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  • In my lab, we build autonomous aerial robots

  • like the one you see flying here.

  • Unlike the commercially available drones that you can buy today,

  • this robot doesn't have any GPS on board.

  • So without GPS,

  • it's hard for robots like this to determine their position.

  • This robot uses onboard sensors, cameras and laser scanners,

  • to scan the environment.

  • It detects features from the environment,

  • and it determines where it is relative to those features,

  • using a method of triangulation.

  • And then it can assemble all these features into a map,

  • like you see behind me.

  • And this map then allows the robot to understand where the obstacles are

  • and navigate in a collision-free manner.

  • What I want to show you next

  • is a set of experiments we did inside our laboratory,

  • where this robot was able to go for longer distances.

  • So here you'll see, on the top right, what the robot sees with the camera.

  • And on the main screen...

  • And of course this is sped up by a factor of four...

  • On the main screen you'll see the map that it's building.

  • So this is a high-resolution map of the corridor around our laboratory.

  • And in a minute you'll see it enter our lab,

  • which is recognizable by the clutter that you see.

  • (Laughter)

  • But the main point I want to convey to you

  • is that these robots are capable of building high-resolution maps

  • at five centimeters resolution,

  • allowing somebody who is outside the lab, or outside the building

  • to deploy these without actually going inside,

  • and trying to infer what happens inside the building.

  • Now there's one problem with robots like this.

  • The first problem is it's pretty big.

  • Because it's big, it's heavy.

  • And these robots consume about 100 watts per pound.

  • And this makes for a very short mission life.

  • The second problem

  • is that these robots have onboard sensors that end up being very expensive...

  • A laser scanner, a camera and the processors.

  • That drives up the cost of this robot.

  • So we asked ourselves a question:

  • what consumer product can you buy in an electronics store

  • that is inexpensive, that's lightweight, that has sensing onboard and computation?

  • And we invented the flying phone.

  • (Laughter)

  • So this robot uses a Samsung Galaxy smartphone that you can buy off the shelf,

  • and all you need is an app that you can download from our app store.

  • And you can see this robot reading the letters, "TED" in this case,

  • looking at the corners of the "T" and the "E"

  • and then triangulating off of that, flying autonomously.

  • That joystick is just there to make sure if the robot goes crazy,

  • Giuseppe can kill it.

  • (Laughter)

  • In addition to building these small robots,

  • we also experiment with aggressive behaviors, like you see here.

  • So this robot is now traveling at two to three meters per second,

  • pitching and rolling aggressively as it changes direction.

  • The main point is we can have smaller robots that can go faster

  • and then travel in these very unstructured environments.

  • And in this next video,

  • just like you see this bird, an eagle, gracefully coordinating its wings,

  • its eyes and feet to grab prey out of the water,

  • our robot can go fishing, too.

  • (Laughter)

  • In this case, this is a Philly cheesesteak hoagie that it's grabbing out of thin air.

  • (Laughter)

  • So you can see this robot going at about three meters per second,

  • which is faster than walking speed, coordinating its arms, its claws

  • and its flight with split-second timing to achieve this maneuver.

  • In another experiment,

  • I want to show you how the robot adapts its flight

  • to control its suspended payload,

  • whose length is actually larger than the width of the window.

  • So in order to accomplish this,

  • it actually has to pitch and adjust the altitude

  • and swing the payload through.

  • But of course we want to make these even smaller,

  • and we're inspired in particular by honeybees.

  • So if you look at honeybees, and this is a slowed down video,

  • they're so small, the inertia is so lightweight...

  • (Laughter)

  • that they don't care...

  • They bounce off my hand, for example.

  • This is a little robot that mimics the honeybee behavior.

  • And smaller is better,

  • because along with the small size you get lower inertia.

  • Along with lower inertia...

  • (Robot buzzing, laughter)

  • along with lower inertia, you're resistant to collisions.

  • And that makes you more robust.

  • So just like these honeybees, we build small robots.

  • And this particular one is only 25 grams in weight.

  • It consumes only six watts of power.

  • And it can travel up to six meters per second.

  • So if I normalize that to its size,

  • it's like a Boeing 787 traveling ten times the speed of sound.

  • (Laughter)

  • And I want to show you an example.

  • This is probably the first planned mid-air collision, at one-twentieth normal speed.

  • These are going at a relative speed of two meters per second,

  • and this illustrates the basic principle.

  • The two-gram carbon fiber cage around it prevents the propellers from entangling,

  • but essentially the collision is absorbed and the robot responds to the collisions.

  • And so small also means safe.

  • In my lab, as we developed these robots,

  • we start off with these big robots

  • and then now we're down to these small robots.

  • And if you plot a histogram of the number of Band-Aids we've ordered

  • in the past, that sort of tailed off now.

  • Because these robots are really safe.

  • The small size has some disadvantages,

  • and nature has found a number of ways to compensate for these disadvantages.

  • The basic idea is they aggregate to form large groups, or swarms.

  • So, similarly, in our lab, we try to create artificial robot swarms.

  • And this is quite challenging

  • because now you have to think about networks of robots.

  • And within each robot,

  • you have to think about the interplay of sensing, communication, computation...

  • And this network then becomes quite difficult to control and manage.

  • So from nature we take away three organizing principles

  • that essentially allow us to develop our algorithms.

  • The first idea is that robots need to be aware of their neighbors.

  • They need to be able to sense and communicate with their neighbors.

