Jeffrey Cares is an author, entrepreneur and thought-leader in military innovation. He consults to the most senior levels of the international defense industry and is a leading researcher in collective robotics and networked warfare. He lectures internationally at senior service colleges on the future of combat, and he develops and conducts military and business war games. Harvard Business Review selected Jeff's research to the Top 20 list of "Breakthrough Ideas for 2006," and he has been featured in such Information Age bellwethers as Wired and Fast Company.A combat veteran of the first Gulf War, Jeff's military career included multiple command tours, over a decade of service on four-star staffs, service in the Pentagon and all Fleet Headquarters, and joint and combined operations worldwide. He is a retired Navy Captain.John Dickmann, Jr is a retired U.S. Navy submarine officer. A graduate of the U.S. Naval Academy, he served on active duty for 22 years in both attack and ballistic missile submarines, with shore assignments in both technical and policy organizations. Following his Navy career, he attended the Massachusetts Institute of Technology, earning a Ph.D. in Engineering Systems. His research focuses on architectures of complex socio-technical systems, emphasizing operational flexibility. He has conducted studies and analysis for the Naval Sea Systems Command, DARPA, the Office of the Secretary of Defense and numerous commercial customers.

• About the contributors xiiiAcknowledgements xix1 Introduction 11.1 Introduction 11.2 Background and Scope 31.3 About the Chapters 4References 62 The In‐Transit Vigilant Covering Tour Problem for Routing Unmanned Ground Vehicles 72.1 Introduction 72.2 Background 82.3 CTP for UGV Coverage 92.4 The In‐Transit Vigilant Covering Tour Problem 92.5 Mathematical Formulation 112.6 Extensions to Multiple Vehicles 142.7 Empirical Study 152.8 Analysis of Results 212.9 Other Extensions 242.10 Conclusions 25Author Statement 25References 253 Near‐Optimal Assignment of UAVs to Targets Using a Market‐Based Approach 273.1 Introduction 273.2 Problem Formulation 293.2.1 Inputs 293.2.2 Various Objective Functions 293.2.3 Outputs 313.3 Literature 313.3.1 Solutions to the MDVRP Variants 313.3.2 Market‐Based Techniques 333.4 The Market‐Based Solution 343.4.1 The Basic Market Solution 363.4.2 The Hierarchical Market 373.4.2.1 Motivation and Rationale 373.4.2.2 Algorithm Details 403.4.3 Adaptations for the Max‐Pro Case 413.4.4 Summary 413.5 Results 423.5.1 Optimizing for Fuel‐Consumption (Min‐Sum) 433.5.2 Optimizing for Time (Min‐Max) 443.5.3 Optimizing for Prioritized Targets (Max‐Pro) 473.6 Recommendations for Implementation 513.7 Conclusions 52Appendix 3.A A Mixed Integer Linear Programming (MILP) Formulation 533.A.1 Sub-tour Elimination Constraints 54References 554 Considering Mine Countermeasures Exploratory Operations Conducted by Autonomous Underwater Vehicles 594.1 Background 594.2 Assumptions 614.3 Measures of Performance 624.4 Preliminary Results 644.5 Concepts of Operations 644.5.1 Gaps in Coverage 644.5.2 Aspect Angle Degradation 644.6 Optimality with Two Different Angular Observations 654.7 Optimality with N Different Angular Observations 664.8 Modeling and Algorithms 674.8.1 Monte Carlo Simulation 674.8.2 Deterministic Model 674.9 Random Search Formula Adapted to AUVs 684.10 Mine Countermeasures Exploratory Operations 704.11 Numerical Results 714.12 Non‐uniform Mine Density Distributions 724.13 Conclusion 74Appendix 4.A Optimal Observation Angle between Two AUV Legs 75Appendix 4.B Probabilities of Detection 78References 795 Optical Search by Unmanned Aerial Vehicles: Fauna Detection Case Study 815.1 Introduction 815.2 Search Planning for Unmanned Sensing Operations 825.2.1 Preliminary Flight Analysis 845.2.2 Flight Geometry Control 855.2.3 Images and Mosaics 865.2.4 Digital Analysis and Identification of Elements 885.3 Results 915.4 Conclusions 92Acknowledgments 94References 946 A Flight Time Approximation Model for Unmanned Aerial Vehicles: Estimating the Effects of Path Variations and Wind 95Nomenclature 956.1 Introduction 966.2 Problem Statement 976.3 Literature Review 976.3.1 