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Autonomous Systems Developmentat
AFRLJune 25, 2002
Paul ZetochaGroup Lead, Intelligent Satellite Systems
AFRL/VS(505) 853-4114
Paul Paul ZetochaZetochaGroup Lead, Intelligent Satellite SystemsGroup Lead, Intelligent Satellite Systems
AFRL/VSAFRL/VS(505) 853(505) 853--41144114
Paul.Paul.ZetochaZetocha@@kirtlandkirtland..afaf.mil.mil
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Distributed systems require autonomous decision-making among multiple satellitesDistributed systems require autonomous
decision-making among multiple satellites
Research in Smart SystemsResearch in Smart Systems
Cluster ManagementCluster Management• Distributed processing• Agent-based communication• Formation flying process control• Data fusion• Virtual satellite command
and control
• Distributed processing• Agent-based communication• Formation flying process control• Data fusion• Virtual satellite command
and control
Collision Avoidance– Goal directed
behavior
Collision Avoidance– Goal directed
behavior
Distributed Resource Allocation
– Market negotiation
Distributed Resource Allocation
– Market negotiation
Fault DetectionIsolation & Resolution– State-based, rule-based, case-based, and model-based reasoning
Fault DetectionIsolation & Resolution– State-based, rule-based, case-based, and model-based reasoning
Cluster geometry formation and maintenance
Cluster geometry formation and maintenance
– Flocking behavior
– Flocking behavior
CooperativeProcessing
CooperativeProcessing
– Genetic algorithms, neural networks, fuzzy logic
– Genetic algorithms, neural networks, fuzzy logic
Message Center
Spacecraft
Spacecraft
Other SatellitesOther Satellites
Message CenterMessage Center
Ground
Agent
Message Center
Agent
AgentAgent Agent
Satellite Autonomy and Fault Detection & Recovery
Satellite Autonomy and Fault Detection & Recovery
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Cluster Manager Objectives
Formation Flying Process Control
• Relative positioning to cm level• Configurations from 100m to 5 Km• Collision avoidance
On-Board PlanningCommand & Telemetry
On-Board Science Processing
• Virtual Satellite Control• Cluster level knowledge
maintained with SOH datapassed through ISL• Commanding to individual
satellites or to CM and then routed• Consolidated telemetry
• Change detection• Feature recognition• Trigger to performreconfiguration• Autonomous data recollect• Optimization of science return
• Intelligent SV replanning dueto mission events • Real-timeresponse • Intelligentsensorqueuing
Fault Management• On-board knowledgebase• Limit Checking• Real-time reaction• CM Rollover• SV mode maintenance
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ASE Mission Scenario
Target Image with Autonomous Spacecraft
Constellation
Onboard Science Processing and Event Detection
Onboard ReplanningNew Science Images
Cluster Management: Constellation
Reconfiguration
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Information FusionInformation Fusion
The Key is Seamless FLOW to the DECISION MAKER
DATA INFORMATION KNOWLEDGE UNDERSTANDING
Terrain/Cultural FeaturesImagery Overlays
Logistics
IntelligenceWeather
Coalition Forces
Situation• Intel Sources•Air Surveillance
• Surface Surveillance• Space
Surveillance
FusionTechnology
Decision Maker
Decision-SpecificInformation
• Timely
• Consistent
• Structured
• Tailored
• High Quality
• Integrated
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How Supercomputing can Enable Autonomy
Help transition to an era where we can accept and trust satellite autonomy
• High fidelity simulations of the space environment to assist inthe development, test, and evaluation of autonomous software
• High fidelity simulations of anomaly scenarios
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How Supercomputing can Enable Autonomy
Enable Future Missions
•Model-based reasoning and planning systems that depict satellite components to extremely high levels of detail and thatcan run anomaly resolution scenarios extremely fast
• Virtual reality / 3-D representations of satellites that would allow an analyst to “step inside” and interact with individual components
• Conduct exhaustive searches of the possible results of operations such as switching to a redundant string
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