Robots that fly … and cooperate | Vijay Kumar

Robots that fly … and cooperate | Vijay Kumar

Good morning. I’m here today to talk
about autonomous flying beach balls. (Laughter) No, agile aerial robots like this one. I’d like to tell you a little bit
about the challenges in building these, and some of the terrific opportunities
for applying this technology. So these robots are related
to unmanned aerial vehicles. However, the vehicles
you see here are big. They weigh thousands of pounds,
are not by any means agile. They’re not even autonomous. In fact, many of these vehicles
are operated by flight crews that can include multiple pilots, operators of sensors, and mission coordinators. What we’re interested in
is developing robots like this — and here are two other pictures — of robots that you can buy off the shelf. So these are helicopters with four rotors, and they’re roughly
a meter or so in scale, and weigh several pounds. And so we retrofit these
with sensors and processors, and these robots can fly indoors. Without GPS. The robot I’m holding in my hand is this one, and it’s been created by two students, Alex and Daniel. So this weighs a little more
than a tenth of a pound. It consumes about 15 watts of power. And as you can see,
it’s about eight inches in diameter. So let me give you
just a very quick tutorial on how these robots work. So it has four rotors. If you spin these rotors
at the same speed, the robot hovers. If you increase the speed
of each of these rotors, then the robot flies up,
it accelerates up. Of course, if the robot were tilted, inclined to the horizontal, then it would accelerate
in this direction. So to get it to tilt, there’s one of two ways of doing it. So in this picture, you see
that rotor four is spinning faster and rotor two is spinning slower. And when that happens, there’s a moment that causes
this robot to roll. And the other way around, if you increase the speed of rotor three
and decrease the speed of rotor one, then the robot pitches forward. And then finally, if you spin opposite pairs of rotors faster than the other pair, then the robot yaws
about the vertical axis. So an on-board processor essentially looks at what motions
need to be executed and combines these motions, and figures out what commands
to send to the motors — 600 times a second. That’s basically how this thing operates. So one of the advantages of this design is when you scale things down, the robot naturally becomes agile. So here, R is the characteristic
length of the robot. It’s actually half the diameter. And there are lots of physical parameters
that change as you reduce R. The one that’s most important
is the inertia, or the resistance to motion. So it turns out the inertia,
which governs angular motion, scales as a fifth power of R. So the smaller you make R, the more dramatically the inertia reduces. So as a result, the angular acceleration, denoted by the Greek letter alpha here, goes as 1 over R. It’s inversely proportional to R. The smaller you make it,
the more quickly you can turn. So this should be clear in these videos. On the bottom right, you see a robot
performing a 360-degree flip in less than half a second. Multiple flips, a little more time. So here the processes on board are getting feedback from accelerometers
and gyros on board, and calculating, like I said before, commands at 600 times a second, to stabilize this robot. So on the left, you see Daniel
throwing this robot up into the air, and it shows you
how robust the control is. No matter how you throw it, the robot recovers and comes back to him. So why build robots like this? Well, robots like this
have many applications. You can send them
inside buildings like this, as first responders to look for intruders, maybe look for biochemical leaks, gaseous leaks. You can also use them
for applications like construction. So here are robots carrying beams, columns and assembling cube-like structures. I’ll tell you a little bit
more about this. The robots can be used
for transporting cargo. So one of the problems
with these small robots is their payload-carrying capacity. So you might want to have
multiple robots carry payloads. This is a picture of a recent
experiment we did — actually not so recent anymore — in Sendai, shortly after the earthquake. So robots like this could be sent
into collapsed buildings, to assess the damage
after natural disasters, or sent into reactor buildings, to map radiation levels. So one fundamental problem
that the robots have to solve if they are to be autonomous, is essentially figuring out how to get
from point A to point B. So this gets a little challenging, because the dynamics of this robot
are quite complicated. In fact, they live
in a 12-dimensional space. So we use a little trick. We take this curved 12-dimensional space, and transform it into a flat,
four-dimensional space. And that four-dimensional space
consists of X, Y, Z, and then the yaw angle. And so what the robot does, is it plans what we call
a minimum-snap trajectory. So to remind you of physics: You have position, derivative, velocity; then acceleration; and then comes jerk, and then comes snap. So this robot minimizes snap. So what that effectively does, is produce a smooth and graceful motion. And it does that avoiding obstacles. So these minimum-snap trajectories
in this flat space are then transformed back into this complicated
12-dimensional space, which the robot must do
for control and then execution. So let me show you some examples of what these minimum-snap
trajectories look like. And in the first video, you’ll see the robot going
from point A to point B, through an intermediate point. (Whirring noise) So the robot is obviously capable
of executing any curve trajectory. So these are circular trajectories, where the robot pulls about two G’s. Here you have overhead
motion capture cameras on the top that tell the robot where it is
100 times a second. It also tells the robot
where these obstacles are. And the obstacles can be moving. And here, you’ll see Daniel
throw this hoop into the air, while the robot is calculating
the position of the hoop, and trying to figure out how to best
go through the hoop. So as an academic, we’re always trained to be able
to jump through hoops to raise funding for our labs, and we get our robots to do that. (Applause) So another thing the robot can do is it remembers pieces of trajectory that it learns or is pre-programmed. So here, you see the robot combining
a motion that builds up momentum, and then changes its orientation
and then recovers. So it has to do this
