raspberrypi.org/magpi The Official Raspberry Pi Projects Book 85
C-TURTLE Projects
>STEP-01
Laser-cut the layers
The cardboard layers are laser cut. For each of the
five layers (two cardboard, two adhesive, and one foil),
holes are cut in specific locations to allow hinges to be
fitted later.>STEP-02
Begin the lamination
Once the layers are cut, they are laminated together
to form a single layered sheet using a heating press.>STEP-03
Ready for assembly
The shapes of the individual parts are cut from the
laminated sheet. The holes are mounting holes,
designed to be used with rivets.CREATE THE
C-TURTLE’S BODY
Below Tests are ongoing to find out
how well the robot, and the Pi, cope in
extreme temperatures
“We envisioned a system where
each robot can carry sensors to
detect and mark land-mines, but
also where the loss of a single
robot is relatively inconsequential
for demining operations, thus
reducing the risk for humans
or bigger demining robots,”
explains Kevin. During the design
process, some key decisions
were made. They ruled out using
wheels – “they usually have issues
with slippage on sand, and they
would create a more complex
manufacturing process,” says
Kevin – and were unanimous in
wanting to use a Raspberry Pi Zero.
Lightweight connectivity
“The Pi felt perfect,” Kevin
continues. “We not only wanted
the ability to send commands to
the robot via WLAN, but also to
perform simple data processing
and machine learning directly
on the robot – a requirement for
using multiple robots in a fully
autonomous fleet. The Zero also
requires relatively little power.
Because of that, we’re exploring
the possibility of using solar panels
for recharging batteries during
the daytime.”
Kevin and Joseph have worked
on an algorithm which allows the
turtle bot to adapt its crawling
technique. “The whole code
infrastructure on the turtle robot,
from motor control to the joint
server and sensor collections, was
written in Python,” Kevin reveals.
“We used TCP/IP connections
to send joint commands to the
robot and also to collect data
for evaluation.”Real-world learning
This was put to the test when
they drove out into the desert
with their first prototypes.
“We got a real-time feed of
what was happening with our
robot, and were able to test and
debug different variations of
the learning scenarios,” Kevin
tells us. By using trial-and-error
learning, the robot gets good
and bad feedback which enables
it to develop.
Through this process, the robot
has managed to work out effective
trajectories over poppy seeds as
well as sand, but the scientists
are continuing to refine the
technology and their ambitions
remain high. “We’d like to take
the robots into space, too,” says
Kevin. “It would be fantastic to
use them to explore Mars.”