Part I Background |
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1.1 Direction of New Space Missions |
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1.1.1 New Millennium Program's Space Technology 5 |
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1.1.2 Solar Terrestrial Relations Observatory |
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1.1.3 Magnetospheric Multiscale |
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1.1.4 Tracking and Data Relay Satellites |
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1.2 Automation vs. Autonomy vs. Autonomic Systems |
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1.2.1 Autonomy vs. Automation |
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1.2.2 Autonomicity vs. Autonomy |
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1.3 Using Autonomy to Reduce the Cost of Missions |
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1.3.1 Multispacecraft Missions |
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1.3.2 Communications Delays |
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1.3.3 Interaction of Spacecraft |
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1.3.4 Adjustable and Mixed Autonomy |
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1.4.3 Immobots or Immobile Robots |
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2 Overview of Flight and Ground Software |
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2.1 Ground System Software |
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2.1.1 Planning and Scheduling |
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2.1.3 Science Schedule Execution |
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2.1.4 Science Support Activity Execution |
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2.1.5 Onboard Engineering Support Activities |
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2.1.6 Downlinked Data Capture |
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2.1.7 Performance Monitoring |
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2.1.10 Downlinked Data Archiving |
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2.1.11 Engineering Data Analysis/Calibration |
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2.1.12 Science Data Processing/Calibration |
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2.2.1 Attitude Determination and Control, Sensor Calibration, Orbit Determination, Propulsion |
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2.2.2 Executive and Task Management, Time Management, Command Processing, Engineering and Science Data Storage and Handling, Communications |
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2.2.3 Electrical Power Management, Thermal Management, SI Commanding, SI Data Processing |
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2.2.4 Data Monitoring, Fault Detection and Correction |
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2.3 Flight vs. Ground Implementation |
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3 Flight Autonomy Evolution |
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3.1 Reasons for Flight Autonomy |
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3.1.1 Satisfying Mission Objectives |
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3.1.2 Satisfying Spacecraft Infrastructure Needs |
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3.1.3 Satisfying Operations Staff Needs |
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3.2 Brief History of Existing Flight Autonomy Capabilities |
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3.2.1 1970's and Prior Spacecraft |
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3.2.5 Flight Autonomy Capabilities of the Future |
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3.3 Current Levels of Flight Automation/Autonomy |
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4 Ground Autonomy Evolution |
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4.1 Agent-Based Flight Operations Associate |
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4.1.1 A Basic Agent Model in AFLOAT |
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4.1.2 Implementation Architecture for AFLOAT Prototype |
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4.1.3 The Human Computer Interface in AFLOAT |
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4.1.4 Inter-Agent Communications in AFLOAT |
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4.2 Lights Out Ground Operations System |
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4.2.1 The LOGOS Architecture |
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4.2.2 An Example Scenario |
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4.3 Agent Concept Testbed |
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4.3.1 Overview of the ACT Agent Architecture |
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4.3.2 Architecture Components |
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4.3.3 Dataflow Between Components |
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4.3.4 ACT Operational Scenario |
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4.3.5 Verification and Correctness |
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Part II Technology |
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5 Core Thchnologies for Developing Autonomous and Autonomic Systems |
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5.1.4 Model-Based Planners |
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5.1.5 Case-Based Planners |
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5.2 Collaborative Languages |
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5.3 Reasoning with Partial Information |
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5.4 Learning Technologies |
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5.4.1 Artificial Neural Networks |
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5.4.2 Genetic Algorithms and Programming |
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5.6 Perception Technologies |
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5.6.2 Image and Signal Processing |
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5.7.1 Software Simulation Environments |
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5.7.2 Simulation Libraries |
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5.7.4 Networked Simulation Environments |
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6 Agent-Based Spacecraft Autonomy Design Concepts |
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6.1 High Level Design Features |
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6.1.2 Inertial Fixed Pointing |
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6.1.3 Ground Commanded Slewing |
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6.1.4 Ground Commanded Thruster Firing |
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6.1.5 Electrical Power Management |
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6.1.7 Health and Safety Communications |
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6.1.8 Basic Fault Detection and Correction |
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6.1.9 Diagnostic Science Instrument Commanding |
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6.1.10 Engineering Data Storage |
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6.2 Remote Agent Functionality |
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6.2.1 Fine Attitude Determination |
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6.2.2 Attitude Sensor/Actuator and Science Instrument Calibration |
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6.2.5 Data Monitoring and Trending |
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6.2.6 "Smart" Fault Detection, Diagnosis, Isolation, and Correction |
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6.2.7 Look-Ahead Modeling |
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6.2.8 Target Planning and Scheduling |
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6.2.9 Science Instrument Commanding and Configuration |
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6.2.10 Science Instrument Data Storage and Communications |
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6.2.11 Science Instrument Data Processing |
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6.3 Spacecraft Enabling Technologies |
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6.3.1 Modern CCD Star Trackers |
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6.3.2 Onboard Orbit Determination |
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6.3.3 Advanced Flight Processor |
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6.3.4 Cheap Onboard Mass Storage Devices |
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6.3.5 Advanced Operating System |
