Autonomy Architecture for a Raven Class Telescope with Space
Situational Awareness Applications 3nd US Chinese Technical Interchange, Beijing
May 15-17, 2013
Ryan D. Coder, Graduate Research Assistant Marcus J. Holzinger, Assistant Professor
School of Aerospace Engineering, Georgia Institute of Technology
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Space Situational Awareness
• SSA is [Joint Publication 3-14]
– SSA involves characterizing, as completely as necessary, Resident Space Objects (RSOs)
• Needs are articulated by
– National Space Policy (2010)
– DoD National Security Space Policy (2011)
• Helps to ensure [Joint Publication 3-14]
– Space flight safety
– Protecting economic interests
– Protecting space capabilities
– Protecting military operations and national interests
– Implementing international treaties and agreements
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Why SSA is Hard
• Data deprived [Sabol et al. 2002 & Nielsen et al. 2012]
• SSN sensors not centrally controlled [Nielsen et al. 2012]
• Increased # of data product customers [Nielsen et al. 2012]
• Air Force analyst staffing issues [Weeden 2012]
N A T I O N A L S E C U R I T Y S P A C E S T R A T E G Y U N C L A S S I F I E D S U M M A R Y
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“The now-ubiquitous and interconnected nature of space capabilities
and the world’s growing dependence on them mean that irresponsible
acts in space can have damaging consequences for all of us.”
- 2010 National Space Policy
Space is vital to U.S. national security and our ability to understand emerging threats,
project power globally, conduct operations, support diplomatic efforts, and enable global
economic viability. As more nations and non-state actors recognize these benefits and
seek their own space or counterspace capabilities, we are faced with new opportunities
and new challenges in the space domain.
The current and future strategic environment is driven by three trends – space is
becoming increasingly congested, contested, and competitive.
Space is increasingly congested. Growing global space activity and testing of China’s
destructive anti-satellite (ASAT) system have increased congestion in important areas in
space. DoD tracks approximately 22,000 man-made objects in orbit, of which 1,100 are
active satellites (see Figure 1). There may be as many as hundreds of thousands of
additional pieces of debris that are too small to track with current sensors. Yet these
smaller pieces of debris can damage satellites in orbit.
THE STRATEGIC ENVIRONMENT
Figure 1. Source: Joint Space Operations Center
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5000
10000
15000
20000
25000
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Nu
mb
er o
f O
bje
cts
Total
Debris
Uncataloged*
Payloads
Rocket bodies
2000
Total: 9,600
1980
Total: 4,600
2010
Total: 22,000
* Uncataloged= unknown object and/or unknown origin
1990
Total: 6,900
1970
Total: 1,800
Iridium-COSMOS Collision
COSMOS 2421 Breakup
Chinese ASAT Test
Shemya Radar to full-power ops
Satellite Catalog Growth
Source: JSpOC, DoD 2011 National Security Space Policy
Category of Sensor
Near Earth (NE)
Deep Space (DS)
Dedicated ~ 25 % ~ 90 %
Collateral ~ 70 % ~ 5 %
Contributing ~ 5 % ~ 5 %
AF Space Surveillance System (AFSSS)
Eglin
Diego Garcia Ground-Based Electro-optical Deep Space Surveillance (GEODSS)
Moron Optical Surveillance System (MOSS)
Globus II
Socorro GEODSS
Maui GEODSS
Maui Space Surveillance System (MSSS)
Cobra Dane
Reagan Test Site (RTS)
Millstone/ Haystack/Auxiliary
Ascension
Thule
Clear
Cavalier
Beale Cape Cod
Fylingdales
UNCLASSIFIED
UNCLASSIFIED
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Need for Autonomy in SSA
Dull, repetitive tasks:
• Modern systems make hundreds of observations nightly [Sabol et al. 2002]
• Developing observation schedule complex
Fast timescales:
• Objects cross telescope field of view in seconds [Shell 2010]
• Dynamic local environment motivates near real time local schedule repair (e.g., weather)
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Overview
• Motivation
• Telescopes
• Autonomy
• Proposed Architecture
• Example
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Raven-class Telescope Overview
• Started as AFRL R&D effort
• Combination of COTS hardware and software
• Many Ravens currently in operation
• 1 Raven at Maui Space Surveillance Site contributes to SSN (Sabol et al. 2002)
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Other Autonomous Telescopes
LANL RAPTOR [Verstrand et al. 2008] NASA MCAT [Mulrooney et al. 2010]
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Overview
• Motivation
• Telescopes
• Autonomy
• Proposed Architecture
• Example
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• Two common cognition models – Observe, Orient, Decide, Act (OODA) loops developed by Col. John Boyd
[Boyd 1976]
– NASA Goddard developed Plan, Perceive, Act (PPA) loop [Truszkowski et al. 2009]
Cognition Models
OODA PPA 10
Control Loop as a Cognition Model
Reference Generation
Controller Actuator
Processing
Sensor Processing
Filter / Estimator
Decide Orient
Observe
Act
Real World
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Autonomy Architectures
Intelligent Machine Design
Levels Autonomy Architectures
NASA IMD [Truszkowski et al.
