On Friday night in an old newspaper printing plant in Austin, the future of 无人驾驶飞机 automation lifted off, accelerated and flew, nearly fast enough to beat one of the best 无人驾驶飞机 pilots in the world.

加布里埃尔科赫尔,在职业联赛的gab707无人驾驶赛车已知的,坐在后面的净,戴着眼镜的视频,并通过门上的短,弯曲的课程指导他十五平方无人机。被他旁边的四位队友从技术在荷兰代尔夫特理工大学的mavlab。他们已经编程他们的自动化无人机,它类似一个小型的隐形轰炸机。他们现在看,看看他们的代码做了无人机快速,准确足以打败科赫尔。

Courtesy of DRL

这是人类对机,至少现在,人类取得了胜利。他科赫尔通过该课程无人机在6秒内引导。它把它mavlab11秒。尽管输给科赫尔,mavlab - 费德里科的四个成员Valles的墙壁,轻哼的圭多,Wagter的克里斯托夫和Nilay谢斯五个其他球队胜过认定为合格的AIRR(Artificial Intelligence Robotic Racing circuit) Championship and pocketed a $1 million prize from sponsor Lockheed Martin.

Afterward, Kocher was relieved to have won but conceded man’s superiority over the machine will not last forever. It’s a matter of time, experts say, before automated 无人驾驶飞机 will whir past humans in increasingly popular competitions hosted by the Drone Racing League—and before they can use similar technology to handle complicated tasks in real life.

The latter possibility led Lockheed Martin to sponsor the AIRR circuit for coders, professors, students, physicists和 pilots to power automated 无人驾驶飞机s. Remotely piloted Lockheed Martin 无人驾驶飞机 导弹下落,帮助警察和消防队员,并协助救援任务失踪人员。自动化无人机挑战的经理洛克希德·马丁公司的基思·林恩说,自动化无人机将更好地处理复杂的救援任务所在的地区运输线路和通信线路细分都有。

自动化技术,无人驾驶飞机在最近几年因为更好的图形处理单元和开放源代码共享文化的显着改善。尽管如此,在自动化无人机最大的发展已有局限于实验室。编程的赛车系列自动无人驾驶飞机,以接近70英里每小时飞行速度,是普通公众不同于你见过。\

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“事情已经有那么快在实验室走的并不快,在现实世界中,”切尔西SABO,比赛和洛克希德 - 马丁公司的软件工程师的技术领先如是说。 “这是我们第一次真正采取了很多这样的走出实验室,并把它放在一个逼真的环境,看到它可以做什么。”

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Lockheed Martin also wants to get the average person more excited about 无人驾驶飞机. Americans have a NIMBY-like relationship with the flying devices. According to Pew, the most common reaction for an American who spots a 无人驾驶飞机 好奇心是, ranking ahead of nervousness, anger or fear. But a majority of Americans surveyed by Pew said they didn’t want the flying devices near their homes, which isn’t welcome news to companies that envision a future with thousands of 无人驾驶飞机 patrolling the skies and making deliveries.

无人机赛车联盟无人机更聪明,更时尚,比任何机器一般人能买快..缺口Horbaczewski在2015年开始参加联赛后面的长岛Home Depot的一个业余的无人机比赛后。在联赛的比赛中,专业飞行员驾驶的无人驾驶飞机,LED灯发光,看起来像在环境带来的生活视频游戏。 Horbaczewski一直设想的DRL的未来功能与人工飞行员竞争对手的机器。 “有很多人在自主无人机世界的工作,”我说。 “我们选择做什么是...拍摄运动的舞台上,并把它作为一个坩埚,加快技术的开发。”

Courtesy of DRL

对于AIRR电路,九队被选为近430今年早些时候申请和资格上周五晚上在奥斯汀的总决赛六支队伍;这是他们的第四个比赛。 racerais无人机被调用。他们重约七磅,没有雷达或GPS;他们是一对机器人本质的眼睛,配有四台摄像机,让他们的观点比人更大的领域。这些团队都使用相同的硬件,所以竞争,五次预赛其由,是战略和代码的测试。无人机必须编程去感知他们在哪里和他们需要去的。该代码是在九月的比赛之前,和无人驾驶飞机的基础上,他们已被编程来运行代码比赛做出决定。大多数球队开始用设计来推动通过该课程的无人驾驶飞机在保守的速度慢,在后来的预赛采用更积极的代码的代码。

In the first contest, in October, the automated 无人驾驶飞机 barely made it off the starting perches before crashing. “A lot of the flights were four seconds,” says MAVLab’s De Wagter, “and you had to learn from that.”

加布里埃尔科赫尔

Courtesy of DRL

速度不是一个自动化无人机的最大问题。科赫尔说,无人驾驶飞机的自动基本达到相同的最高时速为他的无人机周五晚上,尽管许多然后坠毁。与驾驶的无人驾驶飞机相比,一个自动化的无人驾驶飞机具有基于反应时间快速加速更多的潜力。主要的挑战是编程无人驾驶飞机穿过或绕过它来识别障碍物和转向。和无人驾驶飞机去的速度越快,就越难是程序员,使允许无人驾驶飞机,以做出正确的决定的调整。

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“You basically need to anticipate,” says Kocher, who has a PhD in physics. “I have a mental representation in how I can ride this fine line. But for them they need to write the physics model that knows already what’s going to happen and know in advance how it’s going to work out.”

Subtle changes to the visual or auditory environment that mean nothing to a human 可以迷惑 无人驾驶飞机 and other devices dependent on machine learning. On the Austin course, for instance, the gates featured logos of checkered flags that required MAVLab to reconfigure its code. “The human is not distracted by the flags,” De Croon says.

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The MAVLab teammates have been working with autonomous 无人驾驶飞机s for about a decade and racing 无人驾驶飞机 for the last three years. Most recently, they developed what is being hailed as the world’s smallest autonomous racing 无人驾驶飞机. Building on that experience, the team found the RacerAI 无人驾驶飞机 worked most efficiently using only one of its four cameras.\

“It’s better to have lower quality, high frequency (processing) than good, slow solutions, especially when you start racing and everything gets to be a blur and the lighting conditions vary from one setting to the other,” De Wagter says.

MAVLab and Kocher agree that on simpler courses like those in Austin the automated 无人驾驶飞机 may prevail within the next few years. For the complicated courses used regularly by DRL, it will take longer for automation to catch up.

In other man versus machine contests, like , Kocher says, the machines can “brute force” the human out of the contest by calculating far more moves ahead than a person. The unpredictability of 无人驾驶飞机 racing will make it more difficult for the machine.

“Drone racing,” Kocher says, “is a game where they cannot brute force me.”


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