The astounding athletic power of quadcopters
-
0:12 - 0:14So what does it mean
for a machine to be athletic? -
0:15 - 0:18We will demonstrate the concept
of machine athleticism -
0:18 - 0:20and the research to achieve it
-
0:20 - 0:22with the help of these flying machines
called quadrocopters, -
0:23 - 0:24or quads, for short.
-
0:26 - 0:28Quads have been around for a long time.
-
0:29 - 0:32They're so popular these days
because they're mechanically simple. -
0:32 - 0:35By controlling
the speeds of these four propellers, -
0:35 - 0:37these machines can roll, pitch, yaw,
-
0:37 - 0:40and accelerate
along their common orientation. -
0:40 - 0:43On board are also a battery, a computer,
-
0:43 - 0:45various sensors and wireless radios.
-
0:47 - 0:51Quads are extremely agile,
but this agility comes at a cost. -
0:52 - 0:54They are inherently unstable,
-
0:54 - 0:56and they need some form
of automatic feedback control -
0:56 - 0:58in order to be able to fly.
-
1:04 - 1:07So, how did it just do that?
-
1:07 - 1:09Cameras on the ceiling and a laptop
-
1:09 - 1:12serve as an indoor
global positioning system. -
1:12 - 1:14It's used to locate objects in the space
-
1:14 - 1:16that have these reflective
markers on them. -
1:16 - 1:19This data is then sent to another laptop
-
1:19 - 1:21that is running estimation
and control algorithms, -
1:21 - 1:23which in turn sends commands to the quad,
-
1:23 - 1:26which is also running estimation
and control algorithms. -
1:30 - 1:32The bulk of our research is algorithms.
-
1:32 - 1:35It's the magic that brings
these machines to life. -
1:36 - 1:40So how does one design the algorithms
that create a machine athlete? -
1:41 - 1:43We use something broadly
called model-based design. -
1:44 - 1:45We first capture the physics
-
1:45 - 1:48with a mathematical model
of how the machines behave. -
1:49 - 1:53We then use a branch of mathematics
called control theory -
1:53 - 1:54to analyze these models
-
1:54 - 1:57and also to synthesize
algorithms for controlling them. -
1:58 - 2:01For example, that's how we can
make the quad hover. -
2:01 - 2:04We first captured the dynamics
with a set of differential equations. -
2:04 - 2:08We then manipulate these equations
with the help of control theory -
2:08 - 2:11to create algorithms
that stabilize the quad. -
2:11 - 2:13Let me demonstrate
the strength of this approach. -
2:17 - 2:20Suppose that we want
this quad to not only hover -
2:20 - 2:22but to also balance this pole.
-
2:23 - 2:24With a little bit of practice,
-
2:24 - 2:27it's pretty straightforward
for a human being to do this, -
2:27 - 2:30although we do have the advantage
of having two feet on the ground -
2:30 - 2:32and the use of our very versatile hands.
-
2:33 - 2:36It becomes a little bit more difficult
-
2:36 - 2:38when I only have one foot on the ground
-
2:38 - 2:39and when I don't use my hands.
-
2:41 - 2:43Notice how this pole has
a reflective marker on top, -
2:43 - 2:46which means that it can
be located in the space. -
2:52 - 2:53(Audience) Oh!
-
2:53 - 2:55(Applause)
-
2:58 - 2:59(Applause ends)
-
2:59 - 3:02You can notice that this quad
is making fine adjustments -
3:02 - 3:03to keep the pole balanced.
-
3:04 - 3:06How did we design
the algorithms to do this? -
3:07 - 3:09We added the mathematical
model of the pole -
3:09 - 3:11to that of the quad.
-
3:11 - 3:14Once we have a model
of the combined quad-pole system, -
3:14 - 3:17we can use control theory to create
algorithms for controlling it. -
3:19 - 3:20Here, you see that it's stable,
-
3:20 - 3:23and even if I give it little nudges,
-
3:23 - 3:24it goes back...
-
3:26 - 3:27to the nice, balanced position.
-
3:28 - 3:29We can also augment the model
-
3:30 - 3:32to include where we want
the quad to be in space. -
3:32 - 3:35Using this pointer,
made out of reflective markers, -
3:35 - 3:38I can point to where I want
the quad to be in space -
3:38 - 3:39a fixed distance away from me.