  • So this video illustrates the basic idea.

  • You have four robots...

  • One of the robots has actually been hijacked by a human operator, literally.

  • But because the robots interact with each other,

  • they sense their neighbors,

  • they essentially follow.

  • And here there's a single person able to lead this network of followers.

  • So again, it's not because all the robots know where they're supposed to go.

  • It's because they're just reacting to the positions of their neighbors.

  • (Laughter)

  • So the next experiment illustrates the second organizing principle.

  • And this principle has to do with the principle of anonymity.

  • Here the key idea is that

  • the robots are agnostic to the identities of their neighbors.

  • They're asked to form a circular shape,

  • and no matter how many robots you introduce into the formation,

  • or how many robots you pull out,

  • each robot is simply reacting to its neighbor.

  • It's aware of the fact that it needs to form the circular shape,

  • but collaborating with its neighbors

  • it forms the shape without central coordination.

  • Now if you put these ideas together,

  • the third idea is that we essentially give these robots

  • mathematical descriptions of the shape they need to execute.

  • And these shapes can be varying as a function of time,

  • and you'll see these robots start from a circular formation,

  • change into a rectangular formation, stretch into a straight line,

  • back into an ellipse.

  • And they do this with the same kind of split-second coordination

  • that you see in natural swarms, in nature.

  • So why work with swarms?

  • Let me tell you about two applications that we are very interested in.

  • The first one has to do with agriculture,

  • which is probably the biggest problem that we're facing worldwide.

  • As you well know,

  • one in every seven persons in this earth is malnourished.

  • Most of the land that we can cultivate has already been cultivated.

  • And the efficiency of most systems in the world is improving,

  • but our production system efficiency is actually declining.

  • And that's mostly because of water shortage, crop diseases, climate change

  • and a couple of other things.

  • So what can robots do?

  • Well, we adopt an approach that's called Precision Farming in the community.

  • And the basic idea is that we fly aerial robots through orchards,

  • and then we build precision models of individual plants.

  • So just like personalized medicine,

  • while you might imagine wanting to treat every patient individually,

  • what we'd like to do is build models of individual plants

  • and then tell the farmer what kind of inputs every plant needs...

  • The inputs in this case being water, fertilizer and pesticide.

  • Here you'll see robots traveling through an apple orchard,

  • and in a minute you'll see two of its companions

  • doing the same thing on the left side.

  • And what they're doing is essentially building a map of the orchard.

  • Within the map is a map of every plant in this orchard.

  • (Robot buzzing)

  • Let's see what those maps look like.

  • In the next video, you'll see the cameras that are being used on this robot.

  • On the top-left is essentially a standard color camera.

  • On the left-center is an infrared camera.

  • And on the bottom-left is a thermal camera.

  • And on the main panel, you're seeing a three-dimensional reconstruction

  • of every tree in the orchard as the sensors fly right past the trees.

  • Armed with information like this, we can do several things.

  • The first and possibly the most important thing we can do is very simple:

  • count the number of fruits on every tree.

  • By doing this, you tell the farmer how many [fruits] she has in every tree

  • and allow her to estimate the yield in the orchard,

  • optimizing the production chain downstream.

  • The second thing we can do

  • is take models of plants, construct three-dimensional reconstructions,

  • and from that estimate the canopy size,

  • and then correlate the canopy size to the amount of leaf area on every plant.

  • And this is called the leaf area index.

  • So if you know this leaf area index,

  • you essentially have a measure of how much photosynthesis is possible in every plant,

  • which again tells you how healthy each plant is.

  • By combining visual and infrared information,

  • we can also compute indices such as NDVI.

  • And in this particular case, you can essentially see

  • there are some crops that are not doing as well as other crops.

  • This is easily discernible from imagery,

  • not just visual imagery but combining

  • both visual imagery and infrared imagery.

  • And then lastly,

  • one thing we're interested in doing is detecting the early onset of chlorosis...

  • And this is an orange tree...

  • Which is essentially seen by yellowing of leaves.

  • But robots flying overhead can easily spot this autonomously

  • and then report to the farmer that he or she has a problem

  • in this section of the orchard.

  • Systems like this can really help,

  • and we're projecting yields that can improve by about ten percent

  • and, more importantly, decrease the amount of inputs such as water

  • by 25 percent by using aerial robot swarms.

  • A second application area is in first response.

  • This is a picture of the Penn campus,

  • actually south of the Penn campus, the South Bank.

  • I want you to imagine a setting

  • where there might be an emergency call from a building,

  • a 911 call.

  • By the time the police get there, it might take a valuable 5-10 minutes.

  • But imagine now, robots respond.

  • And you have a whole swarm of them,

  • flying to the scene autonomously, just triggered by a 911 call

  • or by a dispacher.

  • By the way, if someone is here from the FAA,

  • this was actually shot in South America.

  • (Laughter)

  • So, robots arrive at the scene,

  • and they're all equipped with downward facing cameras,

  • and they can monitor the scene.

  • And they do this autonomously,

  • so by the time a human first responder or a police officer gets there,

  • they have access to all kinds of information.

  • So on the top left, you see the central screen

  • that a dispacher might see,

  • which is telling her where the robots are flying

  • and how they're encircling the building.

  • And the robots are autonomously deciding which ingress poins

  • should be assigned to what robot.

  • On the top right, you essentialy see images from the robots

  • being assembled into a mosaic.

  • Which again, gives the first responder some idea