2009]
3 Layer [Alami et al. 1998]
DARPA/ISO SARA [Lewandowski
et al. 2001]
JPL CLARAty [Estlin et al. 2001]
Reflection Planning Mission Decision
Routine Executive Hardware -
Reaction Functional Cyber Functional
Reflective Agents have the ability to learn Routine Agents have the ability to evaluate & plan Reactive Agents interface with hardware (e.g., control loops)
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Machine Learning Background
Categorized by type of feedback available [Russell and Norvig 2009]:
• Supervised
– Learns function to map input-output pairs
• Reinforcement
– Agent rewarded or punished for actions taken
• Unsupervised
– No explicit feedback provided
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Constraint Satisfaction Problems
Cast dynamic scheduling problem as CSP [Russell and Norvig 2009]:
• Solved using general purpose heuristics
• Partial sets that violate constraints removed
• Utility function used to select best alternative
Used extensively in space applications:
• Hubble [Johnston 1990]
• Chandra [Brissenden 2001]
• Spitzer [Tyler et al. 2008]
• EO-1 [Sherwood et al. 2007]
– Identify opportunistic science
– Prioritize data downlinks
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Overview
• Motivation
• Telescopes
• Autonomy
• Proposed Architecture
• Example
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Distributed Sensor Networks
Centralized Decentralized
Centralized Mission Planner
Sensor Sensor
Static Task Generation
Dynamic Scheduler
Dynamic Scheduler
Current Space Surveillance Network [Hill et al., 2010]
• Uses knowledge of covariance • Limited sensor knowledge
• Limited covariance knowledge • Excellent sensor knowledge
Distributed Mission Planner
Distributed Mission Planner
Distributed Mission Planner
Robustness & Complexity
Centralized superior when minimizing overall catalog covariance [Hobson et al., 2011]
Decentralized superior to current SSN [Jayaweera et al., 2011]
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Proposed Autonomy Architecture
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Agent
Raven Class Telescope
Central Planning
Agent
… Other networked sensors
Increasing Machine
Intelligence
Reflection
Routine
Reaction
Commanded Objectives
Agent Agent
Agent Agent Agent
Agent Agent Agent
Space Object Catalog
Overview
• Motivation
• Telescopes
• Autonomy
• Proposed Architecture
• Example
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Space Object Detection
• To detect a Space Object, need an SNR ~ 6
• Biggest factor without a model: Atmospheric
Transmittance! • Goal: Autonomously estimate transmittance for local
azimuth and elevation over short time periods to enable local schedule repair / improvement
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Atmospheric transmittance
AllSky340 640x480 KAI-340 CCD F/1.4 Fujinon fisheye lens
SQM-LU-DL HWHM: 10deg
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Desired Information
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High Transmittance (SNR)
Low Transmittance (SNR)
Learning with Response Surface Methodology
Challenges: • Lack of first-principles model for local micro-climate
• Computational effort
Approach: • Physics-based response surface equations [Kirby 2001]
• Catalog star observations selected intelligently using DoE [Box and Draper 1987]
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Example Autonomy Architecture
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CSP Agent
Functional Agent
RSM Agent
Raven Class Telescope
Central Planning
Agent
… Other networked sensors
Increasing Machine
Intelligence Reflection
Routine
Reaction
Commanded Plan
Space Object Catalog
Raven Example Continued
All-Sky Camera Brightness Sensor
Autonomous DoE to observe impactful catalog stars
Empirical Function Fit (RSM)
Empirical Probability of Detection
SO of Interest
Can then use pdetect as an input to a CSP scheduler
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THANK YOU
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