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Over a million people are killed
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each year in disasters.
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Two and a half million people
will be permanently disabled or displaced,
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and the communities will take
20 to 30 years to recover
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and billions of economic losses.
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If you can reduce the initial response
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by one day,
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you can reduce the overall recovery
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by a thousand days, or three years.
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See how that works?
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If the initial responders
can get in, save lives,
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mitigate whatever flooding
danger there is,
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that means the other groups can get in
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to restore the water,
the roads, the electricity,
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which means then the construction people,
the insurance agents,
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all of them can get in
to rebuild the houses,
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which then means you
can restore the economy,
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and maybe even make it better
and more resilient to the next disaster.
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A major insurance company told me
that if they can get a homeowner's claim
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processed one day earlier,
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it'll make a difference of six months
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in that person getting their home repaired.
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And that's why I do disaster robotics,
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because robots can make
a disaster go away faster.
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Now, you've already seen
a couple of these.
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These are the UAVs.
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These are two types of UAVs:
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a rotorcraft, or hummingbird;
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a fixed-wing, a hawk.
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And they're used extensively since 2005,
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Hurricane Katrina.
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Let me show you how this hummingbird,
this rotorcraft, works.
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Fantastic for structural engineers.
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Being able to see damage from angles
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you can't get from binoculars
on the ground or from a satellite image,
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or anything flying at a higher angle.
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But it's not just structural engineers
and insurance people who need this.
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You've got things like
this fixed wing, this hawk.
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Now, this hawk can be used
for geospatial surveys.
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That's where you're pulling together
imagery together
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and getting 3D reconstruction.
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We used both of these at the Oso mudslides
up in Washington state
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because the big problem was
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geospatially and hydrologically
understanding the disaster,
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not the search and rescue.
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The search and rescue teams
had it under control:
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they knew what they were doing.
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The bigger problem was that river
and mudslide might wipe them out
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and flood the responders,
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and not only was it challening
to the responders and property damage,
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it's also putting at risk the future
of salmon fishing
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along that part of Washington state.
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So they needed to understand
what was going on.
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In seven hours, from going from Arlington,
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driving from the instant command post
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to the site, flying the UAVs,
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processing the data,
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driving back to Arlington command post,
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seven hours.
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We gave them in seven hours
data that they could take
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only two to three days
to get any other way
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and at higher resolution.
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It's a game-changer.
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And don't just think about the UAVs.
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I mean, they are sexy, but remember,
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80 percent of the world's
population lives by water,
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and that means our critical
infrastructure is underwater,
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the parts that we can't get to,
like the bridges and things like that,
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and that's why we have
unmanned marine vehicles,
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one type of which you've already met,
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which is SarBot, a square dolphin.
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It goes underwater and uses sonar.
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Well, why are marine vehicles so important
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and why are they very, very important?
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They get overlooked.
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Think about the Japanese tsunami:
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400 miles of coastland totally devastated,
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twice the amount of coastland devastated
by Hurricane Katrina in the United States.
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You're talking about your bridges,
your pipelines, your ports,
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wiped out, and if you don't have a port,
you don't have a way
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to get in enough relief supplies
to support a population.
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That was a huge problem
at the Haiti earthquake.
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So we need marine vehicles.
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Now, let's look at a viewpoint
from the SarBot
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of what they were seeing.
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We were working on a fishing port.
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We were able to reopen that fishing port
using her sonar in four hours.
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The fishing port was told it was going
to be six months before they could get
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a manual team of divers in,
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and it was going to take
the divers two weeks.
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They were going to miss
the fall fishing season,
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which was the major economy for that part,
which is kind of like their Cape Cod.
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UMVs, very important.
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But you know, all the robots
I've shown you have been small,
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and that's because robots
don't do things that people do.
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They go places people can't go,
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and a great example of that is busheld.
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Unmanned ground vehicles
are particularly small,
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so busheld
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-- say hello to busheld (Laughter) --
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bushel was used extensively
at the World Trade Center
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to go through Towers 1, 2, and 3.
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You're climbing into the rubble,
rappelling down,
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going deep in spaces,
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and just to see the World Trade Center
from busheld's viewpoint,
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look at this, you're talking
about a disaster
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where you can't fit a person or a dog,
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and it's on fire.
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The only hope of getting
to a survivor way in the basement,
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you have to go through things
that are on fire.
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It was so hot, one of the robots,
the tracks began to melt and come off.
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Robots don't replace people or dogs,
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or hummingbirds or hawks or dolphins.
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They do things new.
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They assist the responders,
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the experts, in new and innovative ways.
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The biggest problem is not
making the robots smaller, though.
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It's not making them more heat resistant.
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It's not making more sensors.
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The biggest problem is the data,
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the informatics,
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because these people need to get
the right data at the right time.
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So wouldn't it be great if we could have
experts immediately access the robots
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without having to waste any time
of driving to the site,
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so whoever's there, use their robots
over the Internet.
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Well, let's think about that.
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Let's think about a chemical
train derailment in a rural county.
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What are the odds that the experts,
your chemical engineer,
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your railroad transportation engineers,
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have been trained on whatever UAV
that particular county happens to have?
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Probably, like, none,
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so we're using these kinds of interfaces
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to allow people to use the robots
without knowing what robot they're using
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or even if they're using a robot or not.
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What the robots give you,
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what they give the experts,
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is data.
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The problem becomes,
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who gets what data when?
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One thing to do is to ship
all the information to everybody
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and let them sort it out.
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Well, the problem with that is,
it overwhelms the networks,
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and worse yet, it overwhelms
the cognitive abilities
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of each of the people trying to get
that one nugget of information
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they need to make the decision
that's going to make the difference.
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So we need to think
about those kinds of challenges.
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So it's the data.
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At the World Trade Center,
going back to the World Trade Center,
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we tried to solve that problem
by just recording the data from busheld
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only when she was deep in the rubble,
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because that's what the user team
said they wanted.
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What we didn't know at the time
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was that the civil engineers
would have loved,
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needed the data as we recorded
the box beams, the serial numbers,
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the locations as we went into the rubble.
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We lost valuable data, so the challenge
is getting all the data
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and getting it to the right people.
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Now, here's another reason:
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we've learned that some buildings,
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things like schools,
hospitals, city halls,
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get inspected four times
by different agencies
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throughout the response phases.
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Now, we're looking, if we can get
the data from the robots to share,
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not only can we do things like
compress that sequence of phases
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to shorten the response time,
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but now we can begin to do
the response in parallel.
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Everybody can see the data.
We can shorten it that way.
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So really, disaster robotics
is a misnomer.
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It's not about the robots.
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It's about the data.
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(Applause)
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So my challenge to you:
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the next time you hear about a disaster,
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look for the robots.
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They may be underground,
they may be underwater,
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they may be in the sky,
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but they should be there.
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Look for the robots,
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because robots are coming to the rescue.
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(Applause)