[MUSIC PLAYING]
I want to talk to you a little
bit about my background.
I want to talk a
little bit about what
I used to do when I worked in
the computer science department
here.
And then I'll talk about
what I'm doing right now.
Basically, I build, fly,
and often crash drones
and other robotic systems.
So my background
is a little weird.
When I was in high school--
I'm a Nevada native--
I went to McQueen
and I really, really
wanted to become a lawyer.
And so I studied philosophy
and math in college.
And then I decided
to come back here--
I was in Seattle up at the
University of Washington.
And when I came
back, I was offered
a position in the robotics
lab and they said,
we will pay for you
to go to grad school
and then we will pay
you on top of that.
And I said, I love robots.
So that's how I
got into robotics--
completely by accident.
It was not my intention.
In kind of the same
way, I fell into drones.
And so, basically,
I've been getting
consistently lucky in my
career path for a long time.
However, I have liked
math for a while.
So let's start with robots.
So how many of you agree that
funding is a big challenge when
you're doing robotics?
Yes.
Yeah.
Awesome.
OK, so that's actually
a really good segue too.
I was going to save
this till the end
but this is one of
the robots that we
work with at the university.
Does anyone know what this robot
or what kind of robot this is?
First off, what's
the shape of it?
Yeah?
Humanoid?
Humanoid, yes, this
is a humanoid robot.
Why is it a humanoid robot?
Yeah.
Because it's bipedal
with opposable thumbs?
Yeah, so it has
features that we think
of as being very human-like.
So it's got two legs,
though not all humanoids
have two legs or even one leg.
It has two arms, though
not all humanoids
have two arms or even one arm.
And it has a head.
Most of them have heads.
So any guesses how much
a robot like this costs?
So you can hear it's
got some motors--
it's got that Iron Man sound--
and it's made of plastic.
Relatively cheap plastic.
So any guesses how much
something like this costs?
And, by the way, the
computing power in this
is worse than what's in
your phones right now.
So this is about an $8,000
to $10,000 robot from France.
This one's named Sky.
It's named Sky because we took
it to [? Roygon ?] Elementary
School and the first kid
that walked in looked at it,
and she said, "This is Sky."
And that was it.
We couldn't go
with anything else.
So in a second
Sky will stand up.
But while Sky is booting--
because robots are really just
computers with some motors--
what else is hard?
Let's say I gave you all the
money and all the smart people
in the world--
what's hard about robotics?
Do all the problems
go away or are there
still things we need to do?
Yeah?
Programming?
Yes, the programming is
exceptionally difficult.
How many of you have
written code before?
Cool.
How many of you think of that
writing code is challenging?
Everyone who raised their hand.
Actually, everyone
can raise their hand--
code is challenging.
How many of you have
to think really, really
hard about standing up?
Anyone?
No?
Cool, keep it that way.
So humans do that
effortlessly, right?
Robots, on the other hand,
have to be programmed--
specifically to be able
to stand, and to walk,
and move around.
And that is exceptionally
challenging.
So much so that that is an
unsolved problem in robotics.
So you're about to see
Sky hopefully stand up.
I really hope.
Here we go.
OK, Sky is standing.
Sky is not just standing,
Sky is actively balancing.
If you're over on that
side and can't see,
feel free to stand up.
So that's incredibly
challenging.
And building a robot that
can do that is very hard
and will probably continue
to be hard for a while.
So that's a good one.
What else is hard?
The thing that you probably
most take for granted--
the ability to look at
people, to understand
what they're doing, to
be able to talk to them,
for them to be able
to talk to you--
that is, I think, probably the
hardest thing about robotics.
Even if Sky can stand
and can walk around,
Sky still doesn't
understand when
I'm angry because it's not
doing what I tell it to do.
And until robots can understand
that humans are angry,
they're not going to be as
useful as maybe they could be.
We kind of have learned
some lessons about this.
So Sky's doing some
interesting things right now.
What's Sky doing
that's interesting?
It's kind of swaying.
It's looking at you, I think.
Yes, to both of you.
So he said-- he being Zachary--
said it's swaying.
It's balancing?
It's balancing.
Does it need to sway
to balance though?
How many of you think
that Sky needs to sway--
like I do-- to be balanced?
How many of you think
that's not necessary?
Good, it's not.
So it's not the
physics in this case.
And then Lucas said
it's looking at me.
So if I go over here and I
talk, it's entirely possible--
and we'll see how good it is--
that it will turn
eventually to look at me
and it will focus on me.