because this gap in the window is only slightly larger
than the width of the robot. So just like a diver
stands on a springboard and then jumps off it to gain momentum, and then does this pirouette,
this two and a half somersault through and then gracefully recovers, this robot is basically doing that. So it knows how to combine
little bits and pieces of trajectories to do these fairly difficult tasks. So I want change gears. So one of the disadvantages
of these small robots is its size. And I told you earlier that we may want to employ
lots and lots of robots to overcome the limitations of size. So one difficulty is: How do you coordinate
lots of these robots? And so here, we looked to nature. So I want to show you a clip
of Aphaenogaster desert ants, in Professor Stephen Pratt’s lab,
carrying an object. So this is actually a piece of fig. Actually you take any object
coated with fig juice, and the ants will carry it
back to the nest. So these ants don’t have
any central coordinator. They sense their neighbors. There’s no explicit communication. But because they sense the neighbors and because they sense the object, they have implicit coordination
across the group. So this is the kind of coordination
we want our robots to have. So when we have a robot
which is surrounded by neighbors — and let’s look at robot I and robot J — what we want the robots to do, is to monitor the separation between them, as they fly in formation. And then you want to make sure that this separation
is within acceptable levels. So again, the robots monitor this error and calculate the control commands
100 times a second, which then translates into motor commands, 600 times a second. So this also has to be done
in a decentralized way. Again, if you have
lots and lots of robots, it’s impossible to coordinate
all this information centrally fast enough in order for the robots
to accomplish the task. Plus, the robots have to base
their actions only on local information — what they sense from their neighbors. And then finally, we insist that the robots be agnostic
to who their neighbors are. So this is what we call anonymity. So what I want to show you next
is a video of 20 of these little robots, flying in formation. They’re monitoring
their neighbors’ positions. They’re maintaining formation. The formations can change. They can be planar formations, they can be three-dimensional formations. As you can see here, they collapse from a three-dimensional
formation into planar formation. And to fly through obstacles, they can adapt the formations on the fly. So again, these robots come
really close together. As you can see
in this figure-eight flight, they come within inches of each other. And despite the aerodynamic interactions
with these propeller blades, they’re able to maintain stable flight. (Applause) So once you know how to fly in formation, you can actually pick up
objects cooperatively. So this just shows that we can
double, triple, quadruple the robots’ strength, by just getting them to team
with neighbors, as you can see here. One of the disadvantages of doing that is,
as you scale things up — so if you have lots of robots
carrying the same thing, you’re essentially increasing the inertia, and therefore you pay a price;
they’re not as agile. But you do gain in terms
of payload-carrying capacity. Another application I want to show you —
again, this is in our lab. This is work done by Quentin Lindsey,
who’s a graduate student. So his algorithm essentially
tells these robots how to autonomously build cubic structures from truss-like elements. So his algorithm tells the robot
what part to pick up, when, and where to place it. So in this video you see — and it’s sped up 10, 14 times — you see three different structures
being built by these robots. And again, everything is autonomous, and all Quentin has to do is to give them a blueprint
of the design that he wants to build. So all these experiments
you’ve seen thus far, all these demonstrations, have been done with the help
of motion-capture systems. So what happens when you leave your lab, and you go outside into the real world? And what if there’s no GPS? So this robot is actually
equipped with a camera, and a laser rangefinder, laser scanner. And it uses these sensors
to build a map of the environment. What that map consists of are features — like doorways, windows,
people, furniture — and it then figures out
where its position is, with respect to the features. So there is no global coordinate system. The coordinate system
is defined based on the robot, where it is and what it’s looking at. And it navigates with respect
to those features. So I want to show you a clip of algorithms developed by Frank Shen
and Professor Nathan Michael, that shows this robot entering
a building for the very first time, and creating this map on the fly. So the robot then figures out
what the features are, it builds the map, it figures out where it is
with respect to the features, and then estimates its position
100 times a second, allowing us to use the control algorithms
that I described to you earlier. So this robot is actually being
commanded remotely by Frank, but the robot can also figure out
where to go on its own. So suppose I were to send
this into a building, and I had no idea
what this building looked like. I can ask this robot to go in, create a map, and then come back and tell me
what the building looks like. So here, the robot is not
only solving the problem of how to go from point A
to point B in this map, but it’s figuring out what the best
point B is at every time. So essentially it knows where to go to look for places that have
the least information, and that’s how it populates this map. So I want to leave you
with one last application. And there are many applications
of this technology. I’m a professor, and we’re
passionate about education. Robots like this can really change
the way we do K-12 education. But we’re in Southern California, close to Los Angeles, so I have to conclude with something
focused on entertainment. I want to conclude with a music video. I want to introduce the creators,
Alex and Daniel, who created this video. (Applause) So before I play this video, I want to tell you that they created it
in the last three days, after getting a call from Chris. And the robots that play in the video
are completely autonomous. You will see nine robots
play six different instruments. And of course, it’s made
exclusively for TED 2012. Let’s watch. (Sound of air escaping from valve) (Music) (Whirring sound) (Music) (Applause) (Cheers)

