TEDxPenn
Vijay Kumar: The future of flying robots
Vijay Kumar: 飞行机器人的未来
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在宾夕法尼亚大学的实验室里，Vijay Kumar和他的团队从蜜蜂身上获得了灵感，创造了自动化的空中机器人。他们最新的突破：精密耕种，即一群机器人通过规划，重建和分析果园里的每株植物和果实，为农民提供了至关重要的信息，能够帮助农民提高产量，并且让水资源的使用和管理更加智慧。
Vijay Kumar - Roboticist
As the dean of the University of Pennsylvania's School of Engineering and Applied Science, Vijay Kumar studies the control and coordination of multi-robot formations. Full bio
As the dean of the University of Pennsylvania's School of Engineering and Applied Science, Vijay Kumar studies the control and coordination of multi-robot formations. Full bio
Double-click the English transcript below to play the video.
00:13
In my lab, we build
autonomous aerial robots
autonomous aerial robots
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在我的实验室，
我们打造了自动飞行的机器人，
我们打造了自动飞行的机器人，
就是你们眼前的这种。
00:16
like the one you see flying here.
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00:20
Unlike the commercially available drones
that you can buy today,
that you can buy today,
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不像当今市面上销售的那些，
这个机器人没有GPS定位系统。
00:24
this robot doesn't have any GPS on board.
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00:28
So without GPS,
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在没有GPS的情况下，
这样的机器人很难定位自己。
00:29
it's hard for robots like this
to determine their position.
to determine their position.
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00:34
This robot uses onboard sensors,
cameras and laser scanners,
cameras and laser scanners,
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这个机器人用机载传感器，
相机和激光扫描仪，
相机和激光扫描仪，
来扫描环境。
00:39
to scan the environment.
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它能够探测到环境的特征，
00:40
It detects features from the environment,
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并使用三角测量的方式来决定
不同的特征之间
不同的特征之间
00:43
and it determines where it is
relative to those features,
relative to those features,
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有怎样的联系。
00:46
using a method of triangulation.
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然后它能够把所有这些信息
整合到一张地图上，
整合到一张地图上，
00:48
And then it can assemble
all these features into a map,
all these features into a map,
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就是我背后的这张图。
00:52
like you see behind me.
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这个地图能让机器人确定
障碍物的位置，
障碍物的位置，
00:53
And this map then allows the robot
to understand where the obstacles are
to understand where the obstacles are
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并巧妙地避开各种碰撞。
00:57
and navigate in a collision-free manner.
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01:01
What I want to show you next
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我接下来要展示的是
一组我们在户外进行的实验，
01:03
is a set of experiments
we did inside our laboratory,
we did inside our laboratory,
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证明机器人能够在户外
进行长距离飞行。
进行长距离飞行。
01:06
where this robot was able
to go for longer distances.
to go for longer distances.
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01:10
So here you'll see, on the top right,
what the robot sees with the camera.
what the robot sees with the camera.
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你们可以看到，在右上方，
是机器人通过照相机看到的影像。
是机器人通过照相机看到的影像。
01:15
And on the main screen --
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在主屏幕上——
01:16
and of course this is sped up
by a factor of four --
by a factor of four --
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当然这是以4倍速度在播放展示——
在主屏幕上，你们可以看到
地图的创建过程。
地图的创建过程。
01:19
on the main screen you'll see
the map that it's building.
the map that it's building.
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这是一个高分辨率的地图，
展示了我们实验室周围走廊的样子。
展示了我们实验室周围走廊的样子。
01:21
So this is a high-resolution map
of the corridor around our laboratory.
of the corridor around our laboratory.
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很快你们就能看到飞行机器人
进入了我们的实验室，
进入了我们的实验室，
01:26
And in a minute
you'll see it enter our lab,
you'll see it enter our lab,
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这么乱的地方，一看
就知道是我们的实验室。
就知道是我们的实验室。
01:28
which is recognizable
by the clutter that you see.
by the clutter that you see.
