uav research at georgia tech - eric n. johnsonuav research at georgia tech eric n. johnson lockheed...

June 2002 ENJ - Georgia Tech 1

UAV Research at Georgia TechEric N. JohnsonEric N. Johnson

Lockheed Martin Assistant Professor of Lockheed Martin Assistant Professor of Avionics Integration, Avionics Integration,

Georgia Tech School of Aerospace EngineeringGeorgia Tech School of Aerospace Engineering

Presentation at TU DelftPresentation at TU Delft

June 3, 2002June 3, 2002


Outline

•• Previous WorkPrevious Work–– MIT and Draper LaboratoryMIT and Draper Laboratory–– Ph.D. Thesis Work: Advanced Control for the XPh.D. Thesis Work: Advanced Control for the X--3333

•• Current ResearchCurrent Research–– Adaptive Guidance and Control for Hypersonic VehiclesAdaptive Guidance and Control for Hypersonic Vehicles–– Aggressive Maneuvering for UAVsAggressive Maneuvering for UAVs–– DARPA Software Enabled Control, and the GTMax UAVDARPA Software Enabled Control, and the GTMax UAV–– Aerial Robotics CompetitionAerial Robotics Competition


Draper Small Autonomous Air Vehicle (DSAAV) in 1996

IMUSD MotionPak

32cc Engine

D-GPSNovAtel RT-20

Sonar Altimeter

Camera/Tx

Compass

486 ComputerRF Modem

Receiver/Servo Interface

Power Distribution

Battery

6 ft Rotor

Modified TSK BlackStarTotal Weight 23 Pounds, 10 kg


DSAAV at the 1996 Aerial Robotics Competition

•• Organized by the Association for Unmanned Vehicle Organized by the Association for Unmanned Vehicle Systems, International (AUVSI)Systems, International (AUVSI)

•• Epcot Center, Orlando, FloridaEpcot Center, Orlando, Florida

HelicopterHelicopterGround CoverageGround Coverage

Contest Area, 60x120 ftContest Area, 60x120 ft

Safety PilotSafety PilotEmergency Emergency Termination Termination

DD--GPS ReferenceGPS Reference

Vision Vision ProcessorProcessor

GCSGCS

Start BoxStart Box

Bosto

nAe

r ial Robot ic s

Team


Contest Flight #5


•• Research Project Sponsored by NASA MSFCResearch Project Sponsored by NASA MSFC–– Thesis Advisor: Anthony J. Calise, Georgia TechThesis Advisor: Anthony J. Calise, Georgia Tech

•• Exploring Flight Control Technologies Applicable to Exploring Flight Control Technologies Applicable to XX--33 & Future Reusable Launch Vehicles (RLV)33 & Future Reusable Launch Vehicles (RLV)–– Reduce Analysis Required per MissionReduce Analysis Required per Mission–– Increase Tolerance to Failures and EnvironmentIncrease Tolerance to Failures and Environment

Limited Authority Adaptive Flight Control


Neural-Network Adaptive Flight Control

ApproximateDynamicInversion

ApproximateDynamicInversion

PseudoPseudo--ControlControl

νPlantPlant

δ

Plant Inputs (Actual Controls)Plant Inputs (Actual Controls)

+Reference Model

Reference Model

CommandCommand

rmν

PDControl

PDControl

NeuralNetworkNeural

Network

-

+ TrackingTrackingErrorError

pdνadν−


( ) azez −+

=1

1σ

( )xVσW TT== yadνIn matrix form:In matrix form:

•• Feedforward Neural Networks Feedforward Neural Networks with a Single Hidden Layer are with a Single Hidden Layer are Universal Approximators.Universal Approximators.

•• The Sigmoidal Activation The Sigmoidal Activation Function has Internal Function has Internal Activation Potential ‘a’.Activation Potential ‘a’.

