Aeronautics & Astronautics Autonomous Flight Systems Laboratory All slides and material copyright of University of Washington Autonomous Flight Systems Laboratory
Aeronautics & AstronauticsAutonomous Flight Systems Laboratory
All slides and material copyright of University of Washington Autonomous
Flight Systems Laboratory
Aeronautics & AstronauticsAutonomous Flight Systems Laboratory
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
Aeronautics & Astronautics
Autonomous Flight Systems Laboratory
University of Washington 3
General Information
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
To conduct research that advances guidance, navigation, and control technology relevant to Autonomous Vehicles.
Mission Statement
Dr. Rolf RysdykDr. Juris VagnersDr. Uy-Loi LyDr. Kristi MorgansenDr. Anawat Pongpunwattana
Christopher LumCraig HusbyJohn OsborneRichard WiseElizabeth Bykoff
PeopleBen TriplettDan KleinJim Colito
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Hierarchy of Autonomy
Path PlanningTask AllocationSearch PatternsHuman Mission Command
Strategic (low bandwidth)
Tactical (medium bandwidth)
State StabilizationSignal TrackingInner Loop or “autopilot”Configuration changes
Dynamics and Control (high bandwidth)
Target ObservationPath FollowingCommunication & CooperationHuman Monitor Interaction
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Topography of Autonomous Flight
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Hardware-in-the-Loop Simulator
Avionics Tray
HiL Simulator
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Hardware-in-the-Loop Simulator
Groundstation Aircraft
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Distributed Real Time Simulator
Five computers running REAL TIME simulation software.
Used as a high fidelity testing environment to accurately simulate data transfer and communication aspects.
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Infrastructure of Flight Tests
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
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Aspects of Autonomy
Base
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Aspects of Autonomy
Base
STRATEGIC Team Assembly Task AssignmentTACTICAL Pattern HoldDYNAMICS & CONTROL Auto Launch/Retrieval
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Aspects of Autonomy
Base
Pattern hold/Team assembly
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Aspects of Autonomy
Base
TransitionPattern hold/Team assembly
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Aspects of Autonomy
Base
TransitionPattern hold/Team assembly
STRATEGIC Path Planning Adaptive Task Assignment
TACTICAL Obstacle/Threat Avoidance Path Following
DYNAMICS & CONTROL State Stabilization
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Aspects of Autonomy
Base
Transition
Obstacle/Threat Avoidance
Pattern hold/Team assembly
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Aspects of Autonomy
Base
Transition
Obstacle/Threat Avoidance
Pattern hold/Team assembly
STRATEGIC Dynamic Task Allocation Team-Based Cooperation Path Re- planningTACTICAL Obstacle Avoidance Engagement ManeuversDYNAMICS & CONTROL State stabilization
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Aspects of Autonomy
Base
Transition
Obstacle/Threat Avoidance
Pattern hold/Team assembly
Coordination w/ surface vehicles
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Aspects of Autonomy
Base
Transition
Obstacle avoidance
Coordination w/ surface vehicles
Pattern hold/Team assembly
STRATEGIC Provide improved target tasking
and routing info to unmanned surface vehicles
TACTICAL Orbit Coordination Communication Path FollowingDYNAMICS & CONTROL Signal Tracking
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Aspects of Autonomy
Base
Transition
Obstacle/Threat Avoidance
Coordination w/ surface vehicles
Pattern hold/Team assembly
Searching/Target ID
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Aspects of Autonomy
Base
Transition
Obstacle avoidance
Coordination w/ ground vehicles
Pattern hold/Team assembly
Searching/Target ID
STRATEGIC Map-Based and Probabilistic Searches
TACTICAL Path following
DYNAMICS & CONTROL State stabilization
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Aspects of Autonomy
Base
Transition
Obstacle/Threat Avoidance
Searching/Target IDCoordination w/ surface vehicles
Pattern hold/Team assembly
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Current Research Projects
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.
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Real Time Strategic Mission Planning
Base
Transition
Obstacle/Threat Avoidance
Searching/Target IDCoordination w/ surface vehicles
Pattern hold/Team assembly
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System Overview
Previously funded by DARPA & AFOSR
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System Block Diagram
Solving optimal control problems in real-time
planstaskD
pathsQ
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Stochastic Problem Formulation
Predicted probability of survival of each vehicle at time tq+1
Predicted probability that a task is not completed at time tq+1
Team utility function
ON
j
Oj
Oj
vj
Vv
Vv qqBqq
1
)()1(1)()1(
V TN
v
N
j
vij
Vv
Vv
iv
Fi
Fi dqqBqxqx
1 1
)()1(1)()1(
Mission Score CostJ
Vv
Fix
1
1 1 11
( ) ( ) 1 ( 1) ( ) ( ) ( ) ( ( ))V VT T
p
N NN NNF F i V V v V V V Qi i v v v ij v v p v v v p
i q s j vv
J q x q B q q d s N F Q s
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Distributed Architecture for Coordination of Autonomous Vehicles
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
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Evolution-based Cooperative Planning System (ECoPS)
Uses Evolutionary Computation-based 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).