So it's doing those things
for the same reason.
Why is it doing those things?
Why is it swaying and
why is it looking where--
why is it looking at me?
What?
Because you're talking?
Because I'm the one
making noise, right?
So it has multiple microphones.
And this is actually kind
of a cool physics problem--
it's got microphones on
both sides of its head
and the sound takes
different lengths of time
to get to those microphones.
Tiny, Tiny, tiny difference.
But on the basis
of that difference,
it can actually figure
out the direction
the sound is coming from.
So it's just like humans
by normal hearing.
But why?
Why would you
program it that way?
What do you think would happen
if it didn't do those things?
If it always just
looked straight
ahead until you programmed
it to move its head,
and if it didn't sway?
Because swaying is
wasteful, right?
If I'm swaying I'm
expending energy.
I'm putting stress
on those servos.
I'm doing all kinds of things
that mechanically are not good.
So there's got to be a reason.
So what would
happen if you didn't
make it sway and move
its head, and blink
its adorable little eyes?
Because it's also blinking.
So it turns out people find
this thing overwhelmingly creepy
if you turn off
all of the motions.
So this is something
they learned
with the first generation
of these robots--
didn't have that ability to move
around and people hated them.
Now they think they're adorable.
So I think one of the
most interesting things
about robotics is
that everything
we think of as being
really hard is actually
pretty easy for computers.
How many of you play chess?
How many of you have
ever played chess?
OK, how many of you--
do you play Go, which is
like chess but harder?
A couple of you have played Go.
So when did computers
beat humans at chess?
In the '90s.
In the last millennium.
So that was solved, computers
could beat humans in chess.
Now the best computer can
handily beat the best human.
Go which is a much more
challenging variant of chess,
more or less, is still something
that the best humans win at.
But, in fact, this month
one of Google's companies--
Google DeepMind-- has challenged
the best Go player in the world
to a Go competition.
And it is widely believed
that Google's computer
will beat the guy.
So that's coming up, watch
in the news for that.
It's going to be a
big deal either way.
But those things are things
that computers can do.
I'll bet that even the
Go program on my iPad
is probably better than
everybody in this room,
no matter how good you are.
And so with computers much of
what we find very challenging--
super easy.
But everything that we think
of as incredibly simple--
next to impossible for robots.
So walking-- very,
very hard for robots.
Being able to pick
up a bottle of water
without crushing it or
knocking it over or doing
anything terrible--
I'm glad the cap was on--
impossible.
Very, very challenging
for robots to do.
Holding a conversation--
hard for some humans,
hard for all robots.
These are things that
are exceptionally
difficult to get robots to do.
So there's actually
a whole field
called human robot
interaction that
is dedicated to studying
the ways that people
and computers-- and robots
in particular-- can interact.
So that's what I got my PhD in--
I was studying how humans
and robots can interact.
This is a video that we
presented to the military
because--
fun fact about computer
science research--
the military funds just about
all of it in the United States.
And so what's going on here
is a very mundane scene.
I would actually say
nothing interesting
is happening at all, except
that we have a computer--
or a robot, in this
case-- and what
we're seeing in
the top two panels
is the world view
according to the robot.
So on the left we have
normal RGB camera frames,
and on the right the
things that don't matter
have been segmented away.
And it's kind of hard to tell
from here but this is actually
the viewpoint of an
Xbox 360 Kinect--
so the 3D camera.
And so those points,
you can see how Amol--
who was in our lab
and now is working
for Microsoft in Redmond--
Amol looks like he's blotchy.
That's because each
of those points
is actually a point in 3D space.
So I can tell you exactly
how far away Amol is,
and how far each of those
things are away from the camera.
So it sees in 3D which is
exceptionally challenging.
And what it's doing
down on the bottom--
the details aren't
really important
for the Navy thought they were--
is it's figuring out what
Amol is going to do next.
So it's looking at the
activities he's performing
and it's saying that he's
going to clear the table.
This is not hard, right?
So when she just took
a drink from her soda,
I knew she was going to set it
down and not throw it at me.
If she had done that we would
have all been surprised,
wouldn't we?
Yes?
Yeah.
That is a critical
social skill that
is fundamental to human-human
interaction and is
what I spent most of my
graduate student years studying.
And then one more.
So here we have the
same sort of setting.
Here we have more cameras.
You can see that
we've got a robot dog.
We have a red now--
that one was named Marie.