Single Hidden Layer Neural Network

( )σ ⋅

( )σ ⋅

( )σ ⋅

( )σ ⋅

o

o

x1

x2

xN1

o

y1

y2

yN3

V W

N2

N1 N3


( )[ ][ ] V

W

V||'WζV

W||ζxV'W

Γ+−=

+−Γ−=

ζσxζσσ

T

T

κκ

D

DAdaptation Law:Adaptation Law:

Define:Define:

0Q,QPAPA

PζT >−=+

= beTrm

Error Dynamics:Error Dynamics:

((A is Hurwitz)is Hurwitz)

∆−=

−+=

ννννν

xadpdcrm

��

( )∆−+= adrmrm bee νA�

Neural Network Adaptation

( ) ( )zzσzσ

∂∂='

(Diagonal Matrix)(Diagonal Matrix)

−−

=xxxx

erm

rmrm

��


Issues

•• Capability is LimitedCapability is Limited–– Saturation (Including Axis Priority), Rate LimitsSaturation (Including Axis Priority), Rate Limits

•• Not Feedback LinearizableNot Feedback Linearizable•• Sign of Control Effectiveness Becomes ZeroSign of Control Effectiveness Becomes Zero

–– Discrete Control (e.g., RCS Thrusters)Discrete Control (e.g., RCS Thrusters)

•• Need to Make a Flight Certification CaseNeed to Make a Flight Certification Case–– Show Adaptation Extremely Unlikely to Show Adaptation Extremely Unlikely to CauseCause Loss of VehicleLoss of Vehicle

•• Assumptions for Stability Need to be Extremely MildAssumptions for Stability Need to be Extremely Mild•• Require Recovery from Temporary “Faulty” AdaptationRequire Recovery from Temporary “Faulty” Adaptation


NN Adaptive Control with Pseudo-Control Hedging (PCH)

PDControl

PDControl

DynamicInversionDynamicInversion

NeuralNetworkNeural

Network

PlantPlantReference Model

Reference Model

-

++

TrackingTrackingErrorError

CommandCommandEstimateHedge

EstimateHedge

ActuatorActuatorcmdδ

hedgeν

δν

x

rmν

pdνadν−


Implications

•• “Shelter” Adaptive Element from the Adverse Effects “Shelter” Adaptive Element from the Adverse Effects of Plant Input Characteristics: of Plant Input Characteristics: –– Linear Dynamics, Latency, Saturation, Rate Saturation, etc.Linear Dynamics, Latency, Saturation, Rate Saturation, etc.

•• Achievable Adaptation Performance is Increased Achievable Adaptation Performance is Increased DramaticallyDramatically

•• Adaptation is Correct During SaturationAdaptation is Correct During Saturation–– Adaptive Element Can Recover from “Faulty” Adaptation Adaptive Element Can Recover from “Faulty” Adaptation

•• Enables Correct Adaptation When Not in Control of Enables Correct Adaptation When Not in Control of PlantPlant


X-33 Flight ControlSponsored by NASA MSFC

•• Ascent PhaseAscent Phase–– Linear Aerospike Roll/Pitch/YawLinear Aerospike Roll/Pitch/Yaw–– Aerodynamic Controls:Aerodynamic Controls:

•• Body FlapsBody Flaps•• ElevonsElevons•• RuddersRudders

•• Transition and Entry Transition and Entry –– Reaction Control System (RCS)Reaction Control System (RCS)–– Aerodynamic ControlsAerodynamic Controls

RCS (8)RCS (8)Aero Surfaces (8)Aero Surfaces (8)

Aerospike Throttles (4)Aerospike Throttles (4)


-2.5-2

-1.5-1

-0.50

0.51

1.52

2.5

0 50 100 150 200

time (sec)

roll pitch yaw

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

0 50 100 150 200

time (sec)

attit

ude

erro

r (de

g)

roll pitch yaw

Nominal Ascent Phase Results

•• Preliminary Results, Ascent Preliminary Results, Ascent Flight ControlFlight Control–– 33--Axis Attitude SystemAxis Attitude System

•• Performance Improved Over Performance Improved Over Existing DesignExisting Design–– Attitude Error is LowerAttitude Error is Lower–– Hinge Moments Look GoodHinge Moments Look Good–– NothingNothing is Scheduled!is Scheduled!

