Autonomous Flight Systems
Laboratory
Aeronautics & Astronautics

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

General Information
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
People
Dr. Rolf Rysdyk
Dr. Juris Vagners
Dr. Uy-Loi Ly
Dr. Kristi Morgansen
Dr. Anawat Pongpunwattana
Christopher Lum
Craig Husby
John Osborne
Richard Wise
Elizabeth Bykoff
Ben Triplett
Dan Klein
Jim Colito
Research Focus
• Multi-Vehicle Cooperative Control Flight Testing
• Cooperative Strategies for Teams of Autonomous Air & Surface Vehicles
• Probability Based Searching/Target Identification
• Coordinated Underwater Robotics
• Communications for Heterogeneous Cooperating Autonomous Vehicles
Mission Statement
To conduct research that advances guidance, navigation, and control technology relevant to Autonomous Vehicles.
University of Washington 3

Hierarchy of Autonomy
Autonomous Flight Systems Laboratory
Strategic (low bandwidth)
Path Planning
Task Allocation
Search Patterns
Human Mission Command
Tactical (medium bandwidth)
Target Observation
Path Following
Communication & Cooperation
Human Monitor Interaction
Aeronautics & Astronautics
Dynamics and Control (high bandwidth)
State Stabilization
Signal Tracking
Inner Loop or “autopilot”
Configuration changes
University of Washington 4

Topography of Autonomous Flight
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
University of Washington 5

Hardware-in-the-Loop Simulator
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
HiL Simulator
University of Washington
Avionics Tray
6

Hardware-in-the-Loop Simulator
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
Groundstation
University of Washington
Aircraft
7

Distributed Real Time Simulator
Autonomous Flight Systems Laboratory Aeronautics & Astronautics


Five computers running
REAL TIME simulation software.
Used as a high fidelity testing environment to accurately simulate data transfer and communication aspects.
University of Washington 8

Infrastructure of Flight Tests
Autonomous Flight Systems Laboratory




In addition to simulation, direct access to actual hardware and systems.
Partnered with the Insitu Group for ScanEagle
UAVs, Northwind Marine for SeaFox Boats.
Extensive test infrastructure in place by working with these local companies
Includes sea launch & retrieval of UAVs
Aeronautics & Astronautics
University of Washington 9

Aspects of Autonomy
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
Base
University of Washington 10

Aspects of Autonomy
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
Base
STRATEGIC


Team Assembly
Task Assignment
TACTICAL
 Pattern Hold
DYNAMICS & CONTROL
 Auto Launch/Retrieval
University of Washington 11

Aspects of Autonomy
Autonomous Flight Systems Laboratory
Pattern hold/Team assembly
Aeronautics & Astronautics
Base
University of Washington 12

Aspects of Autonomy
Autonomous Flight Systems Laboratory
Pattern hold/Team assembly Transition
Aeronautics & Astronautics
Base
University of Washington 13

Aspects of Autonomy
Autonomous Flight Systems Laboratory
Pattern hold/Team assembly Transition
Aeronautics & Astronautics
STRATEGIC
 Path Planning

TACTICAL
 Obstacle/Threat Avoidance
 Path Following
DYNAMICS & CONTROL
 State Stabilization
University of Washington 14

Aspects of Autonomy
Autonomous Flight Systems Laboratory
Pattern hold/Team assembly Transition
Aeronautics & Astronautics
Obstacle/Threat
Avoidance
Base
University of Washington 15

Aspects of Autonomy
Autonomous Flight Systems Laboratory
Pattern hold/Team assembly Transition
Aeronautics & Astronautics
Base
University of Washington
Obstacle/Threat
Avoidance
STRATEGIC


Dynamic Task Allocation
Team-Based Cooperation
 Path Re- planning
TACTICAL
 Obstacle Avoidance
 Engagement Maneuvers
DYNAMICS & CONTROL
 State stabilization
16

Aspects of Autonomy
Autonomous Flight Systems Laboratory
Pattern hold/Team assembly Transition
Aeronautics & Astronautics
Base
Coordination w/ surface vehicles
University of Washington
Obstacle/Threat
Avoidance
17