This is what the camera sees as
being interesting at the time.
This is the human in the scene.
You can see that it's
recognizing objects, which is--
well, it used to be
incredibly challenging, not so
much anymore.
And then it's also tracking--
not just that there's
a human but also
where my shoulders are, where
my head is, where my hands are,
so that it can try to
predict my activities.
And it's doing it 13 times a
second which is just barely
fast enough to be usable.
If I turn the sound on there
there's ridiculous dialogue.
Right now almost all systems
that work in the real world
operate through a network
connection of some kind.
So it's a cloud.
They're saying cloud robotics
is going to be a big deal.
There are some exceptions.
Can anyone think of
exceptions to that?
Tesla, right?
So those cars are
practically robots
and they're only going
to be more and more
like robots going forward.
And so, in that case,
almost all of the technology
is actually on the vehicle.
When we fly drones--
some of the bigger drones
we fly-- almost all
of the technology
is on the vehicle.
But for now, particularly
with things like humanoids
that is not really feasible.
NAASIC, which is the
group I'm a part of,
is in UNR's College
of Engineering.
But it is not quite like
a standard department.
It really is about two things.
Autonomous systems is just
a fancy word for robots.
So we do actually work
with robots like that one
occasionally, but
the other thing we do
is we work with companies trying
to figure out how to make money
selling robotics technology.
And as a result of that,
we are primarily focused--
that's audio.
That's next.
We are centered downtown
at the Innovation Center
in a snazzy building across
from the Discovery Museum.
And we really focus on
trying to find things
in robotics that are
commercializable-- that
can make money and
bring jobs to the state.
Right now that means drones.
So you don't care about that.
These are the people
we work with though.
So one of the cool
things about what
I was doing in
computer science is
that I was able to focus
on something very narrow
for a long time and learn
a lot about everything
from the neuroscience of social
interaction, all the way to--
how do we get these things to
not fall over most of the time?
Now, though, with
drones what I do
is I really work with
people in all kinds of areas
to help them figure out
how to do cool stuff
with interesting technology.
So right now we're
working with anthropology
to study how we can use drones
to help archeologists map
sites across the
state of Nevada,
so that we can put those
things online and share them
with people.
We're working with
hydrogeology and geography
to study how geothermal
power gets generated.
So we're flying drones
over geothermal plants.
We work with engineering
and we work with business,
but I also get to work
with companies too.
So how many of you
have heard of Flirtey?
Have any of you
heard of Flirtey?
They're in the news a lot.
So they're a drone
delivery company.
They are based next door in
the applied research building,
so they're actually
based on campus.
They performed the first drone
delivery in the United States.
They did it in Virginia
a couple of months ago.
Drone America is a company that
makes some pretty cool stuff.
They build things that
are much, much bigger.
But we also work with groups
like NASA, the airport people.
So we are actually a part
of Nevada's test site.
So there are six places in
the country that can fly
drones for testing purposes.
So it turns out if you want
to fly for fun, you can
and that's totally fine.
If you want to fly and you're
a company or a university,
it turns out it's completely
illegal to fly outside.
If you do it then there
are $10,000 fines, if you
keep doing it there's prison.
It's not good, not good at all.
However, at these
test sites we have
something called a
Certificate of Authorization
from the federal government
that says we can let people fly.
And so what we do is we
help these people get out--
this is actually north of town--
and we conduct
flight operations,
which is actually kind of cool.
So I spent a lot of time in
offices with things like that.
And so now I get to travel
all over the country--
and soon the world--
flying drones.
So it's a neat change of pace.
We also teach people.
So this was a class we taught.
We taught them how to
be a visual observer.
So any guesses what that means?
You watch?
Yeah, you visually observe.
And actually you also
have to learn some things
about how to use a radio.
But really it's
all about safety,
so we focus on trying
to teach people how
to operate these things safely.
We have a couple
of cool projects.
So our director is the guy
with the remote control there.
This is Mongolia and
this whole valley
is about to get
turned into a lake
because they're
going to build a dam.
There are lots of unfortunate
things about this,
but one in particular
is that there's
a type of trout that is--
actually, I think it's the
largest trout in the world,
it's huge-- the
taimen trout, that
will probably be
destroyed when they
dam the river unfortunately.
So what this group was doing
was trying to conduct surveys
to see how many
of them there are,
to understand their habitat
and the local ecology better.
And the way that
they used to do it
is they'd get biologists
to go out in boats
and they'd look until
they saw some fish.
