Autonomous Flight Systems
Laboratory
Aeronautics & Astronautics
All slides and material copyright of
University of Washington Autonomous
Flight Systems Laboratory
Autonomous Flight Systems
Laboratory
Aeronautics & Astronautics
Research and Development at the
Autonomous Flight Systems Laboratory
University of Washington
Seattle, WA
Guggenheim 109, AERB 214
(206) 543-7748
http://www.aa.washington.edu/research/afsl
Autonomous Search and Target
Identification
Autonomous Flight Systems Laboratory
Aeronautics & Astronautics
Pattern hold/Team assembly
Transition
Obstacle/Threat
Avoidance
Base
Searching/Target ID
Coordination w/ surface vehicles
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Probabilistic Searching
Autonomous Flight Systems Laboratory
Aeronautics & Astronautics
Evaluation of Autonomous Airborne Geomagnetic Surveying

Utilize magnetometer to
measure local magnetic
anomalies for known
signature

Identify and classify
anomalies

Search for and track
anomalies cooperatively
Currently funded under WTC Phase II
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General Architecture
Autonomous Flight Systems Laboratory
Aeronautics & Astronautics
Obtaining local magnetic map
Data from Fugro Airborne Surveys
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General Architecture
Autonomous Flight Systems Laboratory
Aeronautics & Astronautics
Agent 1
Agent 2
Local Magnetic Map
Occupancy Map
Groundstation
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Occupancy-Based Map Search
Autonomous Flight Systems Laboratory
Aeronautics & Astronautics
Agents
Target
False
Anomalies
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Occupancy-Based Map Search
Autonomous Flight Systems Laboratory
Aeronautics & Astronautics
Basic Algorithm
Score Cell
Evaluate possible
control population
Execute control
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Occupancy-Based Map Search
Autonomous Flight Systems Laboratory
Aeronautics & Astronautics
Basic Algorithm
Score Cell
Evaluate possible
control population
Execute control
University of Washington
9
Occupancy-Based Map Search
Autonomous Flight Systems Laboratory
Aeronautics & Astronautics
Basic Algorithm
Score Cell
Evaluate possible
control population
Execute control
University of Washington
10
Occupancy-Based Map Search
Autonomous Flight Systems Laboratory
Aeronautics & Astronautics
Basic Algorithm
Score Cell
Evaluate possible
control population
Execute control
University of Washington
11
Occupancy-Based Map Search
Autonomous Flight Systems Laboratory
Aeronautics & Astronautics
Basic Algorithm
Score Cell
Evaluate possible
control population
Execute control
University of Washington
12
Occupancy-Based Map Search
Autonomous Flight Systems Laboratory
Aeronautics & Astronautics
Basic Algorithm
Score Cell
Evaluate possible
control population
Execute control
University of Washington
13
Occupancy-Based Map Search
Autonomous Flight Systems Laboratory
Aeronautics & Astronautics
Basic Algorithm
Score Cell
Evaluate possible
control population
Execute control
University of Washington
14
Occupancy-Based Map Search
Autonomous Flight Systems Laboratory
Aeronautics & Astronautics
Basic Algorithm
Score Cell
Evaluate possible
control population
Execute control
University of Washington
15
Occupancy-Based Map Search
Autonomous Flight Systems Laboratory
Aeronautics & Astronautics
Basic Algorithm
Score Cell
Evaluate possible
control population
Execute control
University of Washington
16
Occupancy-Based Map Search
Autonomous Flight Systems Laboratory
Aeronautics & Astronautics
Basic Algorithm
Score Cell
Evaluate possible
control population
Execute control
University of Washington
17
Occupancy-Based Map Search
Autonomous Flight Systems Laboratory
Aeronautics & Astronautics
Basic Algorithm
Score Cell
Evaluate possible
control population
Execute control
University of Washington
18
Occupancy-Based Map Search
Autonomous Flight Systems Laboratory
Aeronautics & Astronautics
Basic Algorithm
Score Cell
Evaluate possible
control population
Execute control
University of Washington
19
Anomaly Encounter
Autonomous Flight Systems Laboratory
Aeronautics & Astronautics
How to score each cell?
Anomaly
Aeromagnetic Data from Fugro Airborne
Corresponding Line Data
Goal: Classify anomaly as target or false signature
University of Washington
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Particle Filter
Autonomous Flight Systems Laboratory
Aeronautics & Astronautics
How consistent is trace with trajectory over desired target?
Which trajectory
(if any) would
produce trace?
Classify using Particle Filter
Nonparametric Bayes filter. Similar to Unscented Kalman
or discrete Bayes filter.
University of Washington
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Particle Filter
Autonomous Flight Systems Laboratory
Aeronautics & Astronautics
Fox, D., Thrun, S., Burgard, W. 2005, “Probabilistic Robotics”
Klein, D.J., Klink, J.O., 2005, “Mobile Robot Localization”
function t  particle_filter( t 1, ut , zt )
for m=1:M
Sample xt from f motion xt | ut , xt 1 
wt m  f sensor zt | xt 
t :, m  xt
x 2t
x 1t
x mt
t
end
t sampled from t w/probability α wt
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True Anomaly Encounter
Autonomous Flight Systems Laboratory
Aeronautics & Astronautics
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Different Magnetic Signatures
Autonomous Flight Systems Laboratory
Aeronautics & Astronautics
What about for false anomalies?
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Confidence Comparison
Autonomous Flight Systems Laboratory
Aeronautics & Astronautics
Actual Target Encounter
False Encounter
Features
Use combination of particle filter and neural net to identify
target and quantify confidence.
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Contact Us
Autonomous Flight Systems Laboratory
Aeronautics & Astronautics
Investigators
Dr. Rolf Rysdyk
Dr. Uy-Loi Ly
Dr. Juris Vagners
Dr. Kristi Morgansen
Dr. Anawat Pongpunwattana
rysdyk@aa.washington.edu
ly@aa.washington.edu
vagners@aa.washington.edu
morgansen@aa.washington.edu
anawatp@u.washington.edu
Autonomous Flight Systems Laboratory
Guggenheim 109
(206) 543-7748
http://www.aa.washington.edu/research/afsl
Nonlinear Dynamics and Control Laboratory
AERB 120
(206) 685-1530
http://vger.aa.washington.edu
University of Washington
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