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In my lab, we build
autonomous aerial robots
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like the one you see flying here.
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Unlike the commercially available drones
that you can buy today,
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this robot doesn't have any GPS on board.
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So without GPS,
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it's hard for robots like this
to determine their position.
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This robot uses onboard sensors,
cameras and laser scanners,
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to scan the environment.
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It detects features from the environment,
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and it determines where it is
relative to those features,
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using a method of triangulation.
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And then it can assemble
all these features into a map,
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like you see behind me.
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And this map then allows the robot
to understand where the obstacles are
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and navigate in a collision-free manner.
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What I want to show you next,
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is a set of experiments
we did inside our laboratory,
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where this robot was able
to go for longer distances.
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So here you'll see on the top right,
what the robot sees with the camera.
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And on the main screen --
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and of course this is sped up
by a factor of four --
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on the main screen you'll see
the map that it's building.
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So this is a high-resolution map
of the corridor around our laboratory.
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And in a minute you'll see it
enter our lab,
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which is recognizable
by the clutter that you see.
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(Laughter)
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But the main point I want convey to you is
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that these robots are capable
of building high-resolution maps
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at five-centimeters resolution,
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allowing somebody who is outside the lab,
or outside the building
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to deploy these
without actually going inside,
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and trying to infer what happens
inside the building.
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Now there's one problem
with robots like this.
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The first problem is it's pretty big.
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Because it's big, it's heavy.
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And these robots consume
about 100 watts per pound.
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And this makes for a
very short mission life.
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The second problem
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is that these robots have onboard sensors
that end up being very expensive --
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a laser scanner, a camera
and the processors.
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That drives up the cost of this robot.
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So we asked ourselves a question:
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what consumer product
can you buy in an electronics store
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that is inexpensive, that's lightweight,
that has sensing onboard and computation?
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And we invented the flying phone.
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(Laughter)
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So this robot uses a Samsung Galaxy
smartphone that you can buy off the shelf,
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and all you need is an app that you
can download from our app store.
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And you can see this robot
reading the letters, "TED" in this case,
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looking at the corners
of the "T" and the "E"
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and then triangulating off of that,
flying autonomously.
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That joystick is just there to make sure
if the robot goes crazy,
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Giuseppe can kill it.
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(Laughter)
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In addition to building
these small robots,
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we also experiment with aggressive
behaviors, like you see here.
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So this robot is now traveling
at two to three meters per second,
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pitching and rolling aggressively
as it changes direction.
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The main point is we can have
smaller robots that can go faster
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and then travel in these
very unstructured environments.
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And in this next video,
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just like you see this bird, an eagle,
gracefully coordinating its wings,
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its eyes and feet to grab prey
out of the water,
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our robot can go fishing, too.
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(Laughter)
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In this case, this is a Philly cheesesteak
hoagie that it's grabbing out of thin air.
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(Laughter)
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So you can see this robot
going at about three meters per second,
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which is faster than walking speed,
coordinating its arms, its claws
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and its flight with split-second timing
to achieve this maneuver.
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In another experiment,
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I want to show you
how the robot adapts its flight
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to control its suspended payload,
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whose length is actually larger
than the width of the window.
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So in order to accomplish this,
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it actually has to pitch
and adjust the altitude
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and swing the payload through.
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But of course we want
to make these even smaller,
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and we're inspired
in particular by honeybees.
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So if you look at honeybees,
and this is a slowed-down video,
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they're so small,
the inertia is so lightweight,
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(Laughter)
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that they don't care --
they bounce off my hand, for example.
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This is a little robot
that mimics the honeybee behavior.
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And smaller is better,
because along with the small size
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you get lower inertia.
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Along with lower inertia,
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(Robot buzzing, laughter)
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along with lower inertia,
you're resistant to collisions.
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And that makes you more robust.
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So just like these honeybees,
we build small robots.
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And this particular one
is only 25 grams in weight.
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It consumes only six watts of power.
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And it can travel
up to six meters per second.
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So if I normalize that to its size,
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it's like a Boeing 787 traveling
ten times the speed of sound.
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(Laughter)
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And I want to show you an example.
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This is probably the first planned mid-air
collision, at one-twentieth normal speed.
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These are going at a relative speed
of two meters per second,
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and this illustrates the basic principle.
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The two-gram carbon fiber cage around it
prevents the propellers from entangling,
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but essentially the collision is absorbed
and the robot responds to the collisions.
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And so small also means safe.
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In my lab, as we developed these robots,
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we start off with these big robots and
then now down to these small robots.
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And if you plot a histogram
of the number of Band-Aids we've ordered
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in the past, that sort of tailed off now.
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Because these robots are really safe.
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The small size has some disadvantages,
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and nature has found a number of ways
to compensate for these disadvantages.
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The basic idea is they aggregate
to form large groups, or swarms.
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So, similarly, in our lab, we try
to create artificial robot swarms.
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And this is quite challenging
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because now you have to think
about networks of robots.
