The Future of Flying Robots | Vijay Kumar | TED Talks

755,311 views ・ 2015-11-04

TED


請雙擊下方英文字幕播放視頻。

譯者: Songling Zhang 審譯者: Karen SONG
00:13
In my lab, we build 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,
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,
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和那些可以在市場上 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.
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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,
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這個飛行器自帶傳感器、 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,
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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,
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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
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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,
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,
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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.
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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.
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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,
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 --
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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.
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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.
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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,
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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.
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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
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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
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
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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,
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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.
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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.
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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.
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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.
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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 --
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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.
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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,
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
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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?
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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......
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,
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這個飛行器搭載了一個 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.
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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。
02:50
And you can see this robot 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,
02:55
looking at the corners 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.
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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,
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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,
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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.
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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,
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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.
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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
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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,
03:33
and then travel in these 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,
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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,
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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.
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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。
03:56
(Laughter)
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(笑聲)
03:59
So you can see this robot 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
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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
04:08
and its flight with split-second timing 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
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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.
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這個負載的長度比飛行器 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
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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,
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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.
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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,
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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 --
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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.
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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.
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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.
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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.
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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.
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所以就像這些蜜蜂一樣, 我們製造了小型飛行器。
05:18
And this particular one 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.
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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.
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它就像是一架以10倍 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.
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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。
05:46
These are going at a relative speed 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,
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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。
05:57
but essentially the collision is absorbed 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。
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,
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.
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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
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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,
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.
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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.
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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。
06:32
So, similarly, in our lab, 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。
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.
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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 --
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我們需要顧及到傳感、通訊 和計算的相互作用 --
06:48
and this network then becomes 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
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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.
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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.
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第一個是,飛行器需要關注 附近的其他飛行器。
07:06
They need to be able to sense 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。
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.
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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,
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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.
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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.
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這並不是因為這些飛行器 知道它們該飛往哪裡。
07:37
It's because they're just reacting 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。
07:43
(Laughter)
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(笑聲)
07:48
So the next experiment illustrates 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.
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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.
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每個飛行器並不知道 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,
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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.
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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,
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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.
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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
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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.
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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,
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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,
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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,
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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
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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.
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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.
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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.
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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.
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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,
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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.
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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
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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,
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.
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我們在社區中採用了一種 叫做“精確種植”的方法。
09:45
And the basic idea is that we fly 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.
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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,
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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
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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 --
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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.
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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,
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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
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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.
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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.
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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.
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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.
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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.
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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
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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.
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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.
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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:
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我們能做的第一件事 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
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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,
11:16
and allow her to estimate 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.
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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,
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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.
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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,
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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.
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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,
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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)。
11:57
And in this particular case, 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.
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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 --
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我們很想做的一件事是, 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.
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可以看到泛黃的樹葉。
12:21
But robots flying overhead 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,
12:25
and then report to the farmer 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
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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
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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.
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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,
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最後,我希望大家向創造了 未來的人們致以掌聲,
12:52
Yash Mulgaonkar, Sikang Liu and Giuseppe Loianno,
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Yash Mulgaonkar, Sikang Liu 和 Giuseppe Loianno,
12:57
who are responsible for the three 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)
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