The first edited volume addressing analysis for unmanned vehicles, with focus on operations research rather than engineering
- The editors have a unique combination of extensive operational experience and technical expertise
- Chapters address a wide-ranging set of examples, domains and applications
- Accessible to a general readership and also informative for experts
Table of Contents:
About the contributors xiii
Acknowledgements xix
1 Introduction 1
1.1 Introduction 1
1.2 Background and Scope 3
1.3 About the Chapters 4
References 6
2 The In‐Transit Vigilant Covering Tour Problem for Routing Unmanned Ground Vehicles 7
2.1 Introduction 7
2.2 Background 8
2.3 CTP for UGV Coverage 9
2.4 The In‐Transit Vigilant Covering Tour Problem 9
2.5 Mathematical Formulation 11
2.6 Extensions to Multiple Vehicles 14
2.7 Empirical Study 15
2.8 Analysis of Results 21
2.9 Other Extensions 24
2.10 Conclusions 25
Author Statement 25
References 25
3 Near‐Optimal Assignment of UAVs to Targets Using a Market‐Based Approach 27
3.1 Introduction 27
3.2 Problem Formulation 29
3.2.1 Inputs 29
3.2.2 Various Objective Functions 29
3.2.3 Outputs 31
3.3 Literature 31
3.3.1 Solutions to the MDVRP Variants 31
3.3.2 Market‐Based Techniques 33
3.4 The Market‐Based Solution 34
3.4.1 The Basic Market Solution 36
3.4.2 The Hierarchical Market 37
3.4.2.1 Motivation and Rationale 37
3.4.2.2 Algorithm Details 40
3.4.3 Adaptations for the Max‐Pro Case 41
3.4.4 Summary 41
3.5 Results 42
3.5.1 Optimizing for Fuel‐Consumption (Min‐Sum) 43
3.5.2 Optimizing for Time (Min‐Max) 44
3.5.3 Optimizing for Prioritized Targets (Max‐Pro) 47
3.6 Recommendations for Implementation 51
3.7 Conclusions 52
Appendix 3.A A Mixed Integer Linear Programming (MILP) Formulation 53
3.A.1 Sub-tour Elimination Constraints 54
References 55
4 Considering Mine Countermeasures Exploratory Operations Conducted by Autonomous Underwater Vehicles 59
4.1 Background 59
4.2 Assumptions 61
4.3 Measures of Performance 62
4.4 Preliminary Results 64
4.5 Concepts of Operations 64
4.5.1 Gaps in Coverage 64
4.5.2 Aspect Angle Degradation 64
4.6 Optimality with Two Different Angular Observations 65
4.7 Optimality with N Different Angular Observations 66
4.8 Modeling and Algorithms 67
4.8.1 Monte Carlo Simulation 67
4.8.2 Deterministic Model 67
4.9 Random Search Formula Adapted to AUVs 68
4.10 Mine Countermeasures Exploratory Operations 70
4.11 Numerical Results 71
4.12 Non‐uniform Mine Density Distributions 72
4.13 Conclusion 74
Appendix 4.A Optimal Observation Angle between Two AUV Legs 75
Appendix 4.B Probabilities of Detection 78
References 79
5 Optical Search by Unmanned Aerial Vehicles: Fauna Detection Case Study 81
5.1 Introduction 81
5.2 Search Planning for Unmanned Sensing Operations 82
5.2.1 Preliminary Flight Analysis 84
5.2.2 Flight Geometry Control 85
5.2.3 Images and Mosaics 86
5.2.4 Digital Analysis and Identification of Elements 88
5.3 Results 91
5.4 Conclusions 92
Acknowledgments 94
References 94
6 A Flight Time Approximation Model for Unmanned Aerial Vehicles: Estimating the Effects of Path Variations and Wind 95
Nomenclature 95
6.1 Introduction 96
6.2 Problem Statement 97
6.3 Literature Review 97
6.3.1 Flight Time Approximation Models 97
6.3.2 Additional Task Types to Consider 98
6.3.3 Wind Effects 99
6.4 Flight Time Approximation Model Development 99
6.4.1 Required Mathematical Calculations 100
6.4.2 Model Comparisons 101
6.4.3 Encountered Problems and Solutions 102
6.5 Additional Task Types 103
6.5.1 Radius of Sight Task 103
6.5.2 Loitering Task 105
6.6 Adding Wind Effects 108
6.6.1 Implementing the Fuel Burn Rate Model 110
6.7 Computational Expense of the Final Model 111
6.7.1 Model Runtime Analysis 111
6.7.2 Actual versus Expected Flight Times 113
6.8 Conclusions and Future Work 115
Acknowledgments 117
References 117
7 Impacts of Unmanned Ground Vehicles on Combined Arms Team Performance 119
7.1 Introduction 119
7.2 Study Problem 120