And then they'd try to catch
them, and tag them, and put
them back.
As you can imagine that is
challenging and not necessarily
super fun.
So what they did was they
had us come with them
and fly-- this is
one of our drones.
Does anyone know what
this one is called?
It's a pretty common model.
It's a DJI Inspire, it's
designed for photographers.
But our director, Warren, was
able to fly over the river
and see the fish in a way
that biologists had never
seen before.
And so this they think is going
to completely revolutionize
how they study those rivers.
And as soon as there's
enough water in the Truckee
we are going to do
the same thing here.
So we're taking the lessons
that we learned in Asia
and we're starting
to apply them locally
around Pyramid, around Tahoe.
So this is one of
our current projects,
we'll be going back in August.
I think I'll be going
this time as well.
I think by far my
favorite stuff to talk
about, though, is what
we're doing with-- does
anyone know what this is?
I guess let's start
with that big thing.
Have any of you ever seen this?
It's close.
It's not here but it's nearby.
So this is in Silicon Valley.
This is called Hangar 1,
this is an airship hangar.
So when the Hindenburg was on
the west coast, it used this.
This was one of the largest
free standing structures
in the world--
it's for blimps.
It's at NASA Ames
in Silicon Valley
so this is literally
right next door to Google.
And NASA doesn't
just do space, they
do a lot with aeronautics--
the first A-- and one
of their big projects
right now is trying to build
a system for drone traffic
management.
The goal for a lot of people is
to have these drones everywhere
doing all kinds of things.
Following cars-- though
that's kind of creepy--
doing search and rescue,
performing package delivery,
studying agriculture.
These are all things that
would be very, very cool if we
could do them with drones.
But you'd need lots of drones
to make them work, right?
Yes?
In order to make them cheap.
So one of the things
we're working on with NASA
is understanding how to
build an air traffic control
system for all of these things.
To coordinate thousands,
or tens or hundreds
of thousands of drones all at
once, which is kind of cool.
We were actually one
of the first groups
in the country here at UNR
to connect to this system.
We drove down to NASA Ames--
I took one of my interns--
and we hooked up the system
that I built to the system
they built and checked it out.
And then a couple
of months later
we got to fly with NASA
in central California
on an old abandoned
military base.
And we'll be continuing
to do that actually
for the next couple of years.
This is a very busy
slide but it describes
some of the other projects
that we are working on as well.
So this is probably the
biggest one right now.
It's called Sense and Avoid.
Any guesses what that means?
So they don't crash?
Yeah, so they don't
crash into each other.
So how do you do that?
So right now drones
have GPS that
is-- it's not quite military
grade but it's pretty good.
It's surprisingly good.
And then they have
all kinds of sensors.
So you can see they've got
radar and then, like you said,
they also need cameras.
All of those things--
so remember how I said
everything in robotics
that we think of as being
really easy is really hard?
So getting a robot to
see, to recognize you--
so I showed the video where
it recognized the laptop--
extraordinarily difficult.
So now try to imagine
that it's seeing,
and it's going
100 miles an hour,
and if it doesn't see
correctly then it crashes.
Much more challenging.
But also much more fun.
So this is a big project
we're working on.
And then, it's actually
not on here but--
have you flown your drone
so far you couldn't see it?
Only when it landed
out of my sight.
Only when it landed
out of your sight.
So technically it was out
of your line of sight.
It turns out, right now flying
your drone so far that you
can't see it is also illegal.
In that case, though,
it's fairly sensible.
Because if you're not
able to see the drone,
you're not able to
see what's around it
and you don't know how to
take evasive maneuvers if you
need to.
And so one of the things that
we're working on-- actually
with NASA too--
is this problem
of getting drones
to operate beyond line of
sight, which it turns out
is rather challenging
because of Sense and Avoid
and because of all
kinds of other issues.
One of the things that
people have conjectured
is that once we have all
of these drones in the sky,
they'll be able to
sense pressure and wind
and they'll be able to
share that information.
So we will have a better
picture of our environment
and our climates--
and even just the weather--
than we have ever had before.
It already happens
in the ocean--
buoys have sensors in them--
but we'll be able to do that
for the whole planet which
is kind of cool to think about.
In fact, one of the things
that Ames is working on
is trying to build models of
how the wind flows in cities.
So it turns out-- yeah, right?
Kind of weird.