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（笑声）
01:31
(Laughter)
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但我重点想说的是，
01:32
But the main point I want to convey to you
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这些飞行器可以创建
高分辨率的地图，
高分辨率的地图，
01:34
is that these robots are capable
of building high-resolution maps
of building high-resolution maps
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达到5厘米的分辨率，
01:38
at five centimeters resolution,
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可以使那些在实验室外，
或者是房屋外的人，
或者是房屋外的人，
01:40
allowing somebody who is outside the lab,
or outside the building
or outside the building
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无需进入房间即可观察到这些内容，
01:44
to deploy these
without actually going inside,
without actually going inside,
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01:48
and trying to infer
what happens inside the building.
what happens inside the building.
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并尝试了解房屋内发生的状况。
01:52
Now there's one problem
with robots like this.
with robots like this.
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当然这样的机器人也有问题。
01:55
The first problem is it's pretty big.
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首先，它有些大了。
01:58
Because it's big, it's heavy.
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因为很大，所以很重。
02:00
And these robots consume
about 100 watts per pound.
about 100 watts per pound.
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这些机器人每磅重量可以消耗
100瓦的电能。
100瓦的电能。
02:04
And this makes for
a very short mission life.
a very short mission life.
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这使得它的运作时间很短。
02:08
The second problem
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第二个问题就是，
这些机器人有机载传感器，
它们非常昂贵——
它们非常昂贵——
02:09
is that these robots have onboard sensors
that end up being very expensive --
that end up being very expensive --
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一个激光扫描仪，一个相机
以及处理器。
以及处理器。
02:13
a laser scanner, a camera
and the processors.
and the processors.
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02:17
That drives up the cost of this robot.
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这些让这个飞行机器人的
制造成本十分高昂。
制造成本十分高昂。
02:21
So we asked ourselves a question:
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因此我们问了自己一个问题：
你能在电子产品商店买到怎样的物品，
02:24
what consumer product
can you buy in an electronics store
can you buy in an electronics store
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它既不昂贵，又很轻，
还有机载传感器和计算能力？
还有机载传感器和计算能力？
02:27
that is inexpensive, that's lightweight,
that has sensing onboard and computation?
that has sensing onboard and computation?
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02:36
And we invented the flying phone.
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于是我们发明了会飞的手机。
（笑声）
02:38
(Laughter)
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这个机器人使用了你可以轻松购买到的
三星银河系列手机，
三星银河系列手机，
02:40
So this robot uses a Samsung Galaxy
smartphone that you can buy off the shelf,
smartphone that you can buy off the shelf,
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你所需要的只是一个应用程序，
可以从我们的应用程序商店下载。
可以从我们的应用程序商店下载。
02:46
and all you need is an app that you
can download from our app store.
can download from our app store.
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你们可以看到这个飞行机器人在读字，
“TED”，
“TED”，
02:50
And you can see this robot
reading the letters, "TED" in this case,
reading the letters, "TED" in this case,
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观察字母“T”和“E”角落的位置，
02:55
looking at the corners
of the "T" and the "E"
of the "T" and the "E"
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再应用三角测量法，实现自主飞行。
02:58
and then triangulating off of that,
flying autonomously.
flying autonomously.
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03:02
That joystick is just there
to make sure if the robot goes crazy,
to make sure if the robot goes crazy,
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那个手柄的作用仅限于
当飞行器失控时，
当飞行器失控时，
Giuseppe就能让它失去功能。
03:06
Giuseppe can kill it.
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（笑声）
03:07
(Laughter)
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03:10
In addition to building
these small robots,
these small robots,
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除了制造这些飞行机器人外，
我们还试着让它们做更激烈的动作，
比如这样。
比如这样。
03:14
we also experiment with aggressive
behaviors, like you see here.
behaviors, like you see here.
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03:19
So this robot is now traveling
at two to three meters per second,
at two to three meters per second,
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这个飞行机器人目前的速度是
每秒钟2到3米，
每秒钟2到3米，
在转变方向的时候
激烈地起降和翻转。
激烈地起降和翻转。
03:25
pitching and rolling aggressively
as it changes direction.
as it changes direction.
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重点是，我们还能制造更小，更快的
飞行机器人，
飞行机器人，
03:28
The main point is we can have
smaller robots that can go faster
smaller robots that can go faster
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它们可以在非常复杂的环境中飞行。
03:33
and then travel in these
very unstructured environments.
very unstructured environments.
