TEDxPenn
Vijay Kumar: The future of flying robots
偉傑．庫馬爾: 無人機的未來
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在他賓夕法尼亞大學的實驗室裡頭，偉傑．庫馬爾與他的團隊以蜜蜂為靈感，開發了自動飛行機器人（無人機）。他們最新的突破用於精准農業，運用無人機集群來勘查、重建與分析果園裡的每一株植物，每一個果實，為農民提供重要的資訊，幫助他們增加畝產，並進行更智能化的灌溉管理。
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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在我的實驗室，我們製造
自動式飛行測量機器人，
自動式飛行測量機器人，
00:16
like the one you see flying here.
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比如現在正在飛的這個。
00:20
Unlike the commercially available drones
that you can buy today,
that you can buy today,
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和那些可以在市場上
買到的飛行器不同的是，
買到的飛行器不同的是，
00:24
this robot doesn't have any GPS on board.
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這個飛行器並沒有搭載全球定位系統（GPS）。
00:28
So without GPS,
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沒有定位系統，
00:29
it's hard for robots like this
to determine their position.
to determine their position.
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飛行器很難確定它們自己所在的位置。
00:34
This robot uses onboard sensors,
cameras and laser scanners,
cameras and laser scanners,
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這個飛行器自帶傳感器、
攝像機和激光掃描儀，
攝像機和激光掃描儀，
00:39
to scan the environment.
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用於掃描檢測周圍的環境。
00:40
It detects features from the environment,
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飛行器從周圍環境中探測物體，
00:43
and it determines where it is
relative to those features,
relative to those features,
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用三角測量法，
00:46
using a method of triangulation.
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確定自己和周圍物體的相對位置和距離。
00:48
And then it can assemble
all these features into a map,
all these features into a map,
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然後將這些物體整合到一張地圖中，
00:52
like you see behind me.
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像我身後展示的這樣。
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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這個地圖可以使飛行器了解
那些外界物體的位置，
那些外界物體的位置，
00:57
and navigate in a collision-free manner.
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從而在導航的時候避免碰撞。
01:01
What I want to show you next
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接下來我想展示給你們的
01:03
is a set of experiments
we did inside our laboratory,
we did inside our laboratory,
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是我們在實驗室中做的一系列實驗，
01:06
where this robot was able
to go for longer distances.
to go for longer distances.
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在實驗室中飛行器可以進行長距離飛行。
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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你們看，在右上方的位置，是飛行器
在攝像機中“看”到的場景，
在攝像機中“看”到的場景，
01:15
And on the main screen --
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以及在主屏幕中的樣子，
01:16
and of course this is sped up
by a factor of four --
by a factor of four --
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當然這個視頻是以4倍速度在播放，
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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在主屏幕上你可以看到地圖的搭建進程。
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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這是一個高分辨率的實驗室周圍走廊的地圖。
01:26
And in a minute
you'll see it enter our lab,
you'll see it enter our lab,
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馬上你們將看到飛行器飛入我們的實驗室，
01:28
which is recognizable
by the clutter that you see.
by the clutter that you see.
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一看這麼亂就知道是我們實驗室。
01:31
(Laughter)
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（笑聲）
01:32
But the main point I want to convey to you
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我想說的重點是，
01:34
is that these robots are capable
of building high-resolution maps
of building high-resolution maps
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這些飛行器可以創建
高達5釐米*5釐米（每像素）的
高達5釐米*5釐米（每像素）的
01:38
at five centimeters resolution,
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高分辨率地圖，
01:40
allowing somebody who is outside the lab,
or outside the building
or outside the building
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可以使那些在實驗室外或者建築物外的人，
01:44
to deploy these
without actually going inside,
without actually going inside,
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不用進入內部就可以進行部署，
01:48
and trying to infer
what happens inside the building.
what happens inside the building.
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並嘗試推斷建築物內部的事態情況。
01:52
Now there's one problem
with robots like this.
with robots like this.
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這些飛行器有一些主要的缺點。
01:55
The first problem is it's pretty big.