But it turns out that
in urban canyons--
if you go to a big city
with lots of buildings that
are close together, wind
blows in those urban canyons
just because.
So if you're outside of the city
and it's a perfectly calm day,
that doesn't mean it's going
to necessarily be perfectly
calm in, say, Manhattan.
And so understanding
the microclimates
of those urban canyons
is an important problem
for just practical
business purposes.
So I can tell you--
I can't tell you who,
but I can tell you
that there are people
working on that right
now, which is kind of cool.
There are all kinds of
things that don't move,
that you would think would
be really easy not to hit--
trees, power lines are
kind of another example.
Buildings-- we really don't
want to hit buildings.
So how many of you think
we could map the world?
So we mapped the
world once, right?
So I think it was in the
1800s, we pretty much
finished building reasonably
accurate maps of the planet.
We knew where all the
coasts were more or less.
And then GIS came along--
Geographical
Information Systems--
we started putting all
this stuff in computers.
And we did that, and then
Google Maps came along
and we were pretty
sure we were done then.
But how many of you think we
could build accurate 3D maps
of the whole planet?
You're such pacifists.
Really?
Why?
Why not?
We already mapped
the planet once--
at least once.
Yeah.
It's always changing slowly.
It's always changing
so that's true.
So it's going to be hard--
exceptionally hard-- but once
these things fly over a long
time--
Oh, my gosh, where's
your spirit of adventure?
I'm mostly kidding but
I think we actually
will have accurate 3D
maps of the whole planet.
And once we do it will make a
lot of this much, much easier.
There are, in fact,
companies that
are trying to build these
databases right now.
However, it's a
wide open problem.
In fact, actually,
you're old enough--
you probably could start
companies and start
trying to tackle this
right now, which is
kind of amazing to think about.
The way we're going
about it is we're
actually turning
this space here at--
How many of you have
been to the air races?
Couple of you?
Cool.
So this is the
Reno State Airport
where the air races happen.
We are turning this building
into a research facility where
we are going to be studying all
of these problems surrounding
drones.
Actually it opens on April 4th.
So if you come back
in a month, I'll
be able to tell you
that we are actually
conducting research,
trying to figure out
how to solve these problems
of Detect and Avoid
and getting drones
to operate together.
And we'll be doing it
for quite some time.
So that's what we're
up to right now.
So this is actually a
picture of the air races.
So UTM is NASA's system
for traffic management.
We're turning this whole
valley into a research area
to study this problem
of how you handle
air traffic control for drones.
We have people from NASA Ames
coming up on a regular basis
talking about how to do some
fairly cool things out here.
Drone delivery I
mentioned already.
We work with a
company called Flirtey
that is trying to build
up the infrastructure
to do delivery by drone.
And does anyone know
what SAR stands for?
Search and rescue.
So tomorrow, as it happens,
we will be going out I
think actually to that square.
And we're going to send
a student volunteer
into the middle
right before sunset
and we're going to
tell them to lie down.
And then when we do that, we're
going to fly a drone over them
and we're going to try to find
them with a normal camera.
And then the sun is
going to set and then
we're going to use
a thermal camera.
And we're going to fly over
them with a thermal camera.
And we're really hoping--
and I'm sure after sunset
the undergraduate will
really be hoping--
that we can find them much more
easily with our thermal camera
than with a normal camera.
So the idea is that if someone
gets lost up in the mountains,
we can find them very,
very quickly as long
as they're still warm.
So that's something
that we're going
to be working on pretty actively
here starting tomorrow evening.
This one's not
open to the public,
though I think
future ones we'll be.
Does anyone know what this is?
Nasty-looking.
What?
Nasty-looking.
It's, yeah, nasty-looking
so this is not just
a problem with my screen.
A couple months ago--
so this is Colorado,
this is a river--
the EPA, the Environmental
Protection Agency, screwed up
and they tapped into a mine
that was full of toxic sludge.
So those three humans are
having a delightful time
in a river of toxic sludge.
And so what they've done
is they've come to us
and they've said, we
want to fly drones.
And we want to build
very, very accurate maps
of this whole region so
that we can figure out
the best possible way to
clean up all the junk.
In particular, there
are just these big piles
of mine tailings.
Just rocks that are--
maybe they don't
have uranium in them,
maybe they don't have good
enough uranium in them.
I really hope they don't
have uranium in them
because I'm going to be
the one flying there.
But we're going to be assisting
the EPA with that cleanup
effort and we're
going to be doing it
with a technology called
LiDAR on one of our drones.