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And within each robot,
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you have to think about the interplay
of sensing, communication, computation --
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and this network then becomes
quite difficult to control and manage.
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So from nature we take away
three organizing principles
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that essentially allow us
to develop our algorithms.
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The first idea is that robots
need to be aware of their neighbors.
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They need to be able to sense
and communicate with their neighbors.
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So this video illustrates the basic idea.
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You have four robots --
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one of the robots has actually been
hijacked by a human operator, literally.
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But because the robots interact with
each other, they sense their neighbors,
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they essentially follow.
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And here there's a single person
able to lead this network of followers.
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So again, it's not because all the robots
know where they're supposed to go.
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It's because they're just reacting
to the positions of their neighbors.
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(Laughter)
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So the next experiment illustrates
the second organizing principle.
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And this principle has to do
with the principle of anonymity.
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Here the key idea is that
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the robots are agnostic
to the identities of their neighbors.
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They're asked to form a circular shape,
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and no matter how many robots
you introduce into the formation,
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or how many robots you pull out,
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each robot is simply
reacting to its neighbor.
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It's aware of the fact that it needs
to form the circular shape,
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but collaborating with its neighbors
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it forms the shape
without central coordination.
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Now if you put these ideas together,
the third idea
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is that we essentially give these
robots mathematical descriptions
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of the shape they need to execute.
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And these shapes can be varying
as a function of time,
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and you'll see these robots start
from a circular formation,
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change into a rectangular formation,
stretch into a straight line,
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back into an ellipse.
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And they do this with the same
kind of split-second coordination
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that you see in natural swarms, in nature.
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So why work with swarms?
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Let me tell you about two applications
that we are very interested in.
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The first one has to do with agriculture,
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which is probably the biggest problem
that we're facing worldwide.
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As you well know,
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one in every seven persons
in this earth is malnourished.
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Most of the land that we can cultivate
has already been cultivated.
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And the efficiency of most systems
in the world is improving,
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but our production system
efficiency is actually declining.
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And that's mostly because of water
shortage, crop diseases, climate change
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and a couple of other things.
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So what can robots do?
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Well, we adopt an approach that's
called precision farming in the community.
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And the basic idea is that we fly
aerial robots through orchards,
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and then we build
precision models of individual plants.
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So just like personalized medicine,
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while you might imagine wanting
to treat every patient individually,
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what we'd like to do is build
models of individual plants
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and then tell the farmer what kind
of inputs every plant needs --
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the inputs in this case being water,
fertilizer and pesticide.
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Here you'll see robots traveling
through an apple orchard,
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and in a minute you'll see
two of its companions
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doing the same thing on the left side.
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And what they're doing is essentially
building a map of the orchard.
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Within the map is a map
of every plant in this orchard.
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(Robot buzzing)
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Let's see what those maps look like.
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In the next video, you'll see the cameras
that are being used on this robot.
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On the top-eft is essentially
a standout color camera.
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On the left-center is an infrared camera.
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And on the bottom-left
is a thermal camera.
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And on the main panel, you're seeing
a three-dimensional reconstruction
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of every tree in the orchard
as the sensors fly right past the trees.
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Armed with information like this,
we can do several things.
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The first and possibly the most important
thing we can do is very simple:
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count the number of fruits on every tree.
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By doing this, you tell the farmer
how many fruits she has in every tree
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and allow her to estimate
the yield in the orchard,
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optimizing the production
chain downstream.
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The second thing we can do
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is take models of plants, construct
three-dimensional reconstructions,
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and from that estimate the canopy size,
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and then correlate the canopy size
to the amount of leaf area on every plant.
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And this is called the leaf area index.
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So if you know this leaf area index,
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you essentially have a measure of how much
photosynthesis is possible in every plant
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which again tells you
how healthy each plant is.
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Combining visual and infrared information,
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we can also compute indices such as NDVI.
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And in this particular case,
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you essentially see there are some crops
that are doing as well as other crops.
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This is easily discernible from imagery,
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not just visual imagery but combining
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both visual imagery and infrared imagery.
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And then lastly,
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one thing we're interested in doing is
detecting the early onset of chlorosis --
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and this is an orange tree --
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which is essentially seen
by yellowing of leaves.
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But robots flying overhead
can easily spot this autonomously
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and then report to the farmer
that he or she has a problem
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in this section of the orchard.
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Systems like this can really help,
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and we're projecting yields
can improve by about ten percent
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and, more importantly, decrease
the amount of inputs such as water
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by 25 percent by using
aerial robot swarms.
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Lastly, I want you to applaud
the people who actually create the future,
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Yash Mulgaonkar, Sikang Liu
and Giuseppe Lorianno,
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who are responsible for the
three demonstrations that you saw.
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Thank you.
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(Applause)
Brian Greene
A correction was made to this transcript on 1/15/16.
At 10:25, the subtitle now reads: "On the top-left is essentially a standard color camera."