Aspects of Autonomy
Autonomous Flight Systems Laboratory
Pattern hold/Team assembly Transition
Aeronautics & Astronautics
Base
Obstacle avoidance
STRATEGIC
 Provide improved target tasking and routing info to unmanned surface vehicles
TACTICAL



Orbit Coordination
Communication
Path Following
DYNAMICS & CONTROL
 Signal Tracking
Coordination w/ surface vehicles
University of Washington 18

Aspects of Autonomy
Autonomous Flight Systems Laboratory
Pattern hold/Team assembly Transition
Aeronautics & Astronautics
Obstacle/Threat
Avoidance
Base
Searching/Target ID
Coordination w/ surface vehicles
University of Washington 19

Aspects of Autonomy
Autonomous Flight Systems Laboratory
Pattern hold/Team assembly Transition
Aeronautics & Astronautics
Obstacle avoidance
 Map-Based and Probabilistic Searches
TACTICAL
 Path following
DYNAMICS & CONTROL
 State stabilization
Coordination w/ ground vehicles
Searching/Target ID
University of Washington 20

Aspects of Autonomy
Autonomous Flight Systems Laboratory
Pattern hold/Team assembly Transition
Aeronautics & Astronautics
Obstacle/Threat
Avoidance
Base
Searching/Target ID
Coordination w/ surface vehicles
University of Washington 21

Current Research Projects
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
 Real Time Strategic Mission Planning
 dynamic task and path planning for a team of autonomous vehicles to cooperatively execute a set of assigned tasks.
 Coordination of Heterogeneous Vehicles
 developing robust navigation and guidance algorithms to coordinate multiple vehicles to perform a cooperative task.
 Autonomous Search and Target Identification
 using total magnetic intensity measurements to search and identify magnetic anomalies in a predetermined area.
University of Washington 22

Real Time Strategic Mission Planning
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
Pattern hold/Team assembly Transition
Obstacle/Threat
Avoidance
Base
Searching/Target ID
Coordination w/ surface vehicles
University of Washington 23

System Overview
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
University of Washington
Previously funded by DARPA & AFOSR
24

System Block Diagram
Autonomous Flight Systems Laboratory
Solving optimal control problems in real-time
Aeronautics & Astronautics
D
 task plans
Q
 paths
University of Washington 25

Stochastic Problem Formulation
Autonomous Flight Systems Laboratory



Aeronautics & Astronautics

V
Predicted probability of survival of each vehicle at time t q+1
 v
V
( q

1 )
  v
V
( q )
N
O 

1 j

1

B v j
( q

1 )
 j
O
( q )
 O j
 x i
F
Predicted probability that a task is not completed at time t q+1 x i
F ( q

1 )
 x i
F ( q )
N v
V 

1

 1

B v i ( q

1 )
 v
V ( q )

V v
N j
 T

1 d ij v


Team utility function
J

Mission Score

Cost
J

N
T
N

1   i

1
 p
 i
F
( ) i
F
( ) v
N
V 

1



1
 i
B q v

1)

V v
( )

V v
N
T j


1 d ij v




N
V v


1 v

V v
( s p
)
  v
V
N

 
Q
F Q s v
( ( v p
))
University of Washington 26

Distributed Architecture for
Coordination of Autonomous Vehicles
Autonomous Flight Systems Laboratory Aeronautics & Astronautics




Each vehicle plans its own path and makes task trading decisions to maximize the team utility function
There is one active coordinator agent at a time


 efficiency failure detection local/global information exchanges
Computational requirement for running coordinator agent is small compared to planning
Coordinator role can be transferred to another vehicle via a voting procedure
University of Washington 27

Evolution-based Cooperative Planning
System (ECoPS)
Autonomous Flight Systems Laboratory Aeronautics & Astronautics




Uses Evolutionary Computationbased techniques in the optimization of trading decision making and path planning
Task planner uses price and shared information in addition to predicted states of the world for making trading decisions
Task planner interacts with path planner and state predictor to simultaneously search feasible near-optimal task and path plans.
We call this system the “Evolution-
Based Collaborative Planning
System” – ECoPS, combining market based techniques with evolutionary computation (EC).
University of Washington 28

Evolutionary Computation (EC)
Aeronautics & Astronautics Autonomous Flight Systems Laboratory
 Motivated by evolution process found in nature
 Population-based stochastic optimization technique
Metaphor Mapping
University of Washington 29

