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