So how many of you
have heard of LiDAR?
Anyone?
How many of you
have heard of radar?
Cool, yeah , so it's just
like that but with lasers.
So you shoot lasers
out, they bounce back,
and then you can
measure distances.
In fact, that thing
which some of you may
have seen walking in--
the little puck--
is a LiDAR unit.
In fact, it is the
LiDAR unit that we'll
be flying over that river.
So this can actually build
accurate 3D point clouds.
And in just a second when
we're done with the front--
and when my computer
decides to uncrash--
I'll show you the
demo of what it sees.
Because we're using it for
drones with a big octacopter
but it's also exactly the
same type of technology that
sits on top of Google's car--
the self-driving car.
So the same company that
makes the lasers for Google
is making that laser,
which is kind of cool.
And it's how we're going to
start the project of mapping
the whole world.
And so these are just pictures
of us helping Nevada's economy.
This is the drone that
we're going to be flying.
It's huge, it's like
four feet across
and big enough to carry
that kind of LiDAR unit.
One of the things I like
about this technology
is that it's really fast moving.
So robotics is actually
making pretty good progress.
There are things we can do
now that were impossible
10 years ago.
Drones are moving at
light speed in comparison.
This is a Velodyne VLP-16.
It's a LiDAR unit so it's a
laser range-finder essentially.
But there are 16 lasers in
here and it's spinning really,
really, really fast.
And what it's doing
is it's seeing
how that light bounces back and
then it's measuring distances.
And you can see from those it's
able to rebuild a model of what
it sees essentially.
So if I zoom in--
zoom out a little bit--
you can see me.
I'm right there so
watch that blob.
It's moving.
As you move, I can
see you moving.
So this is what we're
going to be putting
on one of our octacopters to
build those very, very accurate
maps of the American Southwest.
This is also the
exact same technology
that works on Google
self-driving cars.
So Velodyne is the company
that makes-- they actually
make a slightly
bigger laser that
goes on top of Google's cars.
And that's how Google's cars
are able to see all around them
and begin to drive themselves.
So what we do is we
take this information,
and we take GPS data,
and we fuse them together
to build very, very
accurate 3D maps.
We essentially know
where we are at all times
and we know how we're moving.
So how we're rotating
and how we're
translating through space.
And we take that information,
use some clever statistics,
and we take these
points and we fuse them
into very, very accurate maps.
I think that we're going to
get to a point in the next 100
years, where there will be
humanoid robots our size that
can do everything we can do.
Yeah, that will be
a thing, I promise.
Now once that
happens, we're going
to have a whole other range
of issues to consider.
So drone traffic
management is tricky.
How are we going to do
humanoid traffic management?
Humanoid traffic
management systems
will be a thing in 150
years when we're all dead.
Yeah, I think that's right.
There will be people who go to
school to become robot traffic
controllers.
Or we'll just have robots
do it, I don't know.
[LAUGHTER]
Because if you think from
the perspective of a drone--
this is about 600, 700
grams so a pound and a half.
This is a few dozen grams.
The computer is going to be
probably 80 to 100 grams.
You're talking about
probably two pounds of stuff.
Right now, how long do
drone stay in the air?
Not long.
Not long.
No, nothing more specific?
Like five, 10 minutes.
10 minutes tops for the smalls.
For bigger ones--
15 to 20 minutes.
They can't stay in
the air very long
and that's just
them by themselves.
One of the first things I did
when I got a small drone--
maybe about this big or so--
was I taped an iPhone to the top
to just see--
well, actually no,
to the bottom--
to see if I could have just
an iPhone camera on the drone.
And it cut the power--
the lifetime of the drone--
in half.
So it was interesting.
Interestingly enough,
I actually think Tesla
and actually Panasonic
are going to go
a long way towards
addressing that problem.
The batteries that they're
developing for these cars
are the same sorts of
technologies that will
we will end up using in drones.
In some cases we already do.
And so everything that's pushing
the electric car revolution
forward is also helping us.
In fact, one of
the faculty here--
Kostas Alexis in
computer science--
worked with a team
in Switzerland,
which is where he was
a postdoc, to build
a solar-powered fixed-wing
drone that set the world
record for endurance.
It flew for 81 and 1/2
hours straight continuously.
So it had solar
panels on its wings,
it would charge during the day,
it would draw power at night,
and it flew a circular pattern.
And they use basically the same
batteries that are in a Tesla.
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