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6.3.6 Decoupling of Scheduling from Communications |
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6.3.7 Onboard Data Trending and Analysis |
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6.3.8 Efficient Algorithms for Look-Ahead Modeling |
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6.4 AI Enabling Methodologies |
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6.4.1 Operations Enabled by Remote Agent Design |
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6.4.2 Dynamic Schedule Adjustment Driven by Calibration Status |
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6.4.3 Target of Opportunity Scheduling Driven by Realtime Science Observations |
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6.4.4 Goal-Driven Target Scheduling |
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6.4.5 Opportunistic Science and Calibration Scheduling |
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6.4.6 Scheduling Goals Adjustment Driven by Anomaly Response |
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6.4.7 Adaptable Scheduling Goals and Procedures |
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6.4.8 Science Instrument Direction of Spacecraft Operation |
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6.4.9 Beacon Mode Communication |
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6.4.10 Resource Management |
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6.5 Advantages of Remote Agent Design |
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6.5.1 Efficiency Improvement |
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6.5.2 Reduced FSW Development Costs |
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6.6 Mission Types for Remote Agents |
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6.6.1 LEO Celestial Pointers |
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6.6.2 GEO Celestial Pointers |
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6.6.5 Lagrange Point Celestial Pointers |
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6.6.6 Deep Space Missions |
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6.6.7 Spacecraft Constellations |
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6.6.8 Spacecraft as Agents |
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7.1 Need for Cooperative Autonomy in Space Missions |
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7.1.1 Quantities of Science Data |
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7.1.2 Complexity of Scientific Instruments |
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7.1.3 Increased Number of Spacecraft |
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7.2 General Model of Cooperative Autonomy |
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7.2.3 Cooperative Actions |
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7.3 Spacecraft Mission Management |
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7.3.5 Science Data Processing |
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7.4 Spacecraft Mission Viewed as Cooperative Autonomy |
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7.4.1 Expanded Spacecraft Mission Model |
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7.4.2 Analysis of Spacecraft Mission Model |
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7.4.3 Improvements to Spacecraft Mission Execution |
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7.5 An Example of Cooperative Autonomy: Virtual Platform |
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7.5.1 Virtual Platforms Under Current Environment |
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7.5.2 Virtual Platforms with Advanced Automation |
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7.6 Examples of Cooperative Autonomy |
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7.6.1 The Mobile Robot Laboratory at Georgia Tech |
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7.6.2 Cooperative Distributed Problem Solving Research Group at the University of Maine |
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7.6.3 Knowledge Sharing Effort |
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8.1 Overview of Autonomic Systems |
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8.1.1 What are Autonomic Systems? |
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8.1.2 Autonomic Properties |
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8.1.3 Necessary Constructs |
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8.1.4 Evolution vs. Revolution |
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8.2 State of the Art Research |
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8.2.2 Prediction and Optimization |
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8.2.3 Knowledge Capture and Representation |
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8.2.4 Monitoring and Root-Cause Analysis |
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8.2.5 Legacy Systems and Autonomic Environments |
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8.2.7 Agents for Autonomic Systems |
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8.2.8 Policy-Based Management |
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8.2.9 Related Initiatives |
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8.3 Research and Technology Transfer Issues |
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Part III Applications |
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9 Autonomy in Spacecraft Constellations |
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9.2 Constellations Overview |
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9.3 Advantages of Constellations |
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9.3.2 Coordinated Science |
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9.4 Applying Autonomy and Autonomicity to Constellations |
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9.4.1 Ground-Based Constellation Autonomy |
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9.4.2 Space-Based Autonomy for Constellations |
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9.4.3 Autonomicity in Constellations |
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9.5 Intelligent Agents in Space Constellations |
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9.5.1 Levels of Intelligence in Spacecraft Agents |
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9.5.2 Multiagent-Based Organizations for Satellites |
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9.6.2 Ground-Based Autonomy |
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9.6.3 Space-Based Autonomy |
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10 Swarms in Space Missions |
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10.1 Introduction to Swarms |
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10.2 Swarm Technologies at NASA |
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10.2.2 NASA Prospecting Asteroid Mission |
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10.2.3 Other Space Swarm-Based Concepts |
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10.3 Other Applications of Swarms |
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10.4 Autonomicity in Swarm Missions |
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10.5 Software Development of Swarms |
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10.5.1 Programming Techniques and Tools |
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10.6 Future Swarm Concepts |
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11.1 Factors Driving the Use of Autonomy and Autonomicity |
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11.2 Reliability of Autonomous and Autonomic Systems |
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11.4 Autonomous and Autonomic Systems in Future NASA Missions |
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A Attitude and Orbit Determination and Control |
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B Operational Scenarios and Agent Interactions |
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B.1 Onboard Remote Agent Interaction Scenario |
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B.2 Space-to-Ground Dialog Scenario |
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B.3 Ground-to-Space Dialog Scenario |
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B.4 Spacecraft Constellation Interactions Scenario |
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B.5 Agent-Based Satellite Constellation Control Scenario |
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C Acronyms |
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D Glossary |
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References |
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Index |
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