1

Autonomous Systems Developmentat

AFRLJune 25, 2002

Paul ZetochaGroup Lead, Intelligent Satellite Systems

AFRL/VS(505) 853-4114

Paul.Zetocha@kirtland.af.mil

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

2

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

3

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

4

ASE Mission Scenario

Target Image with Autonomous Spacecraft

Constellation

Onboard Science Processing and Event Detection

Onboard ReplanningNew Science Images

Cluster Management: Constellation

Reconfiguration

5

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

6

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

7

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