BaselineBaseline

NNNN


-120

-60

0

60

120

0 50 100 150 200

time (sec)

attit

ude

erro

r (de

g)

roll pitch yaw

Ascent Phase Multiple Actuator Failures

-150

-100

-50

0

50

100

150

0 50 100 150 200

time (sec)

roll pitch yawBaselineBaseline

NNNN

•• Half of Aero Surfaces Fail Half of Aero Surfaces Fail HardHard--Over at 60 secOver at 60 sec

•• (All Right(All Right--Hand Surfaces Hand Surfaces Give Uncommanded Left Give Uncommanded Left Turn)Turn)

•• Occurs Near Max Q Occurs Near Max Q (60 Seconds)(60 Seconds)

FailureFailure


Ascent Phase Multiple Actuator Failures NN Controller

-15-10-505

101520253035

0 50 100 150 200

time (sec)

surf

ace

defle

ctio

n (d

eg)

flapR flapL elevonInRelevonInL elevonOutR elevonOutLrudderR rudderL

•• Saturates on All Three AxesSaturates on All Three Axes

•• Vehicle Rolls Three TimesVehicle Rolls Three Times

•• Full Recovery Once Full Recovery Once Dynamic Pressure Dynamic Pressure DropsDrops

EffectorsEffectors


•• Adaptation is “Correct” Adaptation is “Correct” During SaturationDuring Saturation

•• No Knowledge of No Knowledge of Failure Used Failure Used (Not Even in the (Not Even in the Hedge!)Hedge!)

Ascent Phase Multiple Actuator Failures NN Controller

Roll Axis PseudoRoll Axis Pseudo--Control SignalsControl Signals

-0.20

0.20.40.60.8

11.2

0 50 100 150 200 250

time (sec)

del vad


Subsequent Research Involving PCH

•• XX--33/RLV Attitude Control33/RLV Attitude Control

•• Adaptive Tracking and Control Adaptive Tracking and Control (Inner and Outer Loops) for RLV(Inner and Outer Loops) for RLV

•• Reconfigurable Flight Control for Civillian Aircraft Reconfigurable Flight Control for Civillian Aircraft (Training While Not in Control)(Training While Not in Control)

•• Yamaha RYamaha R--50/R50/R--MaxMax

•• JDAMJDAM


Georgia Tech UAV Research Facility

http://controls.ae.gatech.edu/labs/uavrfhttp://controls.ae.gatech.edu/labs/gtar


Approach to Adaptive Trajectory Following

•• PCH is Used ToPCH is Used To–– Modify the Command Trajectory to Create the Feasible Reference Modify the Command Trajectory to Create the Feasible Reference

Trajectory (And Leave it Alone if Not at Limits)Trajectory (And Leave it Alone if Not at Limits)–– Protect Outer Loop Adaptation From Inner Loop DynamicsProtect Outer Loop Adaptation From Inner Loop Dynamics–– Protect Inner Loop Adaptation From Limited Control Authority Protect Inner Loop Adaptation From Limited Control Authority

(As Before)(As Before)

InnerLoop

InnerLoop

CommandTrajectory

OuterLoop

OuterLoop

NeuralNetworkNeural

Network

vx, θ

PCHPCH


Application to Rotorcraft Maneuvering

Yamaha R-MaxSimulation Results:Fly in a Circle While

Pirouetting-80 -60 -40 -20 0 20 40 60 80

-80

-60

-40

-20

0

20

40

60

80

Eas t

Nor

th

Network ON

Vel = 15 ft/sYaw = 45 o/sec

-80 -60 -40 -20 0 20 40 60 80-80

-60

-40

-20

0

20

40

60

80

Eas t

Nor

th

Network OFF

1st time around

“Circle”Network ON

Better Each Time

“Pentagon”Network OFF


Software Enabled ControlSponsored by DARPA

•• Develop softwareDevelop software--enabled control methods for enabled control methods for complex dynamic systems with application focus on complex dynamic systems with application focus on intelligent UAVsintelligent UAVs

