Momentum - February 2021 - 9

Student Generation
as cars or animals. With these constraints in mind, we selected
a camera that could capture medium wave infrared (MWIR)
while also being lightweight and able rotate to provide flight
stabilization. The camera we picked could also change its field
of view so that we could have various coverage rates of the fire
depending on the altitude and angle we flew at. This could be
automatically adjusted using a series of on-board sensors. At
the maximum coverage rate, we would be able to capture 137
square miles of imaging data per hour with a single plane. To
put that into perspective, it would take about 150 hours of
flight time to image the entirety of the 2019 Californian
wildfires with a coverage rate of 50 square miles per hour - we
had nearly three times that. While a single plane may have
been sufficient, time would be a critical factor in an actual
wildfire and we therefore opted to use two imaging planes per
unit in our design to speed up our data collection rate.
While we had managed to sort out our issues with imaging,
the next step was to process and analyze the data. In short, we
needed to accomplish the following objectives: 
1.	 Locate the fire front.
2.	Stitch the images together to get an overall view of the
3.	Transmit the information to ground stations and first
We started by setting up two types of image processing.
One was more in-depth but required our imaging drones to
land, while the second type of processing was less accurate but
much faster and could be done in-flight using our on-board
computers. This type of rapid image processing would be
crucial, as it would help to reduce response times for ground
crews. To process the images effectively, we first limited the
rate of images that were taken to increase our operational
efficiency. This would allow us to get a holistic view of the
situation without slowing down our processing speeds.
Next, we would threshold the images to give us a clear
picture of where the fire front was located. After compressing
the image, we would then be able to transmit it live, back to
our ground station for further processing using our
communication drones. These two separate drones would each
carry a radio repeater for land mobile radio systems and act as
a relay plane for over-the-horizon communications. These
acted as mobile signal extenders and helped to transmit all the
information that was gathered through the imaging planes.
Once these images had been transmitted, we would be able to
stitch them together onto a digital elevation model using flight
data. This could then be used to aid in decision making when
paired with local meteorological data as we could predict
where the fire was headed next. Using this information, we
would then be able to optimize the flight paths of supporting
fire suppression aircraft using a heuristic algorithm to best
contain the fire.
All this could be done using the information gathered
through a ground station and system of four UAVs (two for
imaging, two for communications). These drones were all
modular, easily transportable, and could be launched into the
air almost instantly using pneumatic launch rails. With a


An example of a post-processed image. The white markers indicate the
location of active fires.
wingspan of seventeen feet, a cruise speed of 65 knots, and an
operational ceiling of 25,000 feet, the imaging drones could fly
for three hours or a distance of 195 nautical miles. The
communications drones could fly for almost four times that, as
they were lighter and carried more fuel in place of the camera.
The mechanical design for these UAVs was shaped by
factors such as the weight and dimensions of the camera,
endurance, elevation, and take-off/landing requirements. A
high wing design with modular wings and tail was chosen for
flight stability and transportability. This type of modular design
would allow for faster on-site assembly and reduce the time
needed to swap out critical components during a mission if
they were to be damaged. Our attempts at creating an efficient
vehicle also dictated several of our design decisions, from the
composite materials we selected to the airfoil of the drones.
Finally, all of our decisions were verified using rigorous
analysis, simulation, and testing criteria to ensure that our
drones would be able to effectively support wildfire
surveillance and suppression missions.
After finishing our overall design, we shifted our focus to
prepare for the competition. The transition to an online event
caused us to re-evaluate our strategy. We knew that it would
come down to how well we would defend our decisions down
to the last detail. With that in mind, we spent weeks practicing
our delivery, transitions, and timing. Leaving nothing to chance,
we exceeded our own expectations when the day of the
competition arrived. After the scores had been tallied, we
finished the year with our first ever competitive win. Our habit
of asking difficult questions and paying attention to details
helped us as both individuals and as a team to learn, grow, and
inevitably succeed. n
Shadman Sajid, a senior majoring in chemical engineering
at the University of Calgary, wrote this article for
MOMENTUM. He is president of Schulich UAV and has
been leading the team since 2019.

February 2021 9


Momentum - February 2021

Table of Contents for the Digital Edition of Momentum - February 2021

Momentum - February 2021 - Cover1
Momentum - February 2021 - Cover2
Momentum - February 2021 - 1
Momentum - February 2021 - 2
Momentum - February 2021 - 3
Momentum - February 2021 - 4
Momentum - February 2021 - 5
Momentum - February 2021 - 6
Momentum - February 2021 - 7
Momentum - February 2021 - 8
Momentum - February 2021 - 9
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