VijayKumar_2012关于四轴飞行器的演讲稿(中英文对照).doc

早上好我今天想谈谈自主飞行沙滩球其实，是小型飞行器，像这一个我想和大家谈谈设计这些飞行器时的挑战和使用这些飞行器能给我们带来的很多用处这些飞行器源于无人驾驶的飞行器但是那些都体积很大通常上万磅重毫无灵活型可言它们也不是真的自主飞行的事实上，很多这些飞行器都是受飞行团队控制的包括好几个飞行员感应雷达操作员和团队协调员我们想设计的飞行器是这样的这里有两张照片是你能够在超市里买到的那种小飞行器小型直升机，四个螺旋桨不超过一米长只不过几磅重我们把它们稍微改造一下，加上感应器和处理器，它们就可以在室内飞用不着导航系统我现在拿着的这个飞行器是其中之一是两个学生做出来的艾利克斯和丹尼尔这个仅仅比零点一磅稍微重一点只需要大约十五瓦的电源你能看到它的直径大约只有八个英寸让我给你们快速解释一下这些飞行器是怎么工作的它有四个螺旋桨当四个螺旋桨转速相同这个飞行器就浮在空中当所有螺旋桨的速度提升时这个飞行器就加速升高当然了，如果飞行器已经是倾斜的向着地平线侧过来就会向这个方向加速怎么能让它侧过来呢，有两个途径从这张照片你能看到四号螺旋桨旋转加速同时二号螺旋桨转速变慢这时飞行器就能向一边倒反之亦然当三号螺旋桨加速一号减速时飞行器就向前倒最后如果任意两端的螺旋桨的转速大于另两端的螺旋桨的转速飞行器就能原地旋转所以装在飞行器上的处理器基本上能判断需要执行哪些动作然后把它们组合起来决定给螺旋桨下什么指令一秒钟六百次简单地说这些飞行器就是这么工作的这个设计的一个好处就是小巧这些飞行器很灵活这里的R是飞行器的长度其实是半径当半径变小时很多物理参数都会变最重要的一个参数是惯性， 也就是对于运动的阻力结果是惯性决定角速度它是半径的五次方函数当半径变得越来越小时惯性越来越快地减小另一个结果是角速度的加速度也就是这里的希腊字母alpha等于一除以半径也就是半径的倒数当半径越小时飞行器能转弯越快这个视频清楚地显示大家看右下角的飞行器正在做一个三百六十度翻转只需要不到半秒连续翻转，稍微时间长一点这里飞行器上用的处理器能够从飞行器上的加速度计和陀螺仪得到反馈信息然后算出，就像我刚才讲的一秒钟六百个指令来稳定控制这个飞行器在左边你能看到丹尼尔把飞行器抛到空中你能看到飞行器的控制有多快不管你怎么扔飞行器都能恢复平衡飞回来为什么我们要设计这种飞行器呢？因为这样的飞行器有很多用处你能把它们放进像这样的大楼里作为报警器去寻找入侵者寻找生化泄漏或者煤气泄漏你还能用它们建摩天大楼呢这里是飞行器在搬梁运柱架构一个立方体的建筑这里我想和大家介绍一下这些机器人能被用来运货当然一个问题是这些小飞行器担不了多少重量你可能需要很多飞行器来搬运重物我们新做了个实验其实不那么新了在日本仙台，地震后不久我们能把这些飞行器送进倒塌的楼房或者核反应堆大楼来探测放射性强度一个根本的问题是当这些飞行器需要自控飞行，它们自己得弄明白怎么从一个地点到另一个地点这就变得有点难度了因为这些飞行器的动力学是很复杂的事实上它们总在对付十二维的空间这里我们用了一点小技巧我们拿这个十二位的空间把它们转换成平的四维空间这个四维空间包括了横轴，纵轴和竖轴，还有旋转轴这些飞行器只需要计划一件事，我们管它叫最小化加加加速度轨道提醒大家一点点物理学这里我们有位置向量，导数，速度和加速度还有加加速度还有加加加速度这个飞行器把加加加速度最小化基本上它的工作是创造一个光滑优雅的运动曲线这样来绕开障碍物所以这个四维平面中，这个飞行器使用最小化加加加速度轨道， 然后转换回到复杂的十二维空间飞行器必须这样做来获得控制和执行动作让我给大家看几个例子这些最小化加加加速度轨道是什么样的这是第一个视频这个飞行器从一个地点飞到另一个地点中间经停一下显然这个飞行器能飞出一个曲线轨道还有这样的打圈的轨道这里飞行器对抗两倍的重力它们上方还有一个动感监控摄像机，每秒一百幅画面来告诉这些飞行器它们的位置也能告诉这些飞行器障碍物在哪里障碍物移动都不要紧当丹尼尔把套圈扔到空中飞行器就开始计算套圈的位置试图预测怎么才能最有效地钻过去作为一个科研人员我们总在试图钻出重重圈套，拿到更多经费甚至训练了我们的飞行器也来做这个（掌声）另一个飞行器能做的事情是当我们预先编入一些轨迹或者它自己学着走过的，它能够记住这里大家能看到飞行器能够（在预设轨迹上）加上一个动作积聚动量改变它的定向，再回到预设轨迹上来它必须这样做因为这个窗上的缝隙只比它的宽度大一点点所以就像是一个跳水运动员从跳板上起跳，聚集动量，做个旋转，两圈半然后优雅地回到平衡这个飞行器是自主这样做的它知道怎么把小段的轨迹组合起来来做这些高难度的技巧现在我想换个话题谈谈这些小型飞行器的不足之处，就是体积小我已经提过我们需要使用很多飞行器来克服体积小的不便一个难点是怎么使得这些飞行器集体飞行？我们在大自然中寻找答案我想给大家看一个视频是关于Aphaenogaster沙漠蚁的在史狄文普热特教授的实验室里，这些蚂蚁一起搬运重物这是一个无花果事实上无论什么东西，只要蘸上无花果汁这些蚂蚁都会把它们带回巢去这些蚂蚁没有任何中央调控它们是靠感应邻近的蚂蚁它们也没有明确的交流但是因为它们能够感应邻近的蚂蚁也能感应抬着的重物整群的蚂蚁有默契这样的协调正是飞行器需要的当一个飞行器被其他飞行器环绕时让我们注意 I 和 J 这两个当它们成群飞行时我们希望这两个飞行器能够监控它们之间的距离我们需要确定这个距离是在可接受的范围里的飞行器要检测这个变化在控制指令中计算进去也是每秒一百次这个控制指令每秒会被送到马达六百次所以这个程序是分散化执行的再有，如果你有很多很多飞行器要完成集体飞行任务，能足够快地集中协调所有这些信息是几乎不可能的加上这些飞行器只能依靠局部的信息来决定做什么动作也就是要靠感应邻近的飞行器最后我们希望这些机器人不知道它们的邻居是谁也就是匿名飞行下一个我想给大家展示的是这段视频这二十个小型飞行器成群飞行它们在监测邻居的位置维持群队群队的形状还能变它们可以在一个平面上飞也可以上中下地飞大家可以看到它们能从上中下的群队变成平面的在飞越障碍物的时候它们能边飞边变换队形我想强调，这些飞行器距离都很近比如这个群队，八架飞行器相互距离不过几英寸尽管在空气动力学上这些螺旋桨相互干扰它们还是能够维持平稳飞行（掌声）现在它们会成群飞了它们就可以合作抬重物这里展示的是我们能够把飞行器的能力翻倍，翻三倍，四倍仅仅通过让它们和邻居合作，大家可以看到这样做的一个不便之处就是当加大数量时比如使用很多飞行器来抬一个物体你其实是加大了惯性这样它们就不够灵活了，这是一个代价但是你可以增加载荷承载量另一个我想给大家展示的用处是这是在我们实验室这是研究生昆汀林夕的工作他的算法程序告诉这些飞行器怎么使用桁架结构自动建造一个立方体他的算法程序告诉这些机器人该用哪一块什么时候用，用在哪里从这个视频我们可以看到这个视频是十倍或者十四倍速度播放的大家可以看到飞行器在搭建很不一样的构架并且，所有的运动都是自主的昆汀仅仅是给它们一个蓝图也就是他想建的设计所有这里展示的实验所有这些演习都是靠着它们自己的动感检测摄像机完成的那么，当它们离开实验室来到真实世界的时候，又怎么样呢？没有卫星导航会怎么样？这个飞行器其实装有一个摄像机和一个激光测距仪，一个激光扫描仪它可以使用这些探测装置来描绘周围的环境的地图这个地图包括很多细节玄关，窗户人，家具还能弄清楚相对于这些东西它自己在哪里所以这里没有整体的协调系统这个协调系统是靠飞行器自己来完成的它自己在哪里，前面有什么还能利用周围环境为自己找到出路这里我想给大家再看一段视频这个算法程序是法兰克沈和南希麦克教授编的当这个飞行器第一次飞入一个建筑它是怎么边飞边画地图的这个飞行器弄明白了这些细节开始画地图弄明白了相对这些细节，自己在哪里，然后自我定位全以每秒一百次的速度发生这就给我们一个机会来控制这些算法像我之前讲过的所以这个机器人其实是被法兰克遥控的但是它自己也可以弄明白怎么飞假设我想放一个这样的飞行器进一幢楼我并不知道里面是什么样的我可以让它飞进去创造一个地图然后飞回来告诉我里面是什么样的所以，这个飞行器不仅仅解决了怎么从一点到另一点的问题还能够随时知道最好的目标在哪里基本上，它知道该去搜索哪里因为那里的信息是最“未知”的这就是它怎么填充这个地图这里我想展示给大家最后一个用途当然这个技术有很多很多用途我是个教授，我们很关心教育这样的飞行器其实可以改变我们的小学和中学教育我们在南加州离洛杉矶很近所以我不得不放点娱乐元素进去我想给大家看一个音乐视频我想向你们介绍艾利克斯和丹尼尔，他们是导演兼制作（掌声）在我播放这个视频前我想告诉大家这是他们在过去三天做出来的因为主持人克瑞斯给我打了个电话在这个视频中表演的飞行器全是靠自控表演的你能看到九个机器人，演奏六种不同乐器当然了，这是为了今年的TED2012特别制作的请欣赏（音乐）（掌声）Good