Autonomous Flight Systems Laboratory Aeronautics & Astronautics
 Provides a feasible solution at any time
 Optimality is a bonus
 Dynamic replanning
 Non-linear performance function
 Collision avoidance
 Constraints on vehicle capabilities
 Handling loss of vehicles
 Operating in uncertain dynamic environments
 Timing constraints
University of Washington 30

Market-based Planning for
Coordinating Team Tasks
Autonomous Flight Systems Laboratory
Distributed Task Planning Algorithm
Aeronautics & Astronautics
Task allocation problem: max ( A
A
J )
At trading round n
A ( n )


T
1
( n ), T
2
( n ),  , T
N v ( n )

Each vehicle proposes ( ), i ( ) which are approved by the auctioneer based on bid price.
At the end of the trading round:
T i
( n

1 )

T i
( n )

B i
( n )

S i
( n )
The goal of task trading:
J ( A ( n

1 ))

J ( A ( n ))
University of Washington 31

Dynamic Path Planning
Autonomous Flight Systems Laboratory
 Generate feasible paths and planned actions within a specified time limit ( Δ Ts ) while the vehicles are in motion.

T s
 t s p
 t s p

1


Highly dynamic environment requires a high bandwidth planning system (i.e. small
Δ T s
).
Formulate the problem as a
Model-based Predictive
Control (MPC) problem
University of Washington
Aeronautics & Astronautics
32

EC-Based Path Planning
Autonomous Flight Systems Laboratory
Path Encoding
Aeronautics & Astronautics
Dynamic Planning
Mutation
University of Washington 33

Collision Avoidance
Autonomous Flight Systems Laboratory


Model each site in the environment as a
 i
Probability of intersection:
 use numerical approximation
 computationally easier than true solution
B i v   k
 i v
 z
V v
 
, C k i
 i
 v k
 t
Z i v
: possible intersection region
 i v
: probability density field function z v
V
: position on the path
C i
: expected site location v : velocity of the vehicle
University of Washington
Aeronautics & Astronautics
34

Collision Avoidance Example
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
University of Washington 35

Simulation Results
Autonomous Flight Systems Laboratory
Simulation on the Boeing Open Experimental Platform
Aeronautics & Astronautics
University of Washington 36

Some Aspects of ECoPS
Autonomous Flight Systems Laboratory






Aeronautics & Astronautics
Each vehicle computes its own trajectory and makes decision to trade its tasks with other vehicles.
Vehicles may sacrifice themselves if that benefits the team.
Each vehicle needs to have periodically updated locations of nearby vehicles only for collision avoidance.
Each vehicle needs to know the information about the environment. The accuracy of the information affects the quality of its decision making.
The rate of environment information updates should be selected based on how fast objects move in the environment.
Assuming vehicles are equipped with on-board sensors, sharing sensed data improves the performance of the team.
University of Washington 37

Coordination of Heterogeneous Vehicles
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
Pattern hold/Team assembly Transition
Obstacle/Threat
Avoidance
Base
Searching/Target ID
Coordination w/ surface vehicles
University of Washington 38

Coordination and Communication with
Autonomous Surface Vehicles
Autonomous Flight Systems Laboratory Aeronautics & Astronautics



At strategic level,
UAVs can provide improved target tasking and routing information to surface vehicles
Autonomous path planning for surface vehicles through nonstructured environments enhanced by UAV information
At tactical level, UAVs can track evasive targets and update world estimates
Currently funded under WTC Phase I Fall/Winter ‘05
University of Washington 39

Goals and Advantages
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
Goals
 Use multiple low-cost UAVs to cooperatively track targets
 Ability to mark targets, report to central database, report to deployed surface vehicles
 Improve quality and quantity of
ISR data and battlefield awareness
Advantages
 Tracking targets with tactical UAVs can require high operator workload
 Evasive targets could fool a single
UAV
University of Washington 40

Simulation Visualization
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
Autonomous Orbit Coordination for Multiple UAVs
University of Washington 41

Simulation Results
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
Effects of Radius and Airspeed Manipulation
University of Washington 42

Simulation Results
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
Effects of Radius and Airspeed Manipulation
University of Washington 43