7.2.1 Terrain 120
7.2.2 Vehicle Options 122
7.2.3 Forces 122
7.2.3.1 Experimental Force 123
7.2.3.2 Opposition Force 123
7.2.3.3 Civilian Elements 123
7.2.4 Mission 124
7.3 Study Methods 125
7.3.1 Closed‐Loop Simulation 125
7.3.2 Study Measures 126
7.3.3 System Comparison Approach 128
7.4 Study Results 128
7.4.1 Basic Casualty Results 128
7.4.1.1 Low Density Urban Terrain Casualty Only Results 128
7.4.1.2 Dense Urban Terrain Casualty‐Only Results 130
7.4.2 Complete Measures Results 131
7.4.2.1 Low Density Urban Terrain Results 131
7.4.2.2 Dense Urban Terrain Results 132
7.4.2.3 Comparison of Low and High Density Urban Results 133
7.4.3 Casualty versus Full Measures Comparison 135
7.5 Discussion 136
References 137
8 Processing, Exploitation and Dissemination: When is Aided/Automated Target Recognition “Good Enough” for Operational Use? 139
8.1 Introduction 139
8.2 Background 140
8.2.1 Operational Context and Technical Issues 140
8.2.2 Previous Investigations 141
8.3 Analysis 143
8.3.1 Modeling the Mission 144
8.3.2 Modeling the Specific Concept of Operations 145
8.3.3 Probability of Acquiring the Target under the Concept of Operations 146
8.3.4 Rational Selection between Aided/Automated Target Recognition and Extended Human Sensing 147
8.3.5 Finding the Threshold at which Automation is Rational 148
8.3.6 Example 148
8.4 Conclusion 149
Acknowledgments 151
Appendix 8.A 151
Ensuring [Q ] * decreases as ζ* increases 152
References 152
9 Analyzing a Design Continuum for Automated Military Convoy Operations 155
9.1 Introduction 155
9.2 Definition Development 156
9.2.1 Human Input Proportion (H) 156
9.2.2 Interaction Frequency 157
9.2.3 Complexity of Instructions/Tasks 157
9.2.4 Robotic Decision‐Making Ability (R) 157
9.3 Automation Continuum 157
9.3.1 Status Quo (SQ) 158
9.3.2 Remote Control (RC) 158
9.3.3 Tele‐Operation (TO) 158
9.3.4 Driver Warning (DW) 158
9.3.5 Driver Assist (DA) 158
9.3.6 Leader‐Follower (LF) 159
9.3.6.1 Tethered Leader‐Follower (LF1) 159
9.3.6.2 Un‐tethered Leader‐Follower (LF2) 159
9.3.6.3 Un‐tethered/Unmanned/Pre‐driven Leader‐Follower (LF3) 159
9.3.6.4 Un‐tethered/Unmanned/Uploaded Leader‐Follower (LF4) 159
9.3.7 Waypoint (WA) 159
9.3.7.1 Pre‐recorded “Breadcrumb” Waypoint (WA1) 160
9.3.7.2 Uploaded “Breadcrumb” Waypoint (WA2) 160
9.3.8 Full Automation (FA) 160
9.3.8.1 Uploaded “Breadcrumbs” with Route Suggestion Full Automation (FA1) 160
9.3.8.2 Self‐Determining Full Automation (FA2) 160
9.4 Mathematically Modeling Human Input Proportion (H) versus System Configuration 161
9.4.1 Modeling H versus System Configuration Methodology 161
9.4.2 Analyzing the Results of Modeling H versus System Configuration 165
9.4.3 Partitioning the Automation Continuum for H versus System Configuration into Regimes and Analyzing the Results 168
9.5 Mathematically Modeling Robotic Decision‐Making Ability (R) versus System Configuration 169
9.5.1 Modeling R versus System Configuration Methodology 169
9.5.2 Mathematically Modeling R versus System Configuration When Weighted by H 171
9.5.3 Partitioning the Automation Continuum for R (Weighted by H) versus System Configuration into Regimes 175
9.5.4 Summarizing the Results of Modeling H versus System Configuration and R versus System Configuration When Weighted by H 177
9.6 Mathematically Modeling H and R 178
9.6.1 Analyzing the Results of Modeling H versus R 178
9.7 Conclusion 180
9.A System Configurations 180
10 Experimental Design for Unmanned Aerial Systems Analysis: Bringing Statistical Rigor to UAS Testing 187
10.1 Introduction 187
10.2 Some UAS History 188
10.3 Statistical Background for Experimental Planning 189
10.4 Planning the UAS Experiment 192
10.4.1 General Planning Guidelines 192
10.4.2 Planning Guidelines for UAS Testing 193
10.4.2.1 Determine Specific Questions to Answer 194
10.4.2.2 Determine Role of the Human Operator 194
10.4.2.3 Define and Delineate Factors of Concern for the Study 195