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第一，它體積很大。
01:58
Because it's big, it's heavy.
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因為它體積較大，所以比較重。
02:00
And these robots consume
about 100 watts per pound.
about 100 watts per pound.
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這些飛行器消耗大概每磅消耗100瓦能量，
02:04
And this makes for
a very short mission life.
a very short mission life.
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這就使得它們只能執行短時間任務。
02:08
The second problem
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第二個缺點，
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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這些飛行器自帶的傳感器很昂貴：
02:13
a laser scanner, a camera
and the processors.
and the processors.
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包括一個雷射光光掃描儀、一個攝像機，
還有多個處理器，
還有多個處理器，
02:17
That drives up the cost of this robot.
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這些都使得飛行器的製造成本上升。
02:21
So we asked ourselves a question:
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於是我們就問自己：
02:24
what consumer product
can you buy in an electronics store
can you buy in an electronics store
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什麼樣的產品是可以在電子商店裡買到的？
02:27
that is inexpensive, that's lightweight,
that has sensing onboard and computation?
that has sensing onboard and computation?
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那種價格不貴的、輕便，
並且自帶傳感器和處理器的......
並且自帶傳感器和處理器的......
02:36
And we invented the flying phone.
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於是我們發明了“飛行手機”。
02:38
(Laughter)
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（笑聲）
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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這個飛行器搭載了一個
常見的三星智慧型手機，
常見的三星智慧型手機，
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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另外你只需要一個手機應用，
可以在我們的應用商店下載。
可以在我們的應用商店下載。
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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你們可以看到這個飛行器
正在讀TED這幾個字母，
正在讀TED這幾個字母，
02:55
looking at the corners
of the "T" and the "E"
of the "T" and the "E"
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它主要關注T和E這些字母的邊角，
02:58
and then triangulating off of that,
flying autonomously.
flying autonomously.
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然後應用三角測量法自主飛行。
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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那個手柄只是放在那，避免飛行器突然失控，
03:06
Giuseppe can kill it.
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Giuseppe同學就能廢了它。
03:07
(Laughter)
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（笑聲）
03:10
In addition to building
these small robots,
these small robots,
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除了製作這些小飛行器之外，
03:14
we also experiment with aggressive
behaviors, like you see here.
behaviors, like you see here.
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我們還實驗了一些比較激烈的動作。
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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這個飛行器的當前速度是2到3米每秒，
03:25
pitching and rolling aggressively
as it changes direction.
as it changes direction.
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較為激烈地起落和翻滾。
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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重點是，我們可以製作更小的飛行器，
它們飛得更快，
它們飛得更快，
03:33
and then travel in these
very unstructured environments.
very unstructured environments.
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它們可以在不規則的環境中飛行。
03:37
And in this next video,
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在下一個視頻中，
03:39
just like you see this bird, an eagle,
gracefully coordinating its wings,
gracefully coordinating its wings,
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就像你看到的這隻鷹很優雅地扇動翅膀，
03:45
its eyes and feet
to grab prey out of the water,
to grab prey out of the water,
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和眼睛、爪子一起協調，從水中抓魚，
03:49
our robot can go fishing, too.
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我們的飛行器也能這麼抓魚。
03:51
(Laughter)
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（笑聲）
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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在這裡，它是從空氣中抓到一個
菲力芝士牛排三明治。
菲力芝士牛排三明治。
03:56
(Laughter)
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（笑聲）
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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這個飛行器大概每秒飛行3公尺，
04:03
which is faster than walking speed,
coordinating its arms, its claws
coordinating its arms, its claws
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比我們步行速度更快，
它在瞬間協調了機械臂、機械爪
它在瞬間協調了機械臂、機械爪
04:08
and its flight with split-second timing
to achieve this maneuver.
to achieve this maneuver.