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03:37
And in this next video,
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下一个影片中，
你们可以看到这只鹰，
优雅地协调它的翅膀，
优雅地协调它的翅膀，
03:39
just like you see this bird, an eagle,
gracefully coordinating its wings,
gracefully coordinating its wings,
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眼睛和爪子之间的配合，
把猎物抓出水面，
把猎物抓出水面，
03:45
its eyes and feet
to grab prey out of the water,
to grab prey out of the water,
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我们的机器人也能去捕鱼。
03:49
our robot can go fishing, too.
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（笑声）
03:51
(Laughter)
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在这个试验中，它在空中抓起了
这个特大号菲力芝士牛排三明治。
这个特大号菲力芝士牛排三明治。
03:52
In this case, this is a Philly cheesesteak
hoagie that it's grabbing out of thin air.
hoagie that it's grabbing out of thin air.
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（笑声）
03:56
(Laughter)
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03:59
So you can see this robot
going at about three meters per second,
going at about three meters per second,
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你们可以看到这个机器人
以每秒3米左右的速度飞行，
以每秒3米左右的速度飞行，
比步行的速度快一些，
同时还能协调它的手臂和爪子，
同时还能协调它的手臂和爪子，
04:03
which is faster than walking speed,
coordinating its arms, its claws
coordinating its arms, its claws
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以极快的速度完成整套动作。
04:08
and its flight with split-second timing
to achieve this maneuver.
to achieve this maneuver.
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04:14
In another experiment,
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在另一个试验中，
我想要展示，
机器人是如何依据悬浮载重
机器人是如何依据悬浮载重
04:15
I want to show you
how the robot adapts its flight
how the robot adapts its flight
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来调整飞行模式的，
04:19
to control its suspended payload,
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它的总长度大于窗子的高度。
04:21
whose length is actually larger
than the width of the window.
than the width of the window.
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04:25
So in order to accomplish this,
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为了完成任务，
它需要向下倾斜，调整高度，
04:27
it actually has to pitch
and adjust the altitude
and adjust the altitude
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然后把重物摆动过去。
04:31
and swing the payload through.
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04:38
But of course we want
to make these even smaller,
to make these even smaller,
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但是，我们希望让机器人变得更小，
而蜜蜂启发了我们。
04:41
and we're inspired
in particular by honeybees.
in particular by honeybees.
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如果你们观察一下蜜蜂，
在这个慢速的视频里，
在这个慢速的视频里，
04:44
So if you look at honeybees,
and this is a slowed down video,
and this is a slowed down video,
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它们是这么小，
惯性是这么轻 ——
惯性是这么轻 ——
04:47
they're so small,
the inertia is so lightweight --
the inertia is so lightweight --
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04:51
(Laughter)
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（笑声）
它们并不在意——
它们会撞击我的手，比如说。
它们会撞击我的手，比如说。
04:53
that they don't care --
they bounce off my hand, for example.
they bounce off my hand, for example.
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这是个小机器人，
可以模拟蜜蜂的行为。
可以模拟蜜蜂的行为。
04:56
This is a little robot
that mimics the honeybee behavior.
that mimics the honeybee behavior.
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05:00
And smaller is better,
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越小越好，
因为尺寸小，惯性就会小。
05:01
because along with the small size
you get lower inertia.
you get lower inertia.
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惯性小——
05:05
Along with lower inertia --
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（机器人嗡嗡声，笑声）
05:06
(Robot buzzing, laughter)
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惯性小，发生撞击的
可能性就低一些。
可能性就低一些。
05:09
along with lower inertia,
you're resistant to collisions.
you're resistant to collisions.
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这使得机器人更耐用。
05:12
And that makes you more robust.
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05:15
So just like these honeybees,
we build small robots.
we build small robots.
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模仿这些蜜蜂，
我们制作了小型机器人。
我们制作了小型机器人。
这个特殊的机器人只有25克重。
05:18
And this particular one
is only 25 grams in weight.
is only 25 grams in weight.
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它的耗电量仅为6瓦。
05:21
It consumes only six watts of power.
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05:24
And it can travel
up to six meters per second.
up to six meters per second.
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它的飞行速度可达每秒6米。
因此，如果我把它的尺寸按比例放大，
05:27
So if I normalize that to its size,
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就好比一架波音787飞机
以10倍于音速的速度飞行。
以10倍于音速的速度飞行。
05:29
it's like a Boeing 787 traveling
ten times the speed of sound.
ten times the speed of sound.