•• SupportSupport--thethe--development and implement a plugdevelopment and implement a plug--andand--play, realplay, real--time software architecturestime software architectures

•• VTOL UAV hardwareVTOL UAV hardware--inin--thethe--loop simulation and flight loop simulation and flight testingtesting


Non-VolatileMemory Services

Serv

ices Scheduling

ServicesEvent

ServicesNaming

Services

PersistenceServices

TimerServices

TimeServices

Real-Time ORB

OS and Hardware Interfaces

ApplicationComponent



Bold Stroke Open Systems Architecture

•• RealReal--time CORBAtime CORBA--based Integration of Distributed, based Integration of Distributed, Heterogeneous ComponentsHeterogeneous Components

•• Utilizes Object Request Broker (ORB) Architecture Utilizes Object Request Broker (ORB) Architecture Developed by Washington University and BoeingDeveloped by Washington University and Boeing


Ope

n Sy

stem

s Ar

chite

ctur

e

Con

trol

ler

Inte

rfac

e

•• Distributed objectsDistributed objects•• PlugPlug--andand--playplay•• EncapsulationEncapsulation•• ReconfigurationReconfiguration

PID

Neural Net

ControllerStrategy

Sensor InterfacesSensor

Interfaces

Rigid Body + Rotor Dynamics

Force and Moment

Calculations

Sensor ModelsServo Dynamics

Actuator InterfaceActuator Interface

UAV

Inte

rfac

e

Simulation Model

UAV

Inte

rfac

e

Sensor InterfacesSensor

Interfaces

ActuatorInterfaceActuatorInterface

Vehicle

Component Communication Example


Recent UAV Platform Integration Work

•• Yamaha RYamaha R--Max, 66kg, 3m Rotor DiameterMax, 66kg, 3m Rotor Diameter

•• Avionics and Simulation Tools Developed Over the Avionics and Simulation Tools Developed Over the Past YearPast Year

•• HardwareHardware--inin--thethe--Loop Simulation and Ground Loop Simulation and Ground Testing Started in November 2001Testing Started in November 2001

•• Navigation System Ground Tests Navigation System Ground Tests Completed February 2002Completed February 2002

•• Flights Testing (With Avionics)Flights Testing (With Avionics)Began March 2002Began March 2002


GTMax Hardware Components

•• Flight ComputerFlight Computer–– 266MHz Embedded PC, 266MHz Embedded PC,

Ethernet, Flash DriveEthernet, Flash Drive

•• SensorsSensors–– Inertial Measurement UnitInertial Measurement Unit–– Differential GPSDifferential GPS–– MagnetometerMagnetometer–– Sonar and Radar AltimetersSonar and Radar Altimeters–– Vehicle Telemetry Vehicle Telemetry

(RPM, Voltage, Pilot Inputs)(RPM, Voltage, Pilot Inputs)

•• Data LinksData Links–– 11 Mbps Ethernet Data Link11 Mbps Ethernet Data Link–– RSRS--232 Serial Data Link232 Serial Data Link


GTMax Hardware Integration

•• Exchangeable modules:Exchangeable modules:–– Flight Computer ModuleFlight Computer Module–– GPS ModuleGPS Module–– Data Link ModuleData Link Module–– IMU/Radar ModuleIMU/Radar Module–– Unused Module (Growth)Unused Module (Growth)–– Sonar/Magnetometer AssembliesSonar/Magnetometer Assemblies–– Power Distribution SystemPower Distribution System

•• Each module has selfEach module has self--contained power regulation contained power regulation and EMI shieldingand EMI shielding