morning.Im here today to talkabout autonomous, flying beach balls.No, agile aerial robots like this one.Id like to tell you a little bit about the challenges in building theseand some of the terrific opportunitiesfor applying this technology.So these robotsare related to unmanned aerial vehicles.However, the vehicles you see here are big.They weigh thousands of pounds,are not by any means agile.Theyre not even autonomous.In fact, many of these vehiclesare operated by flight crewsthat can include multiple pilots,operators of sensorsand mission coordinators.What were interested in is developing robots like this -and here are two other pictures -of robots that you can buy off the shelf.So these are helicopters with four rotorsand theyre roughly a meter or so in scaleand weigh several pounds.And so we retrofit these with sensors and processors,and these robots can fly indoorswithout GPS.The robot Im holding in my handis this one,and its been created by two students,Alex and Daniel.So this weighs a little morethan a tenth of a pound.It consumes about 15 watts of power.And as you can see,its about eight inches in diameter.So let me give you just a very quick tutorialon how these robots work.So it has four rotors.If you spin these rotors at the same speed,the robot hovers.If you increase the speed of each of these rotors,then the robot flies up, it accelerates up.Of course, if the robot were tilted,inclined to the horizontal,then it would accelerate in this direction.So to get it to tilt, theres one of two ways of doing it.So in this pictureyou see that rotor four is spinning fasterand rotor two is spinning slower.And when that happenstheres moment that causes this robot to roll.And the other way around,if you increase the speed of rotor threeand decrease the speed of rotor one,then the robot pitches forward.And then finally,if you spin opposite pairs of rotorsfaster than the other pair,then the robot yaws about the vertical axis.So an on-board processoressentially looks at what motions need to be executedand combines these motionsand figures out what commands to send to the motors600 times a second.Thats basically how this thing operates.So one of the advantages of this designis, when you scale things down,the robot naturally becomes agile.So here Ris the characteristic length of the robot.Its actually half the diameter.And there are lots of physical parameters that changeas you reduce R.The one thats the most importantis the inertia or the resistance to motion.So it turns out,the inertia, which governs angular motion,scales as a fifth power of R.So the smaller you make R,the more dramatically the inertia reduces.So as a result, the angular acceleration,denoted by Greek letter alpha here,goes as one over R.Its inversely proportional to R.The smaller you make it the more quickly you can turn.So this should be clear in these videos.At the bottom right you see a robotperforming a 360 degree flipin less than half a second.Multiple flips, a little more time.So here the processes on boardare getting feedback from accelerometersand gyros on boardand calculating, like I said before,commands at 600 times a secondto stabilize this robot.So on the left, you see Daniel throwing this robot up into the air.And it shows you how robust the control is.No matter how you throw it,the robot recovers and comes back to him.So why build robots like this?Well robots like this have many applications.You can send them inside buildings like thisas first responders to look for intruders,maybe look for biochemical leaks,gaseous leaks.You can also use themfor applications like