-
3:48 - 3:50(Laughter)
-
3:56 - 3:59The key to these acrobatic
maneuvers is algorithms, -
3:59 - 4:01designed with the help
of mathematical models -
4:01 - 4:02and control theory.
-
4:03 - 4:05Let's tell the quad to come back here
-
4:05 - 4:07and let the pole drop,
-
4:07 - 4:09and I will next demonstrate the importance
-
4:09 - 4:11of understanding physical models
-
4:11 - 4:13and the workings of the physical world.
-
4:25 - 4:29Notice how the quad lost altitude
when I put this glass of water on it. -
4:29 - 4:31Unlike the balancing pole,
-
4:31 - 4:34I did not include
the mathematical of the glass -
4:34 - 4:35in the system.
-
4:35 - 4:38In fact, the system doesn't even know
that the glass is there. -
4:38 - 4:41Like before, I could use
the pointer to tell the quad -
4:41 - 4:42where I want it to be in space.
-
4:46 - 4:47(Applause)
-
4:52 - 4:53(Applause ends)
-
4:54 - 4:55Okay, you should be asking yourself,
-
4:55 - 4:57why doesn't the water
fall out of the glass? -
4:58 - 4:59Two facts.
-
4:59 - 5:03The first is that gravity acts
on all objects in the same way. -
5:03 - 5:05The second is that the propellers
-
5:05 - 5:08are all pointing in the same direction
of the glass, pointing up. -
5:09 - 5:10You put these two things together,
-
5:10 - 5:14the net result is that all side forces
on the glass are small -
5:14 - 5:16and are mainly dominated
by aerodynamic effects, -
5:16 - 5:18which at these speeds are negligible.
-
5:23 - 5:26And that's why you don't need
to model the glass. -
5:26 - 5:28It naturally doesn't spill,
no matter what the quad does. -
5:32 - 5:35(Audience) Oh!
-
5:38 - 5:40(Applause)
-
5:43 - 5:44(Applause ends)
-
5:46 - 5:47The lesson here
-
5:47 - 5:51is that some high-performance tasks
are easier than others, -
5:51 - 5:53and that understanding
the physics of the problem -
5:53 - 5:56tells you which ones are easy
and which ones are hard. -
5:56 - 5:59In this instance, carrying
a glass of water is easy. -
5:59 - 6:00Balancing a pole is hard.
-
6:02 - 6:06We've all heard stories of athletes
performing feats while physically injured. -
6:06 - 6:10Can a machine also perform
with extreme physical damage? -
6:11 - 6:12Conventional wisdom says
-
6:12 - 6:16that you need at least four fixed motor
propeller pairs in order to fly, -
6:16 - 6:19because there are four degrees
of freedom to control: -
6:19 - 6:21roll, pitch, yaw and acceleration.
-
6:21 - 6:24Hexacopters and octocopters,
with six and eight propellers, -
6:24 - 6:26can provide redundancy,
-
6:26 - 6:28but quadrocopters are much more popular
-
6:28 - 6:32because they have the minimum number
of fixed motor propeller pairs: four. -
6:32 - 6:33Or do they?
-
6:41 - 6:42(Audience) Oh!
-
6:42 - 6:43(Laughter)
-
6:49 - 6:52If we analyze the mathematical
model of this machine -
6:52 - 6:54with only two working propellers,
-
6:54 - 6:57we discover that there's
an unconventional way to fly it. -
7:08 - 7:10We relinquish control of yaw,
-
7:10 - 7:13but roll, pitch and acceleration
can still be controlled -
7:13 - 7:16with algorithms that exploit
this new configuration. -
7:22 - 7:26Mathematical models tell us
exactly when and why this is possible. -
7:26 - 7:29In this instance, this knowledge
allows us to design -
7:29 - 7:31novel machine architectures
-
7:31 - 7:35or to design clever algorithms
that gracefully handle damage, -
7:35 - 7:37just like human athletes do,
-
7:37 - 7:39instead of building
machines with redundancy. -
7:41 - 7:42We can't help but hold our breath
-
7:42 - 7:45when we watch a diver
somersaulting into the water, -
7:45 - 7:47or when a vaulter is twisting in the air,
-
7:47 - 7:49the ground fast approaching.
-
7:49 - 7:51Will the diver be able
to pull off a rip entry? -
7:51 - 7:53Will the vaulter stick the landing?