Flight Time Approximation Models 976.3.2 Additional Task Types to Consider 986.3.3 Wind Effects 996.4 Flight Time Approximation Model Development 996.4.1 Required Mathematical Calculations 1006.4.2 Model Comparisons 1016.4.3 Encountered Problems and Solutions 1026.5 Additional Task Types 1036.5.1 Radius of Sight Task 1036.5.2 Loitering Task 1056.6 Adding Wind Effects 1086.6.1 Implementing the Fuel Burn Rate Model 1106.7 Computational Expense of the Final Model 1116.7.1 Model Runtime Analysis 1116.7.2 Actual versus Expected Flight Times 1136.8 Conclusions and Future Work 115Acknowledgments 117References 1177 Impacts of Unmanned Ground Vehicles on Combined Arms Team Performance 1197.1 Introduction 1197.2 Study Problem 1207.2.1 Terrain 1207.2.2 Vehicle Options 1227.2.3 Forces 1227.2.3.1 Experimental Force 1237.2.3.2 Opposition Force 1237.2.3.3 Civilian Elements 1237.2.4 Mission 1247.3 Study Methods 1257.3.1 Closed‐Loop Simulation 1257.3.2 Study Measures 1267.3.3 System Comparison Approach 1287.4 Study Results 1287.4.1 Basic Casualty Results 1287.4.1.1 Low Density Urban Terrain Casualty Only Results 1287.4.1.2 Dense Urban Terrain Casualty‐Only Results 1307.4.2 Complete Measures Results 1317.4.2.1 Low Density Urban Terrain Results 1317.4.2.2 Dense Urban Terrain Results 1327.4.2.3 Comparison of Low and High Density Urban Results 1337.4.3 Casualty versus Full Measures Comparison 1357.5 Discussion 136References 1378 Processing, Exploitation and Dissemination: When is Aided/Automated Target Recognition “Good Enough” for Operational Use? 1398.1 Introduction 1398.2 Background 1408.2.1 Operational Context and Technical Issues 1408.2.2 Previous Investigations 1418.3 Analysis 1438.3.1 Modeling the Mission 1448.3.2 Modeling the Specific Concept of Operations 1458.3.3 Probability of Acquiring the Target under the Concept of Operations 1468.3.4 Rational Selection between Aided/Automated Target Recognition and Extended Human Sensing 1478.3.5 Finding the Threshold at which Automation is Rational 1488.3.6 Example 1488.4 Conclusion 149Acknowledgments 151Appendix 8.A 151References 1529 Analyzing a Design Continuum for Automated Military Convoy Operations 1559.1 Introduction 1559.2 Definition Development 1569.2.1 Human Input Proportion (H) 1569.2.2 Interaction Frequency 1579.2.3 Complexity of Instructions/Tasks 1579.2.4 Robotic Decision‐Making Ability (R) 1579.3 Automation Continuum 1579.3.1 Status Quo (SQ) 1589.3.2 Remote Control (RC) 1589.3.3 Tele‐Operation (TO) 1589.3.4 Driver Warning (DW) 1589.3.5 Driver Assist (DA) 1589.3.6 Leader‐Follower (LF) 1599.3.6.1 Tethered Leader‐Follower (LF1) 1599.3.6.2 Un‐tethered Leader‐Follower (LF2) 1599.3.6.3 Un‐tethered/Unmanned/Pre‐driven Leader‐Follower (LF3) 1599.3.6.4 Un‐tethered/Unmanned/Uploaded Leader‐Follower (LF4) 1599.3.7 Waypoint (WA) 1599.3.7.1 Pre‐recorded “Breadcrumb” Waypoint (WA1) 1609.3.7.2 Uploaded “Breadcrumb” Waypoint (WA2) 1609.3.8 Full Automation (FA) 1609.3.8.1 Uploaded “Breadcrumbs” with Route Suggestion Full Automation (FA1) 1609.3.8.2 Self‐Determining Full Automation (FA2) 1609.4 Mathematically Modeling Human Input Proportion (H) versus System Configuration 1619.4.1 Modeling H versus System Configuration Methodology 1619.4.2 Analyzing the Results of Modeling H versus System Configuration 1659.4.3 Partitioning the Automation Continuum for H versus System Configuration into Regimes and Analyzing the Results 1689.5 Mathematically Modeling Robotic Decision‐Making Ability (R) versus System Configuration 1699.5.1 Modeling R versus System Configuration Methodology 1699.5.2 Mathematically Modeling R versus System Configuration When Weighted by H 1719.5.3 Partitioning the Automation Continuum for R (Weighted by H) versus System Configuration into Regimes 1759.5.4 Summarizing the Results of Modeling H versus System Configuration and R versus System Configuration When Weighted by H 1779.6 Mathematically Modeling H and R 1789.6.1 Analyzing the Results of Modeling H versus R 1789.7 Conclusion 1809.a System Configurations 18010 Experimental Design for Unmanned Aerial Systems Analysis: Bringing Statistical Rigor to UAS Testing 18710.1 Introduction 18710.2 Some UAS History 18810.3 