2011 national air quality conferences j.b. kosmatka , project lead

2011 National Air Quality Conferences

J.B. Kosmatka, Project LeadThomas S. Hong, Student Lead (Presenter)Department of Mechanical and Aerospace EngineeringUniversity of California, San Diego

Massimiliano Lega, CollaboratorDipartimento di Scienze per l’Ambiente, Universita degli Studi di Napoli PartenopeGiuseppe Persechino, CollaboratorCIRA, Italian Aerospace Research Centre

March 9th, 2011

AIR QUALITY PLUME CHARACTERIZATION AND TRACKING USING SMALL UNMANNED AIRCRAFT

OUTLINE Introduction

Plumes UAS

Current System Capabilities

Proposed Test Details

The Future

Composite Aerospace Structures Laboratory

INTRODUCTION: PLUMES

A volume of gas or fluid with a composition of interest moving through another

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Mt. Etna (NASA Image)

INTRODUCTION: PLUMES

Make up can be particulate, chemical, biological, radioactive matter

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Escondido Controlled Burn (AP Photo)

2007 California Wildfires (NASA Image)

INTRODUCTION: PLUMES Wind shear causes

plume drift that is hard to predict

Varying scale plumes call for a scalable solution

Potentially invisible and or hazardous to life and manned sensors

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INTRODUCTION: UNMANNED AERIAL SYSTEM

Used in 3-D missions Dull, Dirty or

Dangerous Small Unmanned Aerial

System (sUAS) More maneuverable than

full sized counterparts Lower operational costs Can be launched and

recovered almost anywhere

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Raven UAS (Aerovironment photo)

Small electric airframe with pusher propeller

2 lbs maximum weight Endurance with payload: 30 minutes

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Multiplex Easy Star 54”(UCSD photo)

SYSTEM DETAILS: AIRCRAFT

Kestrel 2.2 COTS autopilot – GPS, IMU, multiple failsafes

Allows for a easily controlled and fully autonomous aircraft

A external multiplexor is used so that the safety pilot can take over the aircraft at any time

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Kestrel 2.X (Procerus Technologies photo)

SYSTEM DETAILS: AUTOPILOT

SHARP Compact Optical Dust Sensor

Saturated by visible smoke

Allows us to map the boundaries of test plumes

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Optical dust sensor(SHARP photo)

SYSTEM DETAILS: SMOKE SENSOR

SYSTEM DETAILS: PRIOR ART

Kemp et al. 2004 Bang-Bang algorithm

with multiple UUVs Coordination achieved by

changing speed of UUVs

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Complete vs. incomplete coverage

Coverage of three perimeters

SYSTEM DETAILS: PRIOR ART

Hsieh et al. 2005 Implementation of the Kemp

method on ground robots (2D experiment)

Were satisfied with results and its ease of implementation

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Ground tracks of three robots and cooperatively gathered boundary points

SYSTEM DETAILS: PRIOR ART

Encountered a problem: Fast moving vehicles with limited

maneuverability Limited plume sizes

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SYSTEM DETAILS

Plume boundary mapping and tracking via fly-through and centroid

measurements

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Centroid Tracking Algorithm

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SYSTEM DETAILS

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MATLab simulation(UCSD image)

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Centroid Tracking Algorithm

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MATLab simulation(UCSD image)

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Tracking Algorithm with Moving Plume

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SYSTEM DETAILS

Allows for initial 2-D mapping, and tracking of plume by individual UAVs

for a 3-D composite imageComposite Aerospace Structures Laboratory

The controls group has conducted a autonomous ground test with multiple ground robots

Control of the robots as well as their data were handled by an off-site super computer

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Ground robot testing(UCSD photo)

PREVIOUS TESTING

2009 testing was conducted at Los Alamos, New Mexico

Plume characterization tests were conducted without aircraft, and once characterized, the UAVs were flown through the plume for sensor data

Composite Aerospace Structures Laboratory

Smoke testing at Los Alamos (UCSD photos)

PREVIOUS TESTING

Learned that smaller and slower aircraft were needed

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Flight test data (UCSD image)

PREVIOUS TESTING

Cooperative flight (UCSD photo)

Coordinated flight with 3 UAVs at different altitudes

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PREVIOUS TESTING

• Gridded initial search pattern highlights grids with positive readings

• Highlighted grids can be meshed finer then re-queried• Human operator can pick grids of interest if there are multiple regions

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Gridded search simulation(UCSD image)PREVIOUS TESTING

Once a positive detection is made, the algorithm goes into tracking mode

Tracking algorithm simulation (UCSD image)

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PREVIOUS TESTING

Simulated smoke missions at UCSD (UCSD photo)

Servo operated plunger simulated smoke Successful tracking and estimation of plume

boundaries

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PREVIOUS TESTING

Flight tests to be conducted at NASA Dryden FAA regulated flights with multiple UAVs Aircraft will autonomously track released smoke using

the boundary mapping algorithms and wind estimations

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FUTURE TESTS

2011 Flight Testing Schedule: May: Good, Low winds (5-10 days

possible) June: Good, Low winds (10-15 days

possible) July: Great, no wind, hot (30 days

possible, dawn flight) August: Great, no wind, hot (30 days

possible, dawn flight) September: Great, no wind, hot (30 days

possible, dawn flight)

Cruise Ship Pollution and Marine Shipping off of Anacapa Island, CA (Jim Walker, APCD photo)

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FUTURE TESTS

Major shipping routes (CruiseLawNews image)

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FUTURE TESTS

QUESTIONS?Aknowledgements

Chad Foerster, Nima Ghods, Tim Wheeler, David Zhang, 1

Charles Farrar, Will Fox, Matthew Bement, Mike Proicou, and Jeffery Hill 2

1 University of California, San Diego2 Los Alamos National Lab

Composite Aerospace Structures Laboratory