Innovations in Engineering UNCLASSIFIED Copyright 2011 by the Charles Stark Draper Laboratory, Inc. all rights reserved Model Based Test Planning for Autonomous Systems Merging Technology Acquisition and T&E September 15, 2011 1 Team Members Troy Jones, George Sass, Melissa Durfee, Sean Buckley, Nick Borer, Stephen York, Eric Nelson, Mike Ricard, Glenn Ogrodnik, Scott Ingleton
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Model Based Test Planning for Autonomous Systems...Example Results: Position Response 18-400-300-200-100 0 100 200 300 0 500 1000 1500 2000 2500 0 20 40 60 80 100 120 Crosstrack (ft)
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Innovations in Engineering
UNCLASSIFIEDCopyright 2011 by the Charles Stark Draper Laboratory, Inc. all rights reserved
Model Based Test Planning for Autonomous
Systems
Merging Technology Acquisition and T&E
September 15, 2011
1
Team MembersTroy Jones, George Sass, Melissa Durfee, Sean Buckley, Nick Borer,
Stephen York, Eric Nelson, Mike Ricard, Glenn Ogrodnik, Scott Ingleton
UNCLASSIFIEDCopyright 2011 by the Charles Stark Draper Laboratory, Inc. all rights reserved
Implementation
Concept of
Operations
Architecture
Requirements
Design
Integration, Test
& Verification
System
Verification &
Validation
Operations &
Maintenance
Behavioral
Model
Project Vision
2
Research Markov Reliability Analysis and DOE for System-Level test planning
Complementary to Model-Based Engineering
Performance Based Evaluation
Inject failure conditions
Force off-nominal decisions
Continuous Model Refinement
Feedback performance data over time
Improve predictions of future reliability
Implementation
Concept of
Operations
Architecture
Requirements
DesignIntegration, Test
& Verification
System
Verification &
Validation
Operations &
Maintenance
UNCLASSIFIEDCopyright 2011 by the Charles Stark Draper Laboratory, Inc. all rights reserved
Lifecycle Autonomous System Test Design
3
0 10 20 30 40 50 60 70 80
propulsion Omission
Att Psi Omission
Att theta Omission
pressure Omission
fin7 Omission
fin5 Omission
fin6 Omission
Att r Omission
fin4 Omission
sonar fls Omission
sonar bathy Omission
Att P Omission
sonar rsls Omission
sonar lsls Omission
0 nominal
Att q Omission
Att Phi Omission
Position Error: CurrentMag
|T|
current only rank
Current Magnitude
Markov Reliability Sensitivity
Ma
p
Mis
ma
tch
Current
Direct
ion
100%
1(0,0,0)
Priorities?
50%
Mismatch
Environmental
Conditions
B
Current
TemperatureWeather
A
C E
D
Design of Experiments
Mission
Conditions
Map
Quality
Objectives
Failure
Conditions
Sensors
AcutatorsSoftware
180°
Test Condition Ranking
Mission
Simulations
& Analysis
G(z)
K
UNCLASSIFIEDCopyright 2011 by the Charles Stark Draper Laboratory, Inc. all rights reserved
Behavioral Markov Reliability Analysis
Draper developed PARADyM Tool
System Markov Model
Component connections & logical dependencies
Component reliability values (MTBF)
Model Outputs
Probabilities
– Any failure condition over system life
– System Loss
Reliability Metrics
– System Reliability
– Sensitivity of Reliability to Component Failures
4
1
2
3
4
Failure
rate = a
Failure
rate = b
Failure
rate = c
a
b
c
Operational State
System Loss State
0 FL 1 FL
P(System Loss) = Σ(System Loss States)
Reliability = Σ(Operational States)
Motor
Pu
mp
Tank
UNCLASSIFIEDCopyright 2011 by the Charles Stark Draper Laboratory, Inc. all rights reserved
Case Study: Unmanned Underwater Vehicle (UUV)
Based on Typical Small UUV
Non-redundant
architecture
ASTM F41 Software
Architecture
Component Reliability
Estimates
Many hardware item
values (motors, valves)
from U.S. Army service
data
Computer & Sonar values
from vendors of similar
hardware
Simulated Failure Nodes
5
PCControls
Vehicle
Payload
Sensors
ACPerforms Mission
Planning, Commands
Steering & Speed,
Payload Use
Scheduling, SAVC
Performs Low-
Level Vehicle
Control and
Management
Simulation Models
(Ocean + Vehicle)
Sink 1
Sink 2
Injected
FailureSource
UNCLASSIFIEDCopyright 2011 by the Charles Stark Draper Laboratory, Inc. all rights reserved