一座岛屿断电了。为了找到水下电缆, 的断裂处，一艘船拉起整条电缆或部署遥控潜水器(ROV) 来穿越该电缆。但是，如果自动水下航行器 (AUV) 能够绘制线路图并查明故障位置，以便潜水员修复?，结果会怎样呢？
The electricity to an island goes out. To find the break in the underwater power cable, a ship pulls up the entire line or deploys remotely operated vehicles (ROVs) to traverse the line. But what if an autonomous underwater vehicle (AUV) could map the line and pinpoint the location of the fault for a diver to fix?
"潜水员和 AUV 通常不会'在水下组队," 首席研究员马德琳·米勒 (Madeline Miller) 说。 "需要人类的水下任务通常会这样做，因为它们涉及机器人无法完成的某种操作',，例如修复基础设施或停用水雷。即使是 ROV 在水下执行非常熟练的操纵任务也具有挑战性，因为操纵器本身'不够敏捷。"
"Divers and AUVs generally don't team at all underwater," says principal investigator Madeline Miller. "Underwater missions requiring humans typically do so because they involve some sort of manipulation a robot can't do, like repairing infrastructure or deactivating a mine. Even ROVs are challenging to work with underwater in very skilled manipulation tasks because the manipulators themselves aren't agile enough."
除了出众的灵活性之外，, 人类还擅长识别水下物体。但在水下工作的人类无法'执行复杂的计算或快速移动,，特别是如果他们携带重型设备;机器人在处理能力,高速移动,和耐力方面比人类有优势。为了结合这些优势, Miller 和她的团队正在开发用于水下导航和感知的硬件和算法— 有效人机协作的两项关键功能。
Beyond their superior dexterity, humans excel at recognizing objects underwater. But humans working underwater can't perform complex computations or move very quickly, especially if they are carrying heavy equipment; robots have an edge over humans in processing power, high-speed mobility, and endurance. To combine these strengths, Miller and her team are developing hardware and algorithms for underwater navigation and perception — two key capabilities for effective human-robot teaming.
正如 Miller 所解释的，, 潜水员可能只有指南针和鳍踢计数来引导他们。由于地标很少，并且由于深度缺乏光线或水柱中存在生物物质,而可能存在黑暗条件，因此它们很容易迷失方向和迷路。对于帮助潜水员导航, 的机器人来说，他们需要感知周围的环境。然而，, 在黑暗和浑浊的情况下, 光学传感器 (cameras) 无法生成图像,，而声学传感器 (sonar) 生成的图像缺乏颜色，仅显示场景中物体的形状和阴影。历史上缺乏大型,标记声纳图像数据集阻碍了水下感知算法的训练。即使数据可用,，动态海洋也会掩盖物体的真实性质, 混淆人工智能。例如, 一架被击落的飞机破碎成多个碎片, 或被过度生长的贻贝, 覆盖的轮胎可能不再分别像飞机或轮胎,。
As Miller explains, divers may only have a compass and fin-kick counts to guide them. With few landmarks and potentially murky conditions caused by a lack of light at depth or the presence of biological matter in the water column, they can easily become disoriented and lost. For robots to help divers navigate, they need to perceive their environment. However, in the presence of darkness and turbidity, optical sensors (cameras) cannot generate images, while acoustic sensors (sonar) generate images that lack color and only show the shapes and shadows of objects in the scene. The historical lack of large, labeled sonar image datasets has hindered training of underwater perception algorithms. Even if data were available, the dynamic ocean can obscure the true nature of objects, confusing artificial intelligence. For instance, a downed aircraft broken into multiple pieces, or a tire covered in an overgrowth of mussels, may no longer resemble an aircraft or tire, respectively.
"最终,我们希望设计出远征环境中的导航和感知解决方案,"米勒说。 "对于任务，我们'正在考虑,，提前绘制区域图的机会有限或没有机会。例如，对于入港任务,，您可能有卫星地图，但没有水下地图,。"
"Ultimately, we want to devise solutions for navigation and perception in expeditionary environments," Miller says. "For the missions we're thinking about, there is limited or no opportunity to map out the area in advance. For the harbor entry mission, maybe you have a satellite map but no underwater map, for example."
"我们很快了解到，当您考虑洋流时，潜水员需要更多的传感能力," Miller 解释道。 "利用 MIT, 演示的算法，车辆只需定期计算到潜水员的距离, 或范围, 即可解决估计车辆和潜水员随时间变化的位置的优化问题。但随着真正的海洋力量推动, 周围的一切，这个优化问题很快就会爆发。"
"We quickly learned that you need more sensing capabilities on the diver when you factor in ocean currents," Miller explains. "With the algorithms demonstrated by MIT, the vehicle