the water team enes100 over-sand vehicle challengebgrove1/sgc/enes100.pdf · 2017-08-27 · the...
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The mission was an absolute success. During testing,
Water Team managed two perfect runs, achieving every
base and advanced objective both under the anticipated
time and with better results. The OSV over-collected
water both times, drawing extra water to ensure it met the
objective’s amount. Water Team completed all of the
objectives in 2:28:12 min. and 2:09:85 min. in respective
trials, far faster than anticipated. With two perfect trial
runs, our OSV was undoubtedly one of the strongest
contenders of the semester.
Given the results, there is still room for improvement.
With more time, the OSV’s navigation system would be
practiced to an even finer tune. Given the chance, less-
powerful motors would’ve saved
the team money while still
accomplishing the objectives.
There were minor incidences of
wasted money when the designs
changed, and didn’t utilize
sensors that’d already been
bought. However, with this strong
performance, similar mistakes can
be avoided in the future.
The Water TeamENES100 Over-Sand Vehicle Challenge
Nick Abbott, Alexander Dessiatoun, Benjamin Grove, Jake Henkin, Becca Koontz,
Zongyan Li, Hunter Seefried, and Dylan Taira
Mission Objective
Our mission was to design and build an OSV that would
be able to autonomously navigate out of the Landing
Zone (LZ), to within 25cm of the water pool. Upon
reaching the pool, the OSV needed to measure and
transmit the depth of the pool to within 4mm, determine
the salinity state of the pool, and collect at least 75ml of
water. However, our build was limited, not being allowed
to exceed 3 kg, have a footprint greater than
350 x 350mm, or exceed $350 in final-build cost.
Objective Solutions
Navigation from the LZ1. - For basic navigation, the OSV
relied on constant coordinate transmission from an
APC220 Radio Communication Module. Purchased
four Lynxmotion Gearhead motors for individual wheel
propulsion, printed motor mounts and wheel adapters.
Pool Depth Measurement2. - To measure the pool
depth, an Arduino Water Level Sensor Module was
attached to the bottom of the rack. When released, it
would transmit depth from the bottom of the pool in
mm.
Water Salinity Status3. - The Water Level Sensor
Module was crucial because it also transmitted a
value indicating if the water was fresh or saline.
Water Collection4. - A Peristaltic Liquid Pump was used
to pull water to a collection tub mounted on the front of
the OSV. Water was drawn via a silicone tube
attacked to the bottom of the rack.
Auto-CAD Views
Original Proposed OSV Design Final OSV Design
Final OSV Left View Final OSV Top View
Testing Field and OSV Navigation Process
Vehicle Analysism = mass = 3kg
Weight (at 0)̊ = 3kg * 9.8 m/sec2 = 29.4 N
FN (each wheel) = Weight / 4 = 7.4 N
CRR = [(3.33cm3/N) * (FN/(w * d2))]⅓ = 0.38780
FRR = CRR * FN = 0.38780 * 7.4 N = 2.8697 N
FRRT = (2.87 N) * 4 = 11.48 N
a = 2d / (t^2)
a = (2 * 6.5m / 6) / (107seconds/6)^2 =
0.00682 m/sec^2
Sum of F = Total Tractive Effort (TE) – Total
Force of Rolling Resistance (FRRT)
0.00682 m/sec^2 * 3kg = TE- 11.48 N
TE = 11.50 N
CRR = [(3.33cm3/N) * (FN/(w * d2))]⅓ = 0.38780
μ = 0.7
CRR * L < TE < μ* L
At 0 Degrees:
11.4 N < 11.50 N < 20.6 N
At 35 Degrees:
9.32 N < 11.50 N < 16.9 N
To be safe, we picked a motor that generates
12.5 N of tractive effort.
Worst-case scenario:
OSV Speed = VOSV = total distance to travel /
total time = 6.5m / 107 seconds = 0.0607 m/s
Vrequired = radiuswheel x ϖrequired angular speed
0.0607 m/s = 0.046m x ϖrequired angular speed
ϖrequired angular speed = 0.0607 m/s (1 / 0.046m) =
1.32 rad/s = 12.6 rpm
At the operating point, our motor outputs:
Vactual = radiuswheel x ϖactual angular speed = 0.046m
* 2.311 rad/s = 0.106 m/s
Our OSV can travel at 0.106 m/s, which is
above the required speed of 0.0607 m/s
Peristaltic Pump Mechanism
Water Level Sensor
Module
Lynxmotion Gearhead Motor Performance Chart
Mission Results
Special thanks to Professor Valente for teaching us
everything we could need and more, to Alex for the constant
support, and peers for inspiring us to constantly improve.
Poster credits to Benjamin Grove
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