10.4.2.4 Determine and Correlate Response Data 196
10.4.2.5 Select an Appropriate Design 196
10.4.2.6 Define the Test Execution Strategy 198
10.5 Applications of the UAS Planning Guidelines 199
10.5.1 Determine the Specific Research Questions 199
10.5.2 Determining the Role of Human Operators 199
10.5.3 Determine the Response Data 200
10.5.4 Define the Experimental Factors 200
10.5.5 Establishing the Experimental Protocol 201
10.5.6 Select the Appropriate Design 202
10.5.6.1 Verifying Feasibility and Practicality of Factor Levels 202
10.5.6.2 Factorial Experimentation 202
10.5.6.3 The First Validation Experiment 203
10.5.6.4 Analysis: Developing a Regression Model 204
10.5.6.5 Software Comparison 204
10.6 Conclusion 205
Acknowledgments 205
Disclaimer 205
References 205
11 Total Cost of Ownership (TOC): An Approach for Estimating UMAS Costs 207
11.1 Introduction 207
11.2 Life Cycle Models 208
11.2.1 DoD 5000 Acquisition Life Cycle 208
11.2.2 ISO 15288 Life Cycle 208
11.3 Cost Estimation Methods 210
11.3.1 Case Study and Analogy 210
11.3.2 Bottom‐Up and Activity Based 211
11.3.3 Parametric Modeling 212
11.4 UMAS Product Breakdown Structure 212
11.4.1 Special Considerations 212
11.4.1.1 Mission Requirements 214
11.4.2 System Capabilities 214
11.4.3 Payloads 214
11.5 Cost Drivers and Parametric Cost Models 215
11.5.1 Cost Drivers for Estimating Development Costs 215
11.5.1.1 Hardware 215
11.5.1.2 Software 218
11.5.1.3 Systems Engineering and Project Management 218
11.5.1.4 Performance‐Based Cost Estimating Relationship 220
11.5.1.5 Weight‐Based Cost Estimating Relationship 223
11.5.2 Proposed Cost Drivers for DoD 5000.02 Phase Operations and Support 224
11.5.2.1 Logistics – Transition from Contractor Life Support (CLS) to Organic Capabilities 224
11.5.2.2 Training 224
11.5.2.3 Operations – Manned Unmanned Systems Teaming (MUM‐T) 225
11.6 Considerations for Estimating Unmanned Ground Vehicle Costs 225
11.7 Additional Considerations for UMAS Cost Estimation 230
11.7.1 Test and Evaluation 230
11.7.2 Demonstration 230
11.8 Conclusion 230
Acknowledgments 231
References 231
12 Logistics Support for Unmanned Systems 233
12.1 Introduction 233
12.2 Appreciating Logistics Support for Unmanned Systems 233
12.2.1 Logistics 234
12.2.2 Operations Research and Logistics 236
12.2.3 Unmanned Systems 240
12.3 Challenges to Logistics Support for Unmanned Systems 242
12.3.1 Immediate Challenges 242
12.3.2 Future Challenges 242
12.4 Grouping the Logistics Challenges for Analysis and Development 243
12.4.1 Group A – No Change to Logistics Support 243
12.4.2 Group B – Unmanned Systems Replacing Manned Systems and Their Logistics Support Frameworks 244
12.4.3 Group C – Major Changes to Unmanned Systems Logistics 247
12.5 Further Considerations 248
12.6 Conclusions 251
References 251
13 Organizing for Improved Effectiveness in Networked Operations 255
13.1 Introduction 255
13.2 Understanding the IACM 256
13.3 An Agent‐Based Simulation Representation of the IACM 259
13.4 Structure of the Experiment 260
13.5 Initial Experiment 264
13.6 Expanding the Experiment 265
13.7 Conclusion 269
Disclaimer 270
References 270
14 An Exploration of Performance Distributions in Collectives 271
14.1 Introduction 271
14.2 Who Shoots How Many? 272
14.3 Baseball Plays as Individual and Networked Performance 273
14.4 Analytical Questions 275
14.5 Imparity Statistics in Major League Baseball Data 277
14.5.1 Individual Performance in Major League Baseball 278
14.5.2 Interconnected Performance in Major League Baseball 281
14.6 Conclusions 285
Acknowledgments 286
References 286
15 Distributed Combat Power: The Application of Salvo Theory to Unmanned Systems 287
15.1 Introduction 287
15.2 Salvo Theory 288
15.2.1 The Salvo Equations 288
15.2.2 Interpreting Damage 289
15.3 Salvo Warfare with Unmanned Systems 290
15.4 The Salvo Exchange Set and Combat Entropy 291
15.5 Tactical Considerations 292
15.6 Conclusion 293
References 294
Index 295
