Over a million people are killed each year in disasters. Two and a half million people will be permanently disabled or displaced, and the communities will take 20 to 30 years to recover and billions of economic losses.
每年超過百萬人死於災難。 二百五十萬人 永久傷殘或流離失所, 受災社區要花 二三十年重建恢復, 還有大量的經濟損失。
If you can reduce the initial response by one day, you can reduce the overall recovery by a thousand days, or three years. See how that works? If the initial responders can get in, save lives, mitigate whatever flooding danger there is, that means the other groups can get in to restore the water, the roads, the electricity, which means then the construction people, the insurance agents, all of them can get in to rebuild the houses, which then means you can restore the economy, and maybe even make it better and more resilient to the next disaster. A major insurance company told me that if they can get a homeowner's claim processed one day earlier, it'll make a difference of six months in that person getting their home repaired.
如果你能將 初始應變時間縮短一天, 就能加快整體恢復時間 一千天,即三年。 這要如何達成? 如果第一批救災人員 能進入災區、拯救生命、 減輕各種危險造成的災害, 那麼其他團體就能進入 恢復供水、供電、搶修道路, 也就是說之後 施工人員及保險公司 都可以進入重建房子, 也就是說你能恢復經濟, 甚至還能變得更好, 更有能力應變下一場災害。 一家大保險公司告訴我 如果他們能早一天 處理屋主的索賠, 就能讓屋主 早六個月修好房屋。
And that's why I do disaster robotics -- because robots can make a disaster go away faster.
這就是為什麼 我要做「救災機器人學」, 因為機器人能讓災難更快消失。
Now, you've already seen a couple of these. These are the UAVs. These are two types of UAVs: a rotorcraft, or hummingbird; a fixed-wing, a hawk. And they're used extensively since 2005 -- Hurricane Katrina. Let me show you how this hummingbird, this rotorcraft, works. Fantastic for structural engineers. Being able to see damage from angles you can't get from binoculars on the ground or from a satellite image, or anything flying at a higher angle. But it's not just structural engineers and insurance people who need this. You've got things like this fixed-wing, this hawk. Now, this hawk can be used for geospatial surveys. That's where you're pulling imagery together and getting 3D reconstruction.
現在你們已經看到其中幾種了。 這些是無人機。 這裡有兩種無人機: 一種叫旋翼機,又稱蜂鳥; 一種是定翼機,又叫隼。 這兩種自 2005 年 颶風卡崔娜以後 已被廣泛使用。 我跟大家展示一下 這種蜂鳥旋翼機如何運作。 這是結構工程師的夢啊! 這些能從不同角度看受損狀況, 是你無法從地面用望遠鏡 或從衛星圖, 或從任何高一點的飛行角度看到。 但不只是結構工程師 及保險公司有這樣的需求。 你還能從這種定翼機, 這個隼看到東西。 這個隼能拿來做地理空間調查。 你能把成像組合起來 得到立體影像重建。
We used both of these at the Oso mudslides up in Washington State, because the big problem was geospatial and hydrological understanding of the disaster -- not the search and rescue. The search and rescue teams had it under control and knew what they were doing. The bigger problem was that river and mudslide might wipe them out and flood the responders. And not only was it challenging to the responders and property damage, it's also putting at risk the future of salmon fishing along that part of Washington State. So they needed to understand what was going on. In seven hours, going from Arlington, driving from the Incident Command Post to the site, flying the UAVs, processing the data, driving back to Arlington command post -- seven hours. We gave them in seven hours data that they could take only two to three days to get any other way -- and at higher resolution. It's a game changer.