-
7:53 - 7:56Suppose we want this quad here
to perform a triple flip -
7:56 - 7:59and finish off at the exact same
spot that it started. -
8:00 - 8:02This maneuver is going
to happen so quickly -
8:02 - 8:06that we can't use position feedback
to correct the motion during execution. -
8:06 - 8:07There simply isn't enough time.
-
8:08 - 8:11Instead, what the quad can do
is perform the maneuver blindly, -
8:11 - 8:14observe how it finishes the maneuver,
-
8:14 - 8:16and then use that information
to modify its behavior -
8:16 - 8:18so that the next flip is better.
-
8:18 - 8:20Similar to the diver and the vaulter,
-
8:20 - 8:22it is only through repeated practice
-
8:22 - 8:24that the maneuver can
be learned and executed -
8:24 - 8:26to the highest standard.
-
8:33 - 8:34(Laughter)
-
8:34 - 8:38(Applause)
-
8:39 - 8:43Striking a moving ball
is a necessary skill in many sports. -
8:43 - 8:45How do we make a machine do
-
8:45 - 8:47what an athlete does
seemingly without effort? -
8:58 - 8:59(Laughter)
-
9:04 - 9:06(Applause)
-
9:08 - 9:10(Applause ends)
-
9:11 - 9:13This quad has a racket
strapped onto its head -
9:13 - 9:17with a sweet spot roughly the size
of an apple, so not too large. -
9:17 - 9:20The following calculations
are made every 20 milliseconds, -
9:20 - 9:21or 50 times per second.
-
9:22 - 9:24We first figure out where
the ball is going. -
9:24 - 9:27We then next calculate
how the quad should hit the ball -
9:27 - 9:30so that it flies
to where it was thrown from. -
9:30 - 9:34Third, a trajectory is planned
that carries the quad -
9:34 - 9:37from its current state
to the impact point with the ball. -
9:37 - 9:41Fourth, we only execute 20 milliseconds'
worth of that strategy. -
9:41 - 9:44Twenty milliseconds later,
the whole process is repeated -
9:44 - 9:46until the quad strikes the ball.
-
9:55 - 9:58(Applause)
-
9:58 - 10:02Machines can not only perform
dynamic maneuvers on their own, -
10:02 - 10:03they can do it collectively.
-
10:03 - 10:07These three quads are cooperatively
carrying a sky net. -
10:16 - 10:17(Applause)
-
10:20 - 10:22(Applause ends)
-
10:22 - 10:26They perform an extremely dynamic
and collective maneuver -
10:26 - 10:28to launch the ball back to me.
-
10:28 - 10:31Notice that, at full extension,
these quads are vertical. -
10:36 - 10:38(Applause)
-
10:38 - 10:40In fact, when fully extended,
-
10:40 - 10:44this is roughly five times greater
than what a bungee jumper feels -
10:44 - 10:46at the end of their launch.
-
10:51 - 10:54The algorithms to do this are very similar
-
10:54 - 10:57to what the single quad used
to hit the ball back to me. -
10:57 - 11:00Mathematical models are used
to continuously re-plan -
11:00 - 11:03a cooperative strategy
50 times per second. -
11:05 - 11:09Everything we have seen so far has been
about the machines and their capabilities. -
11:09 - 11:12What happens when we couple
this machine athleticism -
11:12 - 11:13with that of a human being?
-
11:14 - 11:17What I have in front of me
is a commercial gesture sensor -
11:17 - 11:19mainly used in gaming.
-
11:19 - 11:21It can recognize
what my various body parts -
11:21 - 11:22are doing in real time.
-
11:23 - 11:25Similar to the pointer
that I used earlier, -
11:25 - 11:27we can use this as inputs to the system.
-
11:27 - 11:30We now have a natural way of interacting
-
11:30 - 11:33with the raw athleticism
of these quads with my gestures. -
12:11 - 12:15(Applause)
-
12:24 - 12:26Interaction doesn't have to be virtual.
-
12:26 - 12:28It can be physical.
-
12:28 - 12:30Take this quad, for example.
-
12:30 - 12:32It's trying to stay
at a fixed point in space. -
12:33 - 12:37If I try to move it
out of the way, it fights me, -
12:37 - 12:39and moves back to where it wants to be.
-
12:40 - 12:42We can change this behavior, however.
