Transcript Slide 1

Intelligence Surveillance and Reconnaissance System
for California Wildfire Detection
Team: ISR Firefighting
Team members:
Shashank Tamaskar
Nadir Bagaveyev
Evan Helmeid
Tiffany Allmandinger
Presented byShashank Tamaskar
Purdue University
[email protected]
Overview
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Definition
Abstraction
Modeling and Implementation
Future work
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Definition
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Need: In 2008, Arsonist fires burned down 25,000 acres of forest land
resulting in 24 million dollar damage to property
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Objective: To understand and analyze the problems associated with the
wildfire prevention and management system and to suggest improvements to
enable faster fire detection in the region
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SoS traits: Heterogeneous geographically separated agents (Watchtowers,
UAVs, arsonists, other human agents) with different degree of autonomy
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Status Quo: Current system consists of watchtowers and the reliance on
civilian reports. Intelligence of multiple fires and fire state dependent on
ground crews or manned aircraft scouting situation. Manned airplanes limited
in allowed exposure to fire conditions. No night flying allowed.
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Operational context: To limit the scope of the project we have concentrated
on interaction between the resource and operational alpha level entities. The
following figure shows our area
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Definition
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Abstraction -“Framing key descriptors and their evolution”
Paper Model
Environment
UAVs
Watchtowers
Home Base
Calculate UAV path based on coverage of assets
Command tracking in case of fire detection
Delay: Call 911 after
20 min
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Abstraction
UAV Path Calculations
Different operating scenarios:
Zigzag model
AOI divided into sectors
Coverage due to watchtowers ignored
Waypoints predefined
ABM
Waypoints dynamically added depending upon coverage
UAV’s avoid watchtowers
Optimal Path Generation
Optimal path generation to maximize the coverage
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Abstraction: Zigzag model
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Abstraction: ABM
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Implementation
Platform: STK is used for calculation of positions of mobile agents while Matlab
is used to implement path algorithms and calculate coverage
Object orientation programming is used to rapidly develop large code
(>1000 loc) also the modular architecture of the code helps us keep the
effective complexity of the code low
Metrics: Four metrics for system performance
1. Coverage
2. Cost
3. Detection Time
4. Response time
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Implementation
Problems addressed:
• Coverage Problem: How to efficiently provide coverage to a area given a set
of assets
• Detection Problem: How to improve the fire detection time
• Random fires
• Arsonist fires
• Arsonist tracking: Track the arsonist after the fire is detected
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Implementation
Demonstration of the model
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Implementation
Simulation Results – Verification/Validation
For a constant field of view:
• 1 UAV provides worst coverage
• 2 to 5 UAVs do not present significant
coverage differences
• Coverage metric is directly related to the detection time over all simulations
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Implementation
Simulation Results
• 1 UAV has the worst detection time
• 10o, 15o FOV cause significant increase
in detection time over 20o, 45o
• 20o FOV provides the best coverage for
the cost
• Cost is directly related to the FOV
• Small FOV yields a highly unstable
system and requires many more
simulations to determine trends
• The larger FOV follows the expected
trend: more UAVs  faster detection
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Implementation
Simulation Results
• Determine effectiveness of watchtowers
and impact on UAV necessity
• UAVs detected the majority of the fires
• Provide significant increase in
system performance over the
current state
• As the number of simulations were
increased the fraction of fires
detected by the watchtowers
became even less
• Watchtowers are good for random
fires but UAVs are good for
arsonist fires. UAVs also allow for
arsonist tracking
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Implementation
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Implementation
Simulation Results
• 10o and 15o FOV do not provide low
enough detection time
• 20o and 45o are the most effective
• 20o is the most cost effective
• Best performance for the money
• 45o does not provide enough
benefit increase to justify
increased cost over the 20o FOV
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Implementation
Simulation Results: Arsonist detection
• Only 19 out of 132 simulations resulted in arsonist detection
• In most cases fires were detected late after they started so arsonists had
sufficient time to flee the scene
• Probability of arsonist detection increases with increase in number of UAVs,
Speed, FOV and altitude
• Arsonist detection by Humans was reported too late
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Conclusions
• Simulation:
• Effective and valid: Simulations show a good correlation with paper model
• Metric is accurate: Found a good correlation between detection time and
coverage metric
• More iterations and simulations are necessary to draw proper conclusions
• Generated a model which can be applied to other ISR problems
• Conclusions:
• UAVs with greater FOV and Altitude can significantly improve the detection time
• More UAVs provide better coverage, but do not necessarily provide significant
benefits
• Arsonist detection may better suited with a fleet mix of UAVs. Slow UAVs for fire
detection and Fast for Arsonist detection
• Watchtowers are not well suited for detection of arsonist fires
A SoS approach is beneficial in analyzing the options for improving the current
system, but it may not be feasible to implement the SoS
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Future Work
• Implement UAV-avoidance algorithm
• Do not revisit areas that were just scanned
• Limit conflict between UAVs
• Consider refueling time
• Create a detailed cost model
• Determine camera/sensor array to use
• Determine optimal UAV for given parameters
• Pool together the lessons learned by various teams
and develop a general purpose tool for ISR
applications which can be used for research at
Purdue
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System-of-Systems Laboratory: Aeronautics Applications
Director: Prof. Dan DeLaurentis ([email protected])
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Thank you for
your
consideration!
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