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05:36
(Laughter)
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（笑声）
我还要给你们展示一个例子。
05:38
And I want to show you an example.
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05:40
This is probably the first planned mid-air
collision, at one-twentieth normal speed.
collision, at one-twentieth normal speed.
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这可能是第一个计划中的
以20分之1正常速度进行的空中相撞。
以20分之1正常速度进行的空中相撞。
它们的相对速度为每秒2米，
05:46
These are going at a relative speed
of two meters per second,
of two meters per second,
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这里展示了基本的原理。
05:49
and this illustrates the basic principle.
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05:52
The two-gram carbon fiber cage around it
prevents the propellers from entangling,
prevents the propellers from entangling,
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两克重的碳纤维笼子包围着它
使螺旋桨不会受损，
使螺旋桨不会受损，
但最关键的是，撞击被吸收了，
机器人能够对撞击做出反应。
机器人能够对撞击做出反应。
05:57
but essentially the collision is absorbed
and the robot responds to the collisions.
and the robot responds to the collisions.
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并且，小也意味着安全。
06:02
And so small also means safe.
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06:05
In my lab, as we developed these robots,
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在我的实验室，
最初开发这些机器人的时候，
最初开发这些机器人的时候，
我们从大的机器人开始
06:07
we start off with these big robots
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现在着手做小的机器人。
06:09
and then now we're down
to these small robots.
to these small robots.
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如果你们看一下我们购买
创可贴的数量统计图，
创可贴的数量统计图，
06:11
And if you plot a histogram
of the number of Band-Aids we've ordered
of the number of Band-Aids we've ordered
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就知道现在我们已经几乎不需要买了。
06:15
in the past, that sort of tailed off now.
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因为这些机器人非常安全。
06:18
Because these robots are really safe.
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06:20
The small size has some disadvantages,
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小尺寸也有一些缺点，
但是自然界有很多方法
来弥补这些缺陷。
来弥补这些缺陷。
06:23
and nature has found a number of ways
to compensate for these disadvantages.
to compensate for these disadvantages.
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06:27
The basic idea is they aggregate
to form large groups, or swarms.
to form large groups, or swarms.
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最基本的想法是，
它们可以聚集在一起组成大型的群落。
它们可以聚集在一起组成大型的群落。
06:32
So, similarly, in our lab,
we try to create artificial robot swarms.
we try to create artificial robot swarms.
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因此，在实验室里，
我们也试着去组建机器人群组。
我们也试着去组建机器人群组。
这是非常有难度的工作，
06:36
And this is quite challenging
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因为我们要考虑飞行机器人网络。
06:37
because now you have to think
about networks of robots.
about networks of robots.
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06:41
And within each robot,
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在每一个飞行器中，
我们都需要考虑传感，沟通和计算，
这些互相影响的因素——
这些互相影响的因素——
06:42
you have to think about the interplay
of sensing, communication, computation --
of sensing, communication, computation --
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这样的机器人网络
不易控制和管理。
不易控制和管理。
06:48
and this network then becomes
quite difficult to control and manage.
quite difficult to control and manage.
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06:54
So from nature we take away
three organizing principles
three organizing principles
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从大自然中，我们汲取了
三个组织原则，
三个组织原则，
最终帮助我们完成了算法的发展。
06:57
that essentially allow us
to develop our algorithms.
to develop our algorithms.
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07:01
The first idea is that robots
need to be aware of their neighbors.
need to be aware of their neighbors.
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第一个是，机器人
需要注意到它的邻居们。
需要注意到它的邻居们。
它们要有能力去感知相邻的机器人
并与它们交流。
并与它们交流。
07:06
They need to be able to sense
and communicate with their neighbors.
and communicate with their neighbors.
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07:10
So this video illustrates the basic idea.
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这个视频展示了最基本的想法。
有4个机器人——
07:12
You have four robots --
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其中的一个被人手动控制了。
07:14
one of the robots has actually been
hijacked by a human operator, literally.
hijacked by a human operator, literally.
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07:19
But because the robots
interact with each other,
interact with each other,
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但是因为机器人会相互通讯，
它们能够感知到旁边机器人的行动，
07:21
they sense their neighbors,
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07:23
they essentially follow.