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和飛行動作來完成整個流程。
04:14
In another experiment,
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在另一個實驗中，
04:15
I want to show you
how the robot adapts its flight
how the robot adapts its flight
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我將展示給各位這個飛行器如何自適應飛行，
04:19
to control its suspended payload,
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來控制它的懸掛負載。
04:21
whose length is actually larger
than the width of the window.
than the width of the window.
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這個負載的長度比飛行器
要穿過的窗戶還要大。
要穿過的窗戶還要大。
04:25
So in order to accomplish this,
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為了做到這一點，
04:27
it actually has to pitch
and adjust the altitude
and adjust the altitude
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它必須利用起落來控制高度，
04:31
and swing the payload through.
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並且通過擺動將負載擺過窗子。
04:38
But of course we want
to make these even smaller,
to make these even smaller,
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當然，我們想把這些飛行器變得更小。
04:41
and we're inspired
in particular by honeybees.
in particular by honeybees.
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我們受到了蜜蜂的啟發。
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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如果你仔細觀察蜜蜂，在這個慢速的視頻中，
04:47
they're so small,
the inertia is so lightweight --
the inertia is so lightweight --
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它們體積很小，慣性也很小，
04:51
(Laughter)
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（笑聲）
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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所以它們并不在意 - 比如說從我手上彈開。
04:56
This is a little robot
that mimics the honeybee behavior.
that mimics the honeybee behavior.
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這是一個模仿蜜蜂動作的小型飛行器。
05:00
And smaller is better,
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越小越好。
05:01
because along with the small size
you get lower inertia.
you get lower inertia.
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因為更小的體積會有更小的慣性。
05:05
Along with lower inertia --
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更小的慣性 --
05:06
(Robot buzzing, laughter)
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（飛行器蜂鳴，笑聲）
05:09
along with lower inertia,
you're resistant to collisions.
you're resistant to collisions.
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更小的慣性，就更加不怕碰撞，
05:12
And that makes you more robust.
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這使飛行器更加強壯。
05:15
So just like these honeybees,
we build small robots.
we build small robots.
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所以就像這些蜜蜂一樣，
我們製造了小型飛行器。
我們製造了小型飛行器。
05:18
And this particular one
is only 25 grams in weight.
is only 25 grams in weight.
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你們看到的這個只有25公克重，
05:21
It consumes only six watts of power.
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它只消耗6瓦能量。
05:24
And it can travel
up to six meters per second.
up to six meters per second.
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它可以每秒飛行6公尺。
05:27
So if I normalize that to its size,
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所以如果我把它的尺寸標準化，
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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它就像是一架以10倍
音速飛行的波音787飛機。
音速飛行的波音787飛機。
05:36
(Laughter)
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（笑聲）
05:38
And I want to show you an example.
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我還想給你們看一個例子。
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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這可能是第一個計劃性的控制碰撞，
降速20倍播放。
降速20倍播放。
05:46
These are going at a relative speed
of two meters per second,
of two meters per second,
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兩個飛行器以相對速度每秒2米飛行，
05:49
and this illustrates the basic principle.
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這闡釋了基本原理。
05:52
The two-gram carbon fiber cage around it
prevents the propellers from entangling,
prevents the propellers from entangling,
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圍住飛行器的是碳纖維架，重2公克。
用於防止兩架飛行器的螺旋槳卷到一起。
用於防止兩架飛行器的螺旋槳卷到一起。
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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最終碰撞力量被吸收了，
飛行器也對碰撞有良好反應。
飛行器也對碰撞有良好反應。
06:02
And so small also means safe.
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所以小尺寸意味著高安全性。
06:05
In my lab, as we developed these robots,
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在我的實驗室中，
就像我們研發這些飛行器一樣，
就像我們研發這些飛行器一樣，
06:07
we start off with these big robots
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我們從研發大型飛行器開始。
06:09
and then now we're down
to these small robots.
to these small robots.
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到現在我們研發這些小飛行器。
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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如果用一個柱狀圖來記錄
我們買過的OK繃的數量，
我們買過的OK繃的數量，
06:15
in the past, that sort of tailed off now.
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現在我們幾乎不需要買了。
06:18
Because these robots are really safe.