這兩種機器人都曾用於 華盛頓州的奧所山崩上, 因為很大的問題出在 瞭解這場災難的 地理空間及水文狀況, 而不是搜救。 搜救隊伍已控制情況, 也很清楚知道他們要做什麼。 但更大的問題是河水及山崩 可能會毀了他們, 並淹沒救災人員。 這不只對救災人員造成挑戰, 並造成財物損失, 這還對將來在華盛頓州 那一帶的釣鮭魚活動造成威脅。 所以他們需要知道情況。 在七個小時內,從阿靈頓出發, 從事故指揮所開車到現場、 飛無人機、 處理數據、 開車回阿靈頓的指揮所, 只花了七個小時。 我們在七個小時內 就給他們數據, 用其他方法要花兩三天—— 而且是更高的解析度。 這改變了局勢。
And don't just think about the UAVs. I mean, they are sexy -- but remember, 80 percent of the world's population lives by water, and that means our critical infrastructure is underwater -- the parts that we can't get to, like the bridges and things like that. And that's why we have unmanned marine vehicles, one type of which you've already met, which is SARbot, a square dolphin. It goes underwater and uses sonar. Well, why are marine vehicles so important and why are they very, very important? They get overlooked. Think about the Japanese tsunami -- 400 miles of coastland totally devastated, twice the amount of coastland devastated by Hurricane Katrina in the United States. You're talking about your bridges, your pipelines, your ports -- wiped out. And if you don't have a port, you don't have a way to get in enough relief supplies to support a population. That was a huge problem at the Haiti earthquake. So we need marine vehicles.
而且不要只想到無人機。 我是說,它們是很迷人, 但你要記住, 80% 的世界人口住在水邊, 意指我們關鍵的 基礎建設都在水下, 我們無法進入的地方, 像橋梁或是類似的東西。 這就是為什麼我們有 無人駕駛的海陸兩棲車, 你們已經看到的其中一種, 沙霸,方型海豚。 它可以進入水下,使用聲納。 為什麼兩棲車這麼重要? 為什麼它們真的非常重要? 它們都被忽視了。 想想日本海嘯。 650 公里的沿海地區被徹底摧毀, 比美國的颶風卡崔娜 所破壞的沿岸區還大兩倍。 你在談的是你的橋梁、 你的管線、你的港口——全沒了。 如果你沒有港口, 你就沒有辦法 運進足夠的救災物資 以支援災民。 這在海地地震 就造成很大的問題。 所以我們需要兩棲車輛。
Now, let's look at a viewpoint from the SARbot of what they were seeing. We were working on a fishing port. We were able to reopen that fishing port, using her sonar, in four hours. That fishing port was told it was going to be six months before they could get a manual team of divers in, and it was going to take the divers two weeks. They were going to miss the fall fishing season, which was the major economy for that part, which is kind of like their Cape Cod. UMVs, very important.
現在我們從沙霸的角度 看他們看到的東西。 我們在搶救一座漁港。 我們能用沙霸的聲納系統 在四小時內重新開放那座漁港。 那座漁港被告知要六個月 才能找到一組潛水員下去看, 而且潛水員還要花兩個星期。 他們會因此錯過秋季魚汛, 那塊區域主要的經濟來源, 有點像麻省的勝地「鱈魚角」。 自動兩棲車非常重要。
But you know, all the robots I've shown you have been small, and that's because robots don't do things that people do. They go places people can't go. And a great example of that is Bujold. Unmanned ground vehicles are particularly small, so Bujold --
但是你知道嗎, 我展示給大家看的機器人都很小, 那是因為機器人不做人做的事。 他們去人到不了的地方。 有個很好的例子就是「步足」。 無人駕駛的地面車輛都特別小, 所以步足
(Laughter)
(笑聲)
Say hello to Bujold.
跟步足打個招呼吧!
(Laughter)
(笑聲)
Bujold was used extensively at the World Trade Center to go through Towers 1, 2 and 4. You're climbing into the rubble, rappelling down, going deep in spaces. And just to see the World Trade Center from Bujold's viewpoint, look at this. You're talking about a disaster where you can't fit a person or a dog -- and it's on fire. The only hope of getting to a survivor way in the basement, you have to go through things that are on fire. It was so hot, on one of the robots, the tracks began to melt and come off. Robots don't replace people or dogs, or hummingbirds or hawks or dolphins. They do things new. They assist the responders, the experts, in new and innovative ways.
步足在紐約世貿恐襲中 被大量使用, 搜索 1、 2 及 4 號大樓。 你爬進廢墟、繞繩下降, 進入位於深處的空間。 從步足的眼光看紐約世貿, 看一下這個。 你在談的是你不能用 人或狗來處理的災難, 而且還在燃燒。 能到地下室找生存者的唯一希望, 你得通過燃燒的火場。 現場非常熱,某個機器人的履帶 都開始熔化脫落。 機器人不能取代人或狗, 或蜂鳥或隼或海豚等無人機。 他們做新的事。 他們以創新的方法 幫助救難人員及專家。
The biggest problem is not making the robots smaller, though. It's not making them more heat-resistant. It's not making more sensors. The biggest problem is the data, the informatics, because these people need to get the right data at the right time.