-
12:43 - 12:45We can use mathematical models
-
12:45 - 12:48to estimate the force
that I'm applying to the quad. -
12:48 - 12:51Once we know this force,
we can also change the laws of physics, -
12:51 - 12:54as far as the quad
is concerned, of course. -
12:56 - 12:59Here, the quad is behaving
as if it were in a viscous fluid. -
13:03 - 13:06We now have an intimate way
of interacting with a machine. -
13:07 - 13:09I will use this new capability to position
-
13:09 - 13:12this camera-carrying quad
to the appropriate location -
13:12 - 13:14for filming the remainder
of this demonstration. -
13:25 - 13:27So we can physically interact
with these quads -
13:27 - 13:29and we can change the laws of physics.
-
13:29 - 13:31Let's have a little bit of fun with this.
-
13:32 - 13:33For what you will see next,
-
13:33 - 13:36these quads will initially behave
as if they were on Pluto. -
13:37 - 13:39As time goes on, gravity will be increased
-
13:39 - 13:41until we're all back on planet Earth,
-
13:41 - 13:43but I assure you we won't get there.
-
13:43 - 13:44Okay, here goes.
-
13:53 - 13:54(Laughter)
-
14:23 - 14:26(Laughter)
-
14:26 - 14:30(Applause)
-
14:30 - 14:31Whew!
-
14:35 - 14:36You're all thinking now,
-
14:36 - 14:38these guys are having way too much fun,
-
14:38 - 14:40and you're probably also asking yourself,
-
14:40 - 14:43why exactly are they building
machine athletes? -
14:44 - 14:47Some conjecture that the role
of play in the animal kingdom -
14:47 - 14:49is to hone skills
and develop capabilities. -
14:50 - 14:52Others think that it has
more of a social role, -
14:52 - 14:54that it's used to bind the group.
-
14:54 - 14:57Similarly, we use the analogy
of sports and athleticism -
14:57 - 14:59to create new algorithms for machines
-
14:59 - 15:00to push them to their limits.
-
15:01 - 15:04What impact will the speed
of machines have on our way of life? -
15:05 - 15:07Like all our past creations
and innovations, -
15:07 - 15:10they may be used to improve
the human condition -
15:10 - 15:12or they may be misused and abused.
-
15:13 - 15:15This is not a technical choice
we are faced with; -
15:15 - 15:16it's a social one.
-
15:16 - 15:18Let's make the right choice,
-
15:18 - 15:21the choice that brings out the best
in the future of machines, -
15:21 - 15:24just like athleticism in sports
can bring out the best in us. -
15:24 - 15:27Let me introduce you to the wizards
behind the green curtain. -
15:27 - 15:31They're the current members
of the Flying Machine Arena research team. -
15:31 - 15:35(Applause)
-
15:35 - 15:38Federico Augugliaro, Dario Brescianini,
-
15:38 - 15:41Markus Hehn, Sergei Lupashin,
Mark Muller and Robin Ritz. -
15:41 - 15:44Look out for them.
They're destined for great things. -
15:44 - 15:45Thank you.
-
15:45 - 15:48(Applause)
- Title:
- The astounding athletic power of quadcopters
- Speaker:
- Raffaello D'Andrea
- Description:
-
In a robot lab at TEDGlobal, Raffaelo D'Andrea demos his flying quadcopters: robots that think like athletes, solving physical problems with algorithms that help them learn. In a series of nifty demos, D'Andrea shows drones that play catch, balance and make decisions together -- and watch out for an I-want-this-now demo of Kinect-controlled quads.
- Video Language:
- English
- Team:
- closed TED
- Project:
- TEDTalks
- Duration:
- 16:08
Camille Martínez edited English subtitles for The astounding athletic power of quadcopters | ||
Krystian Aparta edited English subtitles for The astounding athletic power of quadcopters | ||
Krystian Aparta edited English subtitles for The astounding athletic power of quadcopters | ||
Krystian Aparta commented on English subtitles for The astounding athletic power of quadcopters | ||
Krystian Aparta edited English subtitles for The astounding athletic power of quadcopters | ||
Krystian Aparta edited English subtitles for The astounding athletic power of quadcopters | ||
Morton Bast edited English subtitles for The astounding athletic power of quadcopters | ||
Thu-Huong Ha edited English subtitles for The astounding athletic power of quadcopters |
Krystian Aparta
The English transcript was updated on 2/24/2015.