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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 to convey to you
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is 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,
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because along with the small size 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
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and then now we're 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,
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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,
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the third idea is that we essentially give these robots
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mathematical descriptions 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-left 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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By 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, you can essentially see
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there are some crops that are not 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 that 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 Loianno,
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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)