construction.So here are robots carrying beams, columnsand assembling cube-like structures.Ill tell you a little bit more about this.The robots can be used for transporting cargo.So one of the problems with these small robotsis their payload carrying capacity.So you might want to have multiple robotscarry payloads.This is a picture of a recent experiment we did -actually not so recent anymore -in Sendai shortly after the earthquake.So robots like this could be sent into collapsed buildingsto assess the damage after natural disasters,or sent into reactor buildingsto map radiation levels.So one fundamental problemthat the robots have to solve if theyre to be autonomousis essentially figuring outhow to get from point A to point B.So this gets a little challengingbecause the dynamics of this robot are quite complicated.In fact, they live in a 12-dimensional space.So we use a little trick.We take this curved 12-dimensional spaceand transform itinto a flat four-dimensional space.And that four-dimensional spaceconsists of X, Y, Z and then the yaw angle.And so what the robot doesis it plans what we call a minimum snap trajectory.So to remind you of physics,you have position, derivative, velocity,then acceleration,and then comes jerkand then comes snap.So this robot minimizes snap.So what that effectively doesis produces a smooth and graceful motion.And it does that avoiding obstacles.So these minimum snap trajectories in this flat spaceare then transformed backinto this complicated 12-dimensional space,which the robot must dofor control and then execution.So let me show you some examplesof what these minimum snap trajectories look like.And in the first video,youll see the robot going from point A to point Bthrough an intermediate point.So the robot is obviously capableof executing any curve trajectory.So these are circular trajectorieswhere the robot pulls about two Gs.Here you have overhead motion capture cameras on the topthat tell the robot where it is 100 times a second.It also tells the robot where these obstacles are.And the obstacles can be moving.And here youll see Daniel throw this hoop into the air,while the robot is calculating the position of the hoopand trying to figure out how to best go through the hoop.So as an academic,were always trained to be able to jump through hoops to raise funding for our labs,and we get our robots to do that.(Applause)So another thing the robot can dois it remembers pieces of trajectorythat it learns or is pre-programmed.So here you see the robotcombining a motionthat builds up momentumand then changes its orientation and then recovers.So it has to do this because this gap in the windowis only slightly larger than the width of the robot.So just like a diver stands on a springboardand then jumps off it to gain momentum,and then does this pirouette, this two and a half somersault throughand then gracefully recovers,this robot is basically doing that.So it knows how to combine little bits and pieces of trajectoriesto do these fairly difficult tasks.So I want change gears.So one of the disadvantages of these small robots is its size.And I told you earlierthat we may want to employ lots and lots of robotsto overcome the limitations of size.So one difficultyis how do you coordinate lots of these robots?And so here we looked to nature.So I want to show you a clipof Aphaenogaster desert antsin Professor Stephen Pratts lab carrying an object.So this is actually a piece of fig.Actually you take any object coated with fig juiceand the ants will carry them back to the nest.So these ants dont have any central coordinator.They sense their neighbors.Theres no explicit