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也会跟着它移动。
07:24
And here there's a single person
able to lead this network of followers.
able to lead this network of followers.
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这个人能够领导整个
飞行机器人群体的行动。
飞行机器人群体的行动。
07:32
So again, it's not because all the robots
know where they're supposed to go.
know where they're supposed to go.
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再强调一次，这并不是因为
所有的机器人都知道它们要去哪里。
所有的机器人都知道它们要去哪里。
而是它们能根据相邻机器人位置变化
做出相应的反应。
做出相应的反应。
07:37
It's because they're just reacting
to the positions of their neighbors.
to the positions of their neighbors.
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07:43
(Laughter)
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（笑声）
07:48
So the next experiment illustrates
the second organizing principle.
the second organizing principle.
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下一个实验展示了第二个组织原则。
07:54
And this principle has to do
with the principle of anonymity.
with the principle of anonymity.
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这个原则与匿名原则有关。
07:59
Here the key idea is that
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其中的关键在于，
机器人是不知道与它们相邻的
机器人的身份的。
机器人的身份的。
08:03
the robots are agnostic
to the identities of their neighbors.
to the identities of their neighbors.
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08:08
They're asked to form a circular shape,
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它们被要求形成一个圆圈，
不管你往这个阵列中放多少个机器人，
08:11
and no matter how many robots
you introduce into the formation,
you introduce into the formation,
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或者是你拿出来多少个机器人，
08:14
or how many robots you pull out,
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每个机器人都会很简单地
根据它相邻机器人的行为做出反应。
根据它相邻机器人的行为做出反应。
08:17
each robot is simply
reacting to its neighbor.
reacting to its neighbor.
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它清楚地知道需要形成圆圈，
08:20
It's aware of the fact that it needs
to form the circular shape,
to form the circular shape,
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与它的相邻机器人配合行动，
08:25
but collaborating with its neighbors
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这个过程不需要中枢系统协调。
08:26
it forms the shape
without central coordination.
without central coordination.
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08:31
Now if you put these ideas together,
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综合这两个想法之后，
第三个想法就是我们会
08:33
the third idea is that we
essentially give these robots
essentially give these robots
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用数学方式来描述
它们需要组成的阵列形状。
它们需要组成的阵列形状。
08:37
mathematical descriptions
of the shape they need to execute.
of the shape they need to execute.
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这些形状会根据时间发生变化，
08:42
And these shapes can be varying
as a function of time,
as a function of time,
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你们看到这些飞行器
从一个圆形开始，
从一个圆形开始，
08:45
and you'll see these robots
start from a circular formation,
start from a circular formation,
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接着变为长方形，
然后变成一条直线，
然后变成一条直线，
08:50
change into a rectangular formation,
stretch into a straight line,
stretch into a straight line,
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又变回椭圆形。
08:53
back into an ellipse.
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它们通过瞬间的协调
来完成这些动作，
来完成这些动作，
08:54
And they do this with the same
kind of split-second coordination
kind of split-second coordination
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就像自然界里的蜂群一样。
08:58
that you see in natural swarms, in nature.
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09:03
So why work with swarms?
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为什么要模仿蜂群呢？
我们对这项技术的两种应用
非常感兴趣。
非常感兴趣。
09:05
Let me tell you about two applications
that we are very interested in.
that we are very interested in.
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09:10
The first one has to do with agriculture,
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第一个有关于农业，
农业应该是全球面临最严峻的问题。
09:12
which is probably the biggest problem
that we're facing worldwide.
that we're facing worldwide.
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09:16
As you well know,
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大家都知道，
地球上每7个人中就有1个营养不良。
09:18
one in every seven persons
in this earth is malnourished.
in this earth is malnourished.
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09:21
Most of the land that we can cultivate
has already been cultivated.
has already been cultivated.
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我们耕种了绝大部分可耕的土地。
09:25
And the efficiency of most systems
in the world is improving,
in the world is improving,
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世界上很多系统的效率都在提高，
但是我们生产系统的
效率事实上却是在下降。
效率事实上却是在下降。
09:29
but our production system
efficiency is actually declining.
efficiency is actually declining.
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09:33
And that's mostly because of water
shortage, crop diseases, climate change
shortage, crop diseases, climate change
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很大程度上源于水源短缺，
作物病害，气候变化
作物病害，气候变化
和许多其他的原因。
09:37
and a couple of other things.