但是最大的問題 不是把機器人做得更小。 也不是把他們弄得更耐熱。 也不是加更多的感應器。 最大的問題是數據, 是資訊學, 因為這些人需要 在適當的時間取得正確的資料。
So wouldn't it be great if we could have experts immediately access the robots without having to waste any time of driving to the site, so whoever's there, use their robots over the Internet. Well, let's think about that. Let's think about a chemical train derailment in a rural county. What are the odds that the experts, your chemical engineer, your railroad transportation engineers, have been trained on whatever UAV that particular county happens to have? Probably, like, none. So we're using these kinds of interfaces to allow people to use the robots without knowing what robot they're using, or even if they're using a robot or not. What the robots give you, what they give the experts, is data.
如果專家能立刻從機器人 取得數據不是很棒嗎? 不用浪費時間開車到現場, 所以無論是誰在那, 都可以用網路操縱機器人。 好好想一下。 想一下載了化學品的火車 在郊區縣城脫軌。 你想這機率有多高, 你的專家、化學工程師、 你的鐵路運輸工程師, 剛好就在那個縣城, 還受過無人機訓練? 大概是零吧? 所以我們用這種介面 讓大家使用機器人, 無須知道他們在用哪種機器人, 或根本不用知道 他們有沒有在用機器人。 機器人給你的、 給專家的是數據。
The problem becomes: who gets what data when? One thing to do is to ship all the information to everybody and let them sort it out. Well, the problem with that is it overwhelms the networks, and worse yet, it overwhelms the cognitive abilities of each of the people trying to get that one nugget of information they need to make the decision that's going to make the difference. So we need to think about those kinds of challenges. So it's the data.
問題變成: 誰在什麼時候拿到什麼數據? 有一個方法是把所有的數據 送給每一個人, 讓他們自己選。 這個方法的問題是 這樣會讓網路超載, 最糟的是,這還會讓 試著得到那塊數據的人 認知能力不勝負荷。 他們做的決定會改變一切。 所以我們必須考慮那種挑戰。 所以數據才是大問題。
Going back to the World Trade Center, we tried to solve that problem by just recording the data from Bujold only when she was deep in the rubble, because that's what the USAR team said they wanted. What we didn't know at the time was that the civil engineers would have loved, needed the data as we recorded the box beams, the serial numbers, the locations, as we went into the rubble. We lost valuable data. So the challenge is getting all the data and getting it to the right people.
再回頭來看世界貿易中心, 我們試著解決這個問題, 所以讓步足在深入廢墟後 才記錄下數據, 因為那是坍塌搜救專隊 說他們要的。 那時候我們不知道 土木工程師會很愛、 很需要我們在進入廢墟時 錄下來的箱型梁序號、地點。 我們錯失了珍貴的數據。 所以挑戰是得到所有的數據, 及把數據送到對的人手上。
Now, here's another reason. We've learned that some buildings -- things like schools, hospitals, city halls -- get inspected four times by different agencies throughout the response phases. Now, we're looking, if we can get the data from the robots to share, not only can we do things like compress that sequence of phases to shorten the response time, but now we can begin to do the response in parallel. Everybody can see the data. We can shorten it that way.
現在還有另一個原因。 我們得知某些建築物, 像是學校、醫院、市政廳等, 在整個救災反應期, 要被不同的單位檢查四次, 現在我們來看,如果我們能從機器人 得到數據給大家共用, 我們不但能縮短反應期各個階段 以縮短反應時間, 我們現在還能開始 同時進行不同反應。 每個人都能看到數據。 我們可以用那種方法縮短。
So really, "disaster robotics" is a misnomer. It's not about the robots. It's about the data.
所以說真的, 「救災機器人」學是個誤稱。 這跟機器人無關。 這跟數據有關。
(Applause)
(掌聲)
So my challenge to you: the next time you hear about a disaster, look for the robots. They may be underground, they may be underwater, they may be in the sky, but they should be there. Look for the robots, because robots are coming to the rescue.
所以我給大家的挑戰是, 下一次你聽到某個災難, 去找機器人。 他們可能在地下, 可能在水下, 也可能在天上, 但是它們應該就在那兒。 去找機器人, 因為機器人要來拯救大家了!
(Applause)
(掌聲)