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因為這些飛行器非常安全。
06:20
The small size has some disadvantages,
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小體積也有一些缺陷，
06:23
and nature has found a number of ways
to compensate for these disadvantages.
to compensate for these disadvantages.
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但是自然界找到了很多方法來彌補這些缺點。
06:27
The basic idea is they aggregate
to form large groups, or swarms.
to form large groups, or swarms.
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最基本的概念是牠們
集合到一起組成大型的群族。
集合到一起組成大型的群族。
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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所以同樣的在我們實驗室，
我們嘗試創建機器人（飛行器）群族。
我們嘗試創建機器人（飛行器）群族。
06:36
And this is quite challenging
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這並不容易，
06:37
because now you have to think
about networks of robots.
about networks of robots.
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因為我們需要考慮飛行器的網絡操作。
06:41
And within each robot,
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在每一個飛行器中，
06:42
you have to think about the interplay
of sensing, communication, computation --
of sensing, communication, computation --
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我們需要顧及到傳感、通訊
和計算的相互作用 --
和計算的相互作用 --
06:48
and this network then becomes
quite difficult to control and manage.
quite difficult to control and manage.
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這個網絡結構非常難以控制和管理。
06:54
So from nature we take away
three organizing principles
three organizing principles
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所以我們從大自然中學習到三個組織原則，
06:57
that essentially allow us
to develop our algorithms.
to develop our algorithms.
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最終幫助我們完成了算法。
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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第一個是，飛行器需要關注
附近的其他飛行器。
附近的其他飛行器。
07:06
They need to be able to sense
and communicate with their neighbors.
and communicate with their neighbors.
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它們要能夠探測到附近的
其他飛行器並與其進行通訊。
其他飛行器並與其進行通訊。
07:10
So this video illustrates the basic idea.
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這個視頻闡述了基本概念。
07:12
You have four robots --
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有四台飛行器，
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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其中一台被實驗員握在手中。
07:19
But because the robots
interact with each other,
interact with each other,
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但是因為每個飛行器都與其他飛行器有交互，
07:21
they sense their neighbors,
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它們能探測到彼此，
07:23
they essentially follow.
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就會跟隨實驗員手中的飛行器。
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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所以這個實驗員能夠主導這一群飛行器。
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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這並不是因為這些飛行器
知道它們該飛往哪裡。
知道它們該飛往哪裡。
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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這隻是因為它們在對
彼此的位置進行交互和反饋。
彼此的位置進行交互和反饋。
07:43
(Laughter)
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（笑聲）
07:48
So the next experiment illustrates
the second organizing principle.
the second organizing principle.
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下一個實驗闡述了第二個自然組織法則。
07:54
And this principle has to do
with the principle of anonymity.
with the principle of anonymity.
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這個原則與匿名原則有關。
07:59
Here the key idea is that
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重點在於，
08:03
the robots are agnostic
to the identities of their neighbors.
to the identities of their neighbors.
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每個飛行器並不知道
附近其他飛行器的身份。
附近其他飛行器的身份。
08:08
They're asked to form a circular shape,
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這些飛行器被要求組成一個圓形陣列，
08:11
and no matter how many robots
you introduce into the formation,
you introduce into the formation,
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不管你增加多少飛行器進去，
08:14
or how many robots you pull out,
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或者拿開多少飛行器，
08:17
each robot is simply
reacting to its neighbor.
reacting to its neighbor.
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每個飛行器都只是在跟附近的飛行器交互。
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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它們知道它們需要組成圓陣，
08:25
but collaborating with its neighbors
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與其他飛行器進行配合，
08:26
it forms the shape
without central coordination.
without central coordination.
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這個過程並不需要中樞協調。
08:31
Now if you put these ideas together,
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現在如果我們綜合來看，
08:33
the third idea is that we
essentially give these robots
essentially give these robots
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第三個原則是我們最終應用到飛行器上的，
08:37
mathematical descriptions
of the shape they need to execute.
of the shape they need to execute.
