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This class is about
autonomous robotics.
The competition today,
we've got several teams
with different Turtebots.
And so, we've been tasked
to explore the neighborhood
and look for animals that
might be in need of rescue.
These robots are small, but they
comprise most of the sensors
that actually you would see
on real self-driving car.
One thing that
I'll have to do is
use a LIDAR, which is this
kind of a spinning laser on top
to map its environment.
The robot will figure out a way
to navigate throughout the map
without hitting anything.
At the same time while
its building up the map,
it's also localizing
the animals.
So it's using the cameras
and machine learning
to determine what
objects are what,
figure out which
ones are animals
and what are trees and people.
We made sure that the base
requirement of the project
were kind of small
and that everyone
could tackle them but then give
time to students to develop
their own extensions.
So some teams are able
to react intelligently
to a bicycle crossing the road.
Some other teams will be
extremely efficient in the way
that they'll choose which
animal to go rescue.
Of course, we're not dealing
with all the complexity
of a real self-driving car,
but the point to discuss
is to make that students know
all the key technical aspects.
It's definitely given
me a healthy dose
of respect of what real
autonomous cars have to do.
Here, it's funny when we mess
up and misclassify something,
but in a real world
situation, there
can't be any of
these mistakes or any
of these miscalculations.
I knew a little bit
about everything
before but being able to
implement it on a real robot
and integrating
all of these ideas,
putting them all together
has been really fun.
[APPLAUSE]
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