communication.But because they sense the neighborsand because they sense the object,they have implicit coordination across the group.So this is the kind of coordinationwe want our robots to have.So when we have a robotwhich is surrounded by neighbors -and lets look at robot I and robot J -what we want the robots to dois to monitor the separation between themas they fly in formation.And then you want to make surethat this separation is within acceptable levels.So again the robots monitor this errorand calculate the control commands100 times a second,which then translates to the motor commands 600 times a second.So this also has to be donein a decentralized way.Again, if you have lots and lots of robots,its impossible to coordinate all this information centrallyfast enough in order for the robots to accomplish the task.Plus the robots have to base their actionsonly on local information,what they sense from their neighbors.And then finally,we insist that the robots be agnosticto who their neighbors are.So this is what we call anonymity.So what I want to show you nextis a videoof 20 of these little robotsflying in formation.Theyre monitoring their neighbors position.Theyre maintaining formation.The formations can change.They can be planar formations,they can be three-dimensional formations.As you can see here,they collapse from a three-dimensional formation into planar formation.And to fly through obstaclesthey can adapt the formations on the fly.So again, these robots come really close together.As you can see in this figure-eight flight,they come within inches of each other.And despite the aerodynamic interactionsof these propeller blades,theyre able to maintain stable flight.(Applause)So once you know how to fly in formation,you can actually pick up objects cooperatively.So this just showsthat we can double, triple, quadruplethe robot strengthby just getting them to team with neighbors, as you can see here.One of the disadvantages of doing thatis, as you scale things up -so if you have lots of robots carrying the same thing,youre essentially effectively increasing the inertia,and therefore you pay a price; theyre not as agile.But you do gain in terms of payload carrying capacity.Another application I want to show you -again, this is in our lab.This is work done by Quentin Lindsey whos a graduate student.So his algorithm essentially tells these robotshow to autonomously buildcubic structuresfrom truss-like elements.So his algorithm tells the robotwhat part to pick up,when and where to place it.So in this video you see -and its sped up 10, 14 times -you see three different structures being built by these robots.And again, everything is autonomous,and all Quentin has to dois to get them a blueprintof the design that he wants to build.So all these experiments youve seen thus far,all these demonstrations,have been done with the help of motion capture systems.So what happens when you leave your laband you go outside into the real world?And what if theres no GPS?So this robotis actually equipped with a cameraand a laser rangefinder, laser scanner.And it uses these sensorsto build a map of the environment.What that map consists of are features -like doorways, windows,people, furniture -and it then figures out where its position iswith respect to the features.So there is no global coordinate system.The coordinate system is defined based on the robot,where it is and what its looking at.And it navigates with respect to those features.So I want to show you a clipof algorithms developed by Frank Shenand Professor Nathan Michaelthat shows this robot entering a building for the very first timeand creating this map