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用數學來描述它們需要組成的陣列形狀。
08:42
And these shapes can be varying
as a function of time,
as a function of time,
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這些形狀可以根據一個時間函數進行變化，
08:45
and you'll see these robots
start from a circular formation,
start from a circular formation,
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我們會看到這些飛行器從一個圓形陣列開始，
08:50
change into a rectangular formation,
stretch into a straight line,
stretch into a straight line,
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接著變為長方形陣，然後是一條直線，
08:53
back into an ellipse.
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最後變回橢圓形陣。
08:54
And they do this with the same
kind of split-second coordination
kind of split-second coordination
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它們同樣是瞬間協調來完成這些動作，
08:58
that you see in natural swarms, in nature.
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就像自然界的群族一樣。
09:03
So why work with swarms?
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那麼為什麼要模仿群族呢？
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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我們對兩方面的應用很感興趣。
09:10
The first one has to do with agriculture,
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首先是農業，
09:12
which is probably the biggest problem
that we're facing worldwide.
that we're facing worldwide.
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這可能是世界範圍內最嚴峻的問題。
09:16
As you well know,
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你們都知道，
09:18
one in every seven persons
in this earth is malnourished.
in this earth is malnourished.
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世界上每七個人中就有一個人營養不良。
09:21
Most of the land that we can cultivate
has already been cultivated.
has already been cultivated.
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絕大部分可用耕地都已經被開墾耕種。
09:25
And the efficiency of most systems
in the world is improving,
in the world is improving,
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世界上大多數系統的效率都在提升，
09:29
but our production system
efficiency is actually declining.
efficiency is actually declining.
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但是我們生產系統的效率卻在降低。
09:33
And that's mostly because of water
shortage, crop diseases, climate change
shortage, crop diseases, climate change
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這主要是由於水資源缺乏、
農作物疾病和氣候變化，
農作物疾病和氣候變化，
09:37
and a couple of other things.
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和其他的一些原因。
09:39
So what can robots do?
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那麼機器人能做些什麼呢？
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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我們在社區中採用了一種
叫做“精確種植”的方法。
叫做“精確種植”的方法。
09:45
And the basic idea is that we fly
aerial robots through orchards,
aerial robots through orchards,
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基本思路是用機器人飛行器在果園中飛行，
09:51
and then we build
precision models of individual plants.
precision models of individual plants.
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為每一棵農作物搭建精確的模型。
09:54
So just like personalized medicine,
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就像個體化用藥一樣，
09:56
while you might imagine wanting
to treat every patient individually,
to treat every patient individually,
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對每個病人要對症下藥，
10:01
what we'd like to do is build
models of individual plants
models of individual plants
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我們所做的是給每一棵農作物創建模型，
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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並告訴農民每一棵農作物分別需要什麼：
10:09
the inputs in this case being water,
fertilizer and pesticide.
fertilizer and pesticide.
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可能是水、也可能是化肥或者殺蟲劑。
10:14
Here you'll see robots
traveling through an apple orchard,
traveling through an apple orchard,
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你們可以看到飛行器正在蘋果園中穿行，
10:18
and in a minute you'll see
two of its companions
two of its companions
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馬上你們還會看到另外兩台飛行器，
10:20
doing the same thing on the left side.
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正在左邊做同樣的工作。
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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它們正在創建果園的地圖
10:26
Within the map is a map
of every plant in this orchard.
of every plant in this orchard.
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每一棵果樹都將呈現在地圖上。
10:29
(Robot buzzing)
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（飛行器蜂鳴）
10:31
Let's see what those maps look like.
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我們來看看這些地圖吧。
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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下一個視頻中你們將看到搭載在飛行器上的攝像機。
10:37
On the top-left is essentially
a standard color camera.
a standard color camera.
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左上方是一個高級彩色攝像機
10:41
On the left-center is an infrared camera.
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中間的是一個紅外線攝像機。
10:44
And on the bottom-left
is a thermal camera.
is a thermal camera.