Statistical Background for Experimental Planning 18910.4 Planning the UAS Experiment 19210.4.1 General Planning Guidelines 19210.4.2 Planning Guidelines for UAS Testing 19310.4.2.1 Determine Specific Questions to Answer 19410.4.2.2 Determine Role of the Human Operator 19410.4.2.3 Define and Delineate Factors of Concern for the Study 19510.4.2.4 Determine and Correlate Response Data 19610.4.2.5 Select an Appropriate Design 19610.4.2.6 Define the Test Execution Strategy 19810.5 Applications of the UAS Planning Guidelines 19910.5.1 Determine the Specific Research Questions 19910.5.2 Determining the Role of Human Operators 19910.5.3 Determine the Response Data 20010.5.4 Define the Experimental Factors 20010.5.5 Establishing the Experimental Protocol 20110.5.6 Select the Appropriate Design 20210.5.6.1 Verifying Feasibility and Practicality of Factor Levels 20210.5.6.2 Factorial Experimentation 20210.5.6.3 The First Validation Experiment 20310.5.6.4 Analysis: Developing a Regression Model 20410.5.6.5 Software Comparison 20410.6 Conclusion 205Acknowledgments 205Disclaimer 205References 20511 Total Cost of Ownership (TOC): An Approach for Estimating UMAS Costs 20711.1 Introduction 20711.2 Life Cycle Models 20811.2.1 DoD 5000 Acquisition Life Cycle 20811.2.2 ISO 15288 Life Cycle 20811.3 Cost Estimation Methods 21011.3.1 Case Study and Analogy 21011.3.2 Bottom‐Up and Activity Based 21111.3.3 Parametric Modeling 21211.4 UMAS Product Breakdown Structure 21211.4.1 Special Considerations 21211.4.1.1 Mission Requirements 21411.4.2 System Capabilities 21411.4.3 Payloads 21411.5 Cost Drivers and Parametric Cost Models 21511.5.1 Cost Drivers for Estimating Development Costs 21511.5.1.1 Hardware 21511.5.1.2 Software 21811.5.1.3 Systems Engineering and Project Management 21811.5.1.4 Performance‐Based Cost Estimating Relationship 22011.5.1.5 Weight‐Based Cost Estimating Relationship 22311.5.2 Proposed Cost Drivers for DoD 5000.02 Phase Operations and Support 22411.5.2.1 Logistics – Transition from Contractor Life Support (CLS) to Organic Capabilities 22411.5.2.2 Training 22411.5.2.3 Operations – Manned Unmanned Systems Teaming (MUM‐T) 22511.6 Considerations for Estimating Unmanned Ground Vehicle Costs 22511.7 Additional Considerations for UMAS Cost Estimation 23011.7.1 Test and Evaluation 23011.7.2 Demonstration 23011.8 Conclusion 230Acknowledgments 231References 23112 Logistics Support for Unmanned Systems 23312.1 Introduction 23312.2 Appreciating Logistics Support for Unmanned Systems 23312.2.1 Logistics 23412.2.2 Operations Research and Logistics 23612.2.3 Unmanned Systems 24012.3 Challenges to Logistics Support for Unmanned Systems 24212.3.1 Immediate Challenges 24212.3.2 Future Challenges 24212.4 Grouping the Logistics Challenges for Analysis and Development 24312.4.1 Group A – No Change to Logistics Support 24312.4.2 Group B – Unmanned Systems Replacing Manned Systems and Their Logistics Support Frameworks 24412.4.3 Group C – Major Changes to Unmanned Systems Logistics 24712.5 Further Considerations 24812.6 Conclusions 251References 25113 Organizing for Improved Effectiveness in Networked Operations 25513.1 Introduction 25513.2 Understanding the IACM 25613.3 An Agent‐Based Simulation Representation of the IACM 25913.4 Structure of the Experiment 26013.5 Initial Experiment 26413.6 Expanding the Experiment 26513.7 Conclusion 269Disclaimer 270References 27014 An Exploration of Performance Distributions in Collectives 27114.1 Introduction 27114.2 Who Shoots How Many? 27214.3 Baseball Plays as Individual and Networked Performance 27314.4 Analytical Questions 27514.5 Imparity Statistics in Major League Baseball Data 27714.5.1 Individual Performance in Major League Baseball 27814.5.2 Interconnected Performance in Major League Baseball 28114.6 Conclusions 285Acknowledgments 286References 28615 Distributed Combat Power: The Application of Salvo Theory to Unmanned Systems 28715.1 Introduction 28715.2 Salvo Theory 28815.2.1 The Salvo Equations 28815.2.2 Interpreting Damage 28915.3 Salvo Warfare with Unmanned Systems 29015.4 The Salvo Exchange Set and Combat Entropy 29115.5 Tactical Considerations 29215.6 Conclusion 293References 294Index 295