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09:39
So what can robots do?
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那么，这些机器人能做什么呢？
09:41
Well, we adopt an approach that's
called Precision Farming in the community.
called Precision Farming in the community.
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我们称之为社区里的精密耕作。
我们基本的想法是
让这些飞行机器人飞越果园，
让这些飞行机器人飞越果园，
09:45
And the basic idea is that we fly
aerial robots through orchards,
aerial robots through orchards,
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为每一株植物搭建精密的模型。
09:51
and then we build
precision models of individual plants.
precision models of individual plants.
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09:54
So just like personalized medicine,
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就像个性化的机器，
想象每个病人都有一对一服务，
09:56
while you might imagine wanting
to treat every patient individually,
to treat every patient individually,
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我们要做的就是
为每株植物单独建立模型，
为每株植物单独建立模型，
10:01
what we'd like to do is build
models of individual plants
models of individual plants
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然后告诉农民
每株植物各需要些什么——
每株植物各需要些什么——
10:05
and then tell the farmer
what kind of inputs every plant needs --
what kind of inputs every plant needs --
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需要的因素在这里指的是水，
肥料和杀虫剂。
肥料和杀虫剂。
10:09
the inputs in this case being water,
fertilizer and pesticide.
fertilizer and pesticide.
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10:14
Here you'll see robots
traveling through an apple orchard,
traveling through an apple orchard,
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你们现在看到的是
机器人飞过一片苹果园，
机器人飞过一片苹果园，
很快你们就会看到另外两个机器人
10:18
and in a minute you'll see
two of its companions
two of its companions
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在左侧做着同样的事。
10:20
doing the same thing on the left side.
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10:22
And what they're doing is essentially
building a map of the orchard.
building a map of the orchard.
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它们在制作果园的地图。
这个地图里有果园里每株植物的位置。
10:26
Within the map is a map
of every plant in this orchard.
of every plant in this orchard.
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10:29
(Robot buzzing)
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（机器人嗡嗡声）
我们来看一下
这些地图是什么样子的。
这些地图是什么样子的。
10:31
Let's see what those maps look like.
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在下一个视频中，你们会看到
机器人上安装的各种摄像头。
机器人上安装的各种摄像头。
10:32
In the next video, you'll see the cameras
that are being used on this robot.
that are being used on this robot.
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左上角的是一个高清彩色摄像机。
10:37
On the top-left is essentially
a standard color camera.
a standard color camera.
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10:41
On the left-center is an infrared camera.
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左边中间的是一个红外线摄像机。
左下方的是一个热成像摄像机。
10:44
And on the bottom-left
is a thermal camera.
is a thermal camera.
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在主面板上，你们会看到
每一株果树的三维图像。
每一株果树的三维图像。
10:48
And on the main panel, you're seeing
a three-dimensional reconstruction
a three-dimensional reconstruction
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这是当机器人飞过果树时
传感器采集到的数据。
传感器采集到的数据。
10:52
of every tree in the orchard
as the sensors fly right past the trees.
as the sensors fly right past the trees.
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10:59
Armed with information like this,
we can do several things.
we can do several things.
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有了这些信息，
我们就可以做很多事情。
我们就可以做很多事情。
11:04
The first and possibly the most important
thing we can do is very simple:
thing we can do is very simple:
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我们能做的第一件也可能是最重要的事
非常简单：
非常简单：
清点每株果树上果实的数量。
11:08
count the number of fruits on every tree.
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11:11
By doing this, you tell the farmer
how many fruits she has in every tree
how many fruits she has in every tree
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这样，你就能告诉农民
每株果树上有多少果实，
每株果树上有多少果实，
农民就能估算出果园的产量，
11:16
and allow her to estimate
the yield in the orchard,
the yield in the orchard,
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并调整下游的生产链。
11:20
optimizing the production
chain downstream.
chain downstream.
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11:23
The second thing we can do
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我们能做的第二件事是
给植物建构三维立体图，
11:25
is take models of plants, construct
three-dimensional reconstructions,
three-dimensional reconstructions,
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从而推算出树冠的尺寸，
11:29
and from that estimate the canopy size,
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然后通过树冠尺寸推算出
每株植物的树叶面积。
每株植物的树叶面积。
11:32
and then correlate the canopy size
to the amount of leaf area on every plant.
to the amount of leaf area on every plant.