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左下方是一個熱感攝像機。
10:48
And on the main panel, you're seeing
a three-dimensional reconstruction
a three-dimensional reconstruction
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當飛行器在飛過果樹時由傳感器採集數據，
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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在主面板上你將看到每一棵果樹的三維重建。
10:59
Armed with information like this,
we can do several things.
we can do several things.
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有了這些數據，我們可以做一些研究。
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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我們能做的第一件事
也是最重要的事很簡單：
也是最重要的事很簡單：
11:08
count the number of fruits on every tree.
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計算每一棵果樹上果實的數量。
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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有了這個，我們可以告訴農民
她的每一棵果樹上都有多少果子，
她的每一棵果樹上都有多少果子，
11:16
and allow her to estimate
the yield in the orchard,
the yield in the orchard,
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由她來估算整個果園的產量，
11:20
optimizing the production
chain downstream.
chain downstream.
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從而優化下游產業鏈。
11:23
The second thing we can do
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我們能做的第二件事，
11:25
is take models of plants, construct
three-dimensional reconstructions,
three-dimensional reconstructions,
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是對果樹建模，進行三維重建，
11:29
and from that estimate the canopy size,
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從而估算樹冠的大小，
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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將每一棵果樹的樹冠尺寸和樹葉面積做關聯。
11:36
And this is called the leaf area index.
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這被稱作是“葉面積指數”。
11:38
So if you know this leaf area index,
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如果我們知道一棵樹的葉面積指數，
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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就能大概知道這棵果樹在進行多少光合作用，
11:45
which again tells you
how healthy each plant is.
how healthy each plant is.
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於是就能得知這棵果樹是否健康。
11:49
By combining visual
and infrared information,
and infrared information,
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將視覺信息和紅外信息整合起來，
11:53
we can also compute indices such as NDVI.
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我們就能計算一些指數，
例如歸一化植被指數（NDVI）。
例如歸一化植被指數（NDVI）。
11:57
And in this particular case,
you can essentially see
you can essentially see
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在我們的這個案例中，能夠看到，
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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有一些作物並不像其他作物那樣健康。
12:02
This is easily discernible from imagery,
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這在圖像中是很容易辨別的。
12:07
not just visual imagery but combining
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不是在視覺圖像中，而是在
12:09
both visual imagery and infrared imagery.
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視覺和紅外線合成的圖像中。
12:12
And then lastly,
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最後，
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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我們很想做的一件事是，
檢測早期的樹葉萎黃病。
檢測早期的樹葉萎黃病。
12:17
and this is an orange tree --
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這是一顆橘子樹，
12:19
which is essentially seen
by yellowing of leaves.
by yellowing of leaves.
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可以看到泛黃的樹葉。
12:21
But robots flying overhead
can easily spot this autonomously
can easily spot this autonomously
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飛行器在上空飛行的時候可以
輕易地自動檢測到這一現象，
輕易地自動檢測到這一現象，
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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然後向農民報告，在果園中的這一區域
12:28
in this section of the orchard.
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出現了問題。
12:30
Systems like this can really help,
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這個系統對農業生產非常有用，
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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我們估計農業產量將會上升10%，
12:39
and, more importantly, decrease
the amount of inputs such as water
the amount of inputs such as water
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更重要的是，通過使用飛行器群族，
12:42
by 25 percent by using
aerial robot swarms.
aerial robot swarms.
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降低25%的水資源用量。
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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最後，我希望大家向創造了
未來的人們致以掌聲，
未來的人們致以掌聲，
12:52
Yash Mulgaonkar, Sikang Liu
and Giuseppe Loianno,
and Giuseppe Loianno,
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Yash Mulgaonkar， Sikang Liu
和 Giuseppe Loianno，
和 Giuseppe Loianno，
12:57
who are responsible for the three
demonstrations that you saw.
demonstrations that you saw.
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他們負責你們看到的三次演示。
13:01
Thank you.
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謝謝！
13:02
(Applause)
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（掌聲）
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