Orbit Coordination
Autonomous Flight Systems Laboratory
 Maintains relative phase angle between two UAVs in presence of disturbance
 Nonlinear issues dealing with asymmetry of varying orbits
 Joint effort between
UW, Cornell, U of
Calgary, and The
Insitu Group
Aeronautics & Astronautics
Insitu SeaScan tracking moving target
University of Washington 44

Autonomous Search and Target
Identification
Autonomous Flight Systems Laboratory
Pattern hold/Team assembly Transition
Aeronautics & Astronautics
Obstacle/Threat
Avoidance
Base
Searching/Target ID
Coordination w/ surface vehicles
University of Washington 45

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
University of Washington 46

General Architecture
Autonomous Flight Systems Laboratory
Obtaining local magnetic map
Aeronautics & Astronautics
Data from Fugro Airborne Surveys
University of Washington 47

General Architecture
Autonomous Flight Systems Laboratory
Agent 1
Agent 2
Aeronautics & Astronautics
Groundstation
Local Magnetic Map
University of Washington
Occupancy Map
48

Occupancy-Based Map Search
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
Agents
Target
False
Anomalies
University of Washington 49

Occupancy-Based Map Search
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
Basic Algorithm
Score Cell
Evaluate possible control population
Execute control
University of Washington 50

Occupancy-Based Map Search
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
Basic Algorithm
Score Cell
Evaluate possible control population
Execute control
University of Washington 51

Occupancy-Based Map Search
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
Basic Algorithm
Score Cell
Evaluate possible control population
Execute control
University of Washington 52

Occupancy-Based Map Search
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
Basic Algorithm
Score Cell
Evaluate possible control population
Execute control
University of Washington 53

Occupancy-Based Map Search
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
Basic Algorithm
Score Cell
Evaluate possible control population
Execute control
University of Washington 54

Occupancy-Based Map Search
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
Basic Algorithm
Score Cell
Evaluate possible control population
Execute control
University of Washington 55

Occupancy-Based Map Search
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
Basic Algorithm
Score Cell
Evaluate possible control population
Execute control
University of Washington 56

Occupancy-Based Map Search
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
Basic Algorithm
Score Cell
Evaluate possible control population
Execute control
University of Washington 57

Occupancy-Based Map Search
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
Basic Algorithm
Score Cell
Evaluate possible control population
Execute control
University of Washington 58

Occupancy-Based Map Search
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
Basic Algorithm
Score Cell
Evaluate possible control population
Execute control
University of Washington 59

Occupancy-Based Map Search
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
Basic Algorithm
Score Cell
Evaluate possible control population
Execute control
University of Washington 60

Occupancy-Based Map Search
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
Basic Algorithm
Score Cell
Evaluate possible control population
Execute control
University of Washington 61

Anomaly Encounter
Autonomous Flight Systems Laboratory
How to score each cell?
Aeronautics & Astronautics
Anomaly
Aeromagnetic Data from Fugro Airborne
Corresponding Line Data
Goal: Classify anomaly as target or false signature
University of Washington 62

Particle Filter
Autonomous Flight Systems Laboratory
How consistent is trace with trajectory over desired target?
Aeronautics & Astronautics
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 63

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” x
2 t x
1 t
 t x m t function
 t
 particle_f ilter (
 t

1
, u t
, z t
) for m=1:M
Sample x t from f motion
 x t w t
 f sensor
 z t
| x t

 t
 
 x t
| u t
, x t

1
 end
 t sampled from
 t w/probability α w t
University of Washington 64

True Anomaly Encounter
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
University of Washington 65

Different Magnetic Signatures
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
What about for false anomalies?
University of Washington 66

Confidence Comparison
Autonomous Flight Systems Laboratory
Actual Target Encounter
Aeronautics & Astronautics
False Encounter
Features
Use combination of particle filter and neural net to identify target and quantify confidence.
University of Washington 67

Contact Us
Autonomous Flight Systems Laboratory Aeronautics & Astronautics
Investigators
Dr. Rolf Rysdyk
Dr. Uy-Loi Ly
Dr. Juris Vagners
Dr. Kristi Morgansen rysdyk@aa.washington.edu
ly@aa.washington.edu
vagners@aa.washington.edu
morgansen@aa.washington.edu
Dr. Anawat Pongpunwattana 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 68