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这被称为树叶面积指数。
11:36
And this is called the leaf area index.
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那么如果我们知道一棵树的
树叶面积指数，
树叶面积指数，
11:38
So if you know this leaf area index,
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就能大概测算出这株果树
在进行多少光合作用，
在进行多少光合作用，
11:40
you essentially have a measure of how much
photosynthesis is possible in every plant,
photosynthesis is possible in every plant,
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就能知道这株果树的健康状况。
11:45
which again tells you
how healthy each plant is.
how healthy each plant is.
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11:49
By combining visual
and infrared information,
and infrared information,
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将视觉信息和红外线信息合起来，
我们还能计算出一些指数，
例如常态化差值植生指数。
例如常态化差值植生指数。
11:53
we can also compute indices such as NDVI.
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在这个例子中，
你们可以明显看到
你们可以明显看到
11:57
And in this particular case,
you can essentially see
you can essentially see
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有一些植物并不像其他植物那样健康。
11:59
there are some crops that are
not doing as well as other crops.
not doing as well as other crops.
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在图像中很容易识别，
12:02
This is easily discernible from imagery,
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不仅是在视觉图像中，而是结合了
12:07
not just visual imagery but combining
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视觉和红外线的图像中。
12:09
both visual imagery and infrared imagery.
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最后，
12:12
And then lastly,
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我们感兴趣的另一件事
是检测早期的植被萎黄病——
是检测早期的植被萎黄病——
12:13
one thing we're interested in doing is
detecting the early onset of chlorosis --
detecting the early onset of chlorosis --
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这是一棵橘子树——
12:17
and this is an orange tree --
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你们可以看到泛黄的树叶。
12:19
which is essentially seen
by yellowing of leaves.
by yellowing of leaves.
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12:21
But robots flying overhead
can easily spot this autonomously
can easily spot this autonomously
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当机器人飞过这棵树的顶部
就能很快自主识别出来，
就能很快自主识别出来，
然后报告农民
他/她遇到问题了，
他/她遇到问题了，
12:25
and then report to the farmer
that he or she has a problem
that he or she has a problem
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就在果园的这个区域。
12:28
in this section of the orchard.
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12:30
Systems like this can really help,
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这样的系统真的非常实用，
我们估计这能帮助农业产量提升10%，
12:33
and we're projecting yields
that can improve by about ten percent
that can improve by about ten percent
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更重要的是，通过使用飞行器蜂群，
12:39
and, more importantly, decrease
the amount of inputs such as water
the amount of inputs such as water
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水资源的用量能够降低25%。
12:42
by 25 percent by using
aerial robot swarms.
aerial robot swarms.
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12:47
Lastly, I want you to applaud
the people who actually create the future,
the people who actually create the future,
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最后，我希望大家为创造未来的人们
致以热烈的掌声，
致以热烈的掌声，
Yash Mulgaonkar, Sikang Liu
和Giuseppe Loianno,
和Giuseppe Loianno,
12:52
Yash Mulgaonkar, Sikang Liu
and Giuseppe Loianno,
and Giuseppe Loianno,
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你们看到的三次演示都是他们完成的。
12:57
who are responsible for the three
demonstrations that you saw.
demonstrations that you saw.
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谢谢。
13:01
Thank you.
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（掌声）
13:02
(Applause)
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ABOUT THE SPEAKER
Vijay Kumar - RoboticistAs the dean of the University of Pennsylvania's School of Engineering and Applied Science, Vijay Kumar studies the control and coordination of multi-robot formations.
Why you should listen
At the General Robotics, Automation, Sensing and Perception (GRASP) Lab at the University of Pennsylvania, flying quadrotor robots move together in eerie formation, tightening themselves into perfect battalions, even filling in the gap when one of their own drops out. You might have seen viral videos of the quads zipping around the netting-draped GRASP Lab (they juggle! they fly through a hula hoop!). Vijay Kumar headed this lab from 1998-2004. He's now the dean of the School of Engineering and Applied Science at the University of Pennsylvania in Philadelphia, where he continues his work in robotics, blending computer science and mechanical engineering to create the next generation of robotic wonders.
Vijay Kumar | Speaker | TED.com