100 thoughts on “Robots that fly … and cooperate | Vijay Kumar”

  1. Pure wonder, the "simplicity" of ingenious innovation. This is, quite literally, the most I've been impressed in a very long time…
    I've seen this before, but I'm so happy to find out that this is available on YouTube as well

  2. Nice video but there is a mistake in French translation a tenth of a pound is not equal 20 kg.
    A tenth of a pound is 45g

  3. That was awesome. I would like this team to submit this video or an updated version of it for the Las Vegas Drone Film Festival on June 10-12, 2016. Please contact me. Larry Stockett, Director LVDFF [email protected]

  4. Amazing . Vijay and team has taken robotics to newer levels . I think this can be deployed in many life saving situations .

  5. 5:20 the usual dishonesty w/ respect to applications. what about policing, surveillance, targeted assassination, biochemical weapon delivery, sabotage, etc, etc…

  6. 대단한것처럼 보이지만,,젼혀,,,
    참우스운게,, 공기가 없으면 저 구석기 원시적인 프로펠러는 무용지물 이고 드론은 뜰수없음,,

  7. It is not AI so stop saying Terminator and all AI must have the 3 laws of Artificial Intelligence programmed in to its main core processor. The 3 laws are it may not harm a human. 2 It must defend its self as long as it does not conflict Law1. It must follow instruction as long as it does not conflict law 1 or 2.

  8. that robot could be a very very deadly killing machine. not only because of it is so fast and so little, also because it is able to fly around its target or fly behind it…….

  9. The first thing I look into any robot is responsiveness most of machine we call them robot are extremly slow and will not capable of getting things done.
    So this one interested me.


  11. How long it can fly perpendicular to earth. it pass through the wooden block of in cut .if the block is more thicker it would collapse

  12. Even as sophisticated as they will get. Time will turn them into doing the same thing as bees do, like crashing and bumping. Clumsy flight.
    It's how everything gets. Builds to a crescendo of complexity than realized it will never be perfect so starts building bumpers for all the preplanned problems it will run into.
    Because who will work on one project for infinity without dismay which eventually leads to abandonment.

    Then again, bees could be worn out or tired of the routine and are thinking ahead instead of the now.
    Maybe the thought of animals is more complex than humans. What with not having news to tell them about traffic pollution habitat destruction…but I'm getting off subject.

  13. It is Allah who erected the heavens without pillars that you [can] see. (The Noble Quran. Surah Ar Rad. Verse 2)
    And the heaven We constructed with strength, and indeed, We are [its] expander. (Surah Adh Dhariyat. Verse 47)
    And it is He who created the night and the day and the sun and the moon; all [heavenly bodies] in an orbit are swimming. (Al Anbya. Verse 33)
    Have they not seen that We set upon the land, reducing it from its borders? (Surah Ar Rad. Verse 41)
    And you see the mountains, thinking them rigid, while they will pass as the passing of clouds. [It is] the work of Allah, who perfected all things. (Suran An Naml Verse 88)
    It is Allah who has created seven heavens and of the earth, the like of them. (Surah Talaq. Verse 12)

    Perhaps in the future irradiation (teleportation) may be possible. There were many technologically advanced people in the old tribes. But they were destroyed because of their sins. Here's proof.
    [Solomon] said, "O assembly [of jinn], which of you will bring me her throne before they come to me in submission?"
    A powerful one from among the jinn said, "I will bring it to you before you rise from your place, and indeed, I am for this [task] strong and trustworthy."
    Said one who had knowledge from the Scripture, "I will bring it to you before your glance returns to you. (The Noble Quran. Surah An Naml. Verse 38-40)

    Listen to the voice of the Noble Quran.
    Maybe your life will be changed If God allows.
    Really amazing
    French :

  14. thats just crap robots playing instruments thats bullshit music is all about feeling and living it if robots were meant to play the instruments so tell me why we are able to play them brilliantly we are humans not machines music is all about feeling so please stop eliminating humans from everything

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  16. is there a site of this engineer where we can learn how to make this robot,we are suppose to make a project in our college and would love to make the drone with my group.


  18. The down side is the flight time. In order to gain more flight time you need to increase the battery capacity, and with that you also increase the copter weigh.

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