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Evolutionary Computation (EC)
Motivated by evolution process found in nature
Population-based stochastic optimization technique
Metaphor Mapping
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Features of Evolution-Based Computation
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
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Market-based Planning for Coordinating Team Tasks
)(,),(),()( 21 nnnn vNTTTA
)(max AAJTask allocation problem:
At trading round n
)()()()1( nSnBnn iiii TT
At the end of the trading round:
The goal of task trading:
))(())1(( nJnJ AA
Each vehicle proposes ( ), ( )i iB n S n
which are approved by the auctioneer
based on bid price.
Distributed Task Planning Algorithm
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Dynamic Path Planning
Generate feasible paths and planned actions within a specified time limit (ΔTs ) while the vehicles are in motion.
Highly dynamic environment requires a high bandwidth planning system (i.e. small ΔTs).
Formulate the problem as a Model-based Predictive Control (MPC) problem
1
pp sss ttT
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EC-Based Path Planning
MutationDynamic Planning
Path Encoding
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Collision Avoidance
Model each site in the environment as a uncertainty circular area with radius
Probability of intersection: use numerical approximation computationally easier than true solution
, ( ), ( )v v Vi i v i i
k
B z k C k v k t
i
vi
: possible intersection region
: probability density field function
: position on the path
Ci : expected site location
v : velocity of the vehicle
viZ
Vvz
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Collision Avoidance Example
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Simulation Results
Simulation on the Boeing Open Experimental Platform
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Some Aspects of ECoPS
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.
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Coordination of Heterogeneous Vehicles
Base
Transition
Obstacle/Threat Avoidance
Searching/Target IDCoordination w/ surface vehicles
Pattern hold/Team assembly
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Coordination and Communication with Autonomous Surface Vehicles
At strategic level, UAVs can provide improved target tasking and routing information to surface vehicles
Autonomous path planning for surface vehicles through non-structured 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
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Goals and Advantages
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
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Simulation Visualization
Autonomous Orbit Coordination for Multiple UAVs
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Simulation Results
Effects of Radius and Airspeed Manipulation
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Simulation Results
Effects of Radius and Airspeed Manipulation
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Orbit Coordination
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
Insitu SeaScan tracking moving target
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Autonomous Search and Target Identification
Base
Transition
Obstacle/Threat Avoidance
Searching/Target IDCoordination w/ surface vehicles
Pattern hold/Team assembly
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Probabilistic Searching
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
Obtaining local magnetic map
Data from Fugro Airborne Surveys
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General Architecture
Groundstation
Agent 1
Agent 2
Local Magnetic Map Occupancy Map
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Occupancy-Based Map Search
False Anomalies
Target
Agents
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Occupancy-Based Map Search
Score Cell
Evaluate possible control population
Execute control
Basic Algorithm
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Occupancy-Based Map Search
Score Cell
Evaluate possible control population
Execute control
Basic Algorithm
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Occupancy-Based Map Search
Score Cell
Evaluate possible control population
Execute control
Basic Algorithm
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Occupancy-Based Map Search
Score Cell
Evaluate possible control population
Execute control
Basic Algorithm
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University of Washington 54
Occupancy-Based Map Search
Score Cell
Evaluate possible control population
Execute control
Basic Algorithm
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Occupancy-Based Map Search
Score Cell
Evaluate possible control population
Execute control
Basic Algorithm
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Occupancy-Based Map Search
Score Cell
Evaluate possible control population
Execute control
Basic Algorithm
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Occupancy-Based Map Search
Score Cell
Evaluate possible control population
Execute control
Basic Algorithm
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Occupancy-Based Map Search
Score Cell
Evaluate possible control population
Execute control
Basic Algorithm
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University of Washington 59
Occupancy-Based Map Search
Score Cell
Evaluate possible control population
Execute control
Basic Algorithm
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University of Washington 60
Occupancy-Based Map Search
Score Cell
Evaluate possible control population
Execute control
Basic Algorithm
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University of Washington 61
Occupancy-Based Map Search
Score Cell
Evaluate possible control population
Execute control
Basic Algorithm
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Anomaly Encounter
Aeromagnetic Data from Fugro Airborne Corresponding Line Data
Goal: Classify anomaly as target or false signature
Anomaly
How to score each cell?
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Particle Filter
How consistent is trace with trajectory over desired target?
Classify using Particle Filter
Nonparametric Bayes filter. Similar to Unscented Kalman or discrete Bayes filter.
Which trajectory (if any) would produce trace?
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Particle Filter
Fox, D., Thrun, S., Burgard, W. 2005, “Probabilistic Robotics”
tx 1,| tttmotion xuxfSample from
ttsensort xzfmw |
for m=1:M
tt xm :,
end
),,(ilterparticle_f function 1 tttt zu
t sampled from t w/probability α tw
Klein, D.J., Klink, J.O., 2005, “Mobile Robot Localization”
tmxtx 2
tx1
t
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True Anomaly Encounter
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Different Magnetic Signatures
What about for false anomalies?
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Confidence Comparison
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
InvestigatorsDr. Rolf Rysdyk [email protected]. Uy-Loi Ly [email protected]. Juris Vagners [email protected]. Kristi Morgansen [email protected]. Anawat Pongpunwattana [email protected]
Autonomous Flight Systems LaboratoryGuggenheim 109(206) 543-7748http://www.aa.washington.edu/research/afsl
Nonlinear Dynamics and Control LaboratoryAERB 120(206) 685-1530http://vger.aa.washington.edu