•• Vibration isolated main Vibration isolated main module rackmodule rack


Onboard Avionics Architecture

Flight Computer

Serial Extension Board

FreewaveDGR-115

DC/DC

5V

5V

Battery 12V

Auxi

liary

Mod

ule

Aironet MC4800

EthernetHub

NovAtel RT-2 GPS Receiver

Auxiliary Computer /Payload

Radar Altimeter

ISIS-IMU

12V

DC/DC

DC/DC

HMR-2300Magnetometer

DC/DC

Sonar Altimeter

Power DistributionModule

Generator

Yamaha AttitudeControl System

RC Receiver YACS IMU

12V5V

RS-232 SerialEthernetDC Power

Dat

a Li

nkM

odul

eG

PS M

odul

eIM

U/R

adar

Mod

ule

Flig

ht C

ompu

ter

Mod

ule

5V

12V


Baseline Onboard Software

•• NavigationNavigation–– 17 State Extended Kalman 17 State Extended Kalman

Filter Navigation SystemFilter Navigation System•• Vehicle PositionVehicle Position•• Vehicle VelocityVehicle Velocity•• Vehicle AttitudeVehicle Attitude•• Accelerometer BiasesAccelerometer Biases•• Gyro BiasesGyro Biases•• Terrain HeightTerrain Height

–– All Attitude CapableAll Attitude Capable–– 100 Hz Updates100 Hz Updates–– Flight OperationalFlight Operational

•• ControlControl–– Adaptive Neural Network Adaptive Neural Network

Trajectory Following Trajectory Following Controller Controller

–– Neural Network Neural Network •• 16 Inputs16 Inputs•• 5 Hidden Layer Neurons 5 Hidden Layer Neurons •• 6 Outputs for 6 Degrees 6 Outputs for 6 Degrees

of Freedomof Freedom–– Can Also Be Configured as a Can Also Be Configured as a

Conventional Inverting Conventional Inverting ControllerController

–– Flight OperationalFlight Operational


Simulation Tools

•• Hardware In the Loop Hardware In the Loop Simulation CapableSimulation Capable

•• The Desktop Computer The Desktop Computer Simulation Utilizes Simulation Utilizes –– Actual Flight SoftwareActual Flight Software–– Actual Ground Control Station Actual Ground Control Station

SoftwareSoftware–– Flight Test Verified Dynamic Flight Test Verified Dynamic

Model of HelicopterModel of Helicopter–– Flight Test Verified Model of All Flight Test Verified Model of All

Sensors/ActuatorsSensors/Actuators–– Scene Generation CapabilityScene Generation Capability


Software in the Loop (SITL)•• Test algorithms within the simulationTest algorithms within the simulation•• Generate emulated sensor data from an aircraft simulation Generate emulated sensor data from an aircraft simulation

(including errors)(including errors)

Vehicle Model

SensorDrivers

SensorEmulation(w/ Error Model)

ActuatorDriver

SensorData

StateEstimate Control

ActuatorSimulation

State Control

SensorRaw Data

ActuatorRaw Data

Desktop Computer

TrajectoryPlanner

OtherSystems

FlightController

NavigationFilter


Hardware in the Loop (HITL)•• Flight software runs on the onboard computerFlight software runs on the onboard computer

•• Onboard computer “thinks” it is flying the vehicleOnboard computer “thinks” it is flying the vehicle

Vehicle Model

SensorDrivers

SensorEmulation(w/ Error Model)

ActuatorDriver

SensorData

StateEstimate Control

ActuatorSimulation

State Control

SensorRaw Data

ActuatorRaw Data

Desktop Computer

Flight Computer

TrajectoryPlanner

OtherSystems

FlightController

NavigationFilter


GTMax Flight Operations

•• Network Connections Network Connections Available At Ground Control Available At Ground Control Station from HubStation from Hub–– Multiple Laptops Can Multiple Laptops Can

Communicate with Onboard Communicate with Onboard Computers SimultaneouslyComputers Simultaneously

•• Due to Generator, Due to Generator, Endurance Limited by Endurance Limited by Onboard Fuel (> 1 hour)Onboard Fuel (> 1 hour)

•• Ground Equipment Can Ground Equipment Can Operate on 115VAC or Operate on 115VAC or 12VDC and Has Battery 12VDC and Has Battery BackupBackup


Flight Testing in McDonough, Georgia


First Tests w/ Baseline Controller

•• Neural Network Adaptive Controller on First Flight Neural Network Adaptive Controller on First Flight Test Day (April 10, 2002)Test Day (April 10, 2002)

•• Even With LargeEven With LargeModel Errors, Model Errors, System Was AbleSystem Was AbleTo Control theTo Control theHelicopterHelicopter


Results With Baseline Controller

Step Input of Altitude Command:

1 130 1 135 1140 114 5158

160

162

164

166

168

170

time (sec )

altit

ude

(ft)

s tep input of pos ition c ommand, do wn

P os ition Es tima teP os ition Command


Flight Control Reconfigurations

•• Switched Between Neural Network Adaptive Switched Between Neural Network Adaptive Controller to Much Simpler Conventional Inverting Controller to Much Simpler Conventional Inverting Controller and BackController and Back

•• Real Time and Real Time and Closed LoopClosed Loop

1 80 190 2 00 210 2 20 230 2 40 250325

330

335

340

345

350Con trol Rec onfig ura tion

East

(ft)

time (sec )

P os ition Es tima teP os ition Command

Reconfiguration at 206.24


Collective control failureCollective control failure

Failure detection and control reconfiguration with RPM control

With fault tolerantand reconfigurablecontrol system

With fault tolerantand reconfigurablecontrol system

Simulated Main Rotor Actuator Failure

Without fault tolerantand reconfigurablecontrol system

Without fault tolerantand reconfigurablecontrol system


Tail rotor failureTail rotor failure

Without fault tolerant and reconfigurable control system

Without fault tolerant and reconfigurable control system

Tail Rotor Failure (in Simulation)

Gain altitude usingmain rotor collective

Control reconfiguration using main rotor controls

Translatory descent to a clear area

Control reconfigurationfor autorotation

With fault tolerant and reconfigurable control system

With fault tolerant and reconfigurable control system

Autorotation and landing


International Aerial Robotics 2001-

Launch AreaLaunch Area

Two Lights Two Lights Identify Identify BuildingBuilding

3 km3 km

Vehicle orVehicle or SubvehicleSubvehicle(s) Enter Building(s) Enter Building

Building and an Building and an Entry Point FoundEntry Point Found

>1m>1m

Transmit an Image of “Point of Interest” Transmit an Image of “Point of Interest” Inside BuildingInside Building

In Less Than 15 Minutes:In Less Than 15 Minutes:

Image ReceiverImage Receiver(& Other(& Other GoundGound Components)Components)Sign OverSign Over

EntryEntry


International Aerial Robotics Competition

•• Unmanned and Autonomous Unmanned and Autonomous (No Active Human Operators, no Tethers)(No Active Human Operators, no Tethers)

•• Some Components Can Remain on the Ground Some Components Can Remain on the Ground (e.g., Additional Computers, Navigation Aids)(e.g., Additional Computers, Navigation Aids)

•• Launch and Recovery Need Launch and Recovery Need NotNot Be AutonomousBe Autonomous

•• Mission is Divided into “Levels”Mission is Divided into “Levels”

•• Each Teams Gets 60 Minutes To Fly (...Per Year)Each Teams Gets 60 Minutes To Fly (...Per Year)

•• Rules Change Once a Mission is CompletedRules Change Once a Mission is Completed

http://http://avdilavdil..gtrigtri..gatechgatech..eduedu/AUVS//AUVS/CurrentIARCCurrentIARC/2001CollegiateRules.html/2001CollegiateRules.html


Mission Levels

•• Level 1: Follow Prescribed Waypoints for 3kmLevel 1: Follow Prescribed Waypoints for 3km

•• Level 2: Locate Building and Find an EntryLevel 2: Locate Building and Find an Entry

•• Level 3: Enter the Building Level 3: Enter the Building –– Can Be a Different Vehicle or Can Be a Different Vehicle or SubvehicleSubvehicle That Used AboveThat Used Above–– Can Launch Near Target StructureCan Launch Near Target Structure

•• Level 4: Image Desired Location Within Building and Level 4: Image Desired Location Within Building and Transmit Transmit –– Complete In < 15 Minutes (Launch to Data Retrieval)Complete In < 15 Minutes (Launch to Data Retrieval)

•• Contest is Over Once Somebody Does Level 4Contest is Over Once Somebody Does Level 4


2001 Airplane: ¼ Scale Cub


Winning Flight in 2001, Level 1

-76.44-76.435

-76.43-7 6.425

-7 6.42-7 6.415

38.14

38.14 2

38.144

3 8.14 6

38.148

38. 15

38.15 2

38. 1540

500

Longitude

Latitude

Alti

tude

Automatic FlightManual Takeoff/Landing

Waypoint 1

Waypoint 2

Waypoint 3

Waypoint 4 & Holding Pattern

“Runway” & Ground Station

Under Automatic Flight:Under Automatic Flight:Distance Traveled: 3.1 mi / 4.9 kmDistance Traveled: 3.1 mi / 4.9 kmTime: 3 min 9 secTime: 3 min 9 secAverage Speed: 58 mph / 93 Average Speed: 58 mph / 93 kphkphMax Distance from Ground Station: ½ mi / 0.8 kmMax Distance from Ground Station: ½ mi / 0.8 kmAverage Altitude: 397 ft / 121 mAverage Altitude: 397 ft / 121 m


Plans for 2002

•• Level 2Level 2–– Add a Video Camera and Image Processor Add a Video Camera and Image Processor

(Donation from Texas Instruments)(Donation from Texas Instruments)–– Switch GPS to DSwitch GPS to D--GPS For Level 2 Accuracy (NovAtel)GPS For Level 2 Accuracy (NovAtel)–– Update Ground Station Software and Develop Image Processing Update Ground Station Software and Develop Image Processing

Software Software –– Possibly Also Switch to GTMaxPossibly Also Switch to GTMax

•• Level 2+Level 2+–– Design, Building, and Testing for SubDesign, Building, and Testing for Sub--Vehicle: Drops From Airplane Vehicle: Drops From Airplane

and Enters Building and Enters Building –– Designs for Operation Inside Building (Levels 3 & 4)Designs for Operation Inside Building (Levels 3 & 4)


Some Potential Areas of Collaboration

•• VTOL and FixedVTOL and Fixed--Wing UAV Flight TestingWing UAV Flight Testing–– Lessons LearnedLessons Learned–– Simulation Models and SoftwareSimulation Models and Software–– GTMax as a Research Flight Test PlatformGTMax as a Research Flight Test Platform

•• Studies at Georgia TechStudies at Georgia Tech


UAV Avionics at the2002 Digital Avionics Systems Conference•• AIAA/IEEEAIAA/IEEE

•• October 27October 27--31; Irvine, California31; Irvine, California

•• NEWNEW Applications of Avionics: Applications of Avionics: Uninhabited Air Vehicles (UAV) & Missiles Track:Uninhabited Air Vehicles (UAV) & Missiles Track:–– Avionics systems for UAVs, intelligent systems for vehicle autonAvionics systems for UAVs, intelligent systems for vehicle autonomy, omy,

operation of UAVs in controlled airspace, payloads, missiles, anoperation of UAVs in controlled airspace, payloads, missiles, and d guided munitionsguided munitions

–– 5 Sessions5 Sessions–– Paper Acceptance Still Possible for New Track, But Act FastPaper Acceptance Still Possible for New Track, But Act Fast

•• Contact: Eric N. JohnsonContact: Eric N. Johnson404404--385385--2519, Eric.Johnson@2519, [email protected]

uav research at georgia tech - eric n. johnsonuav research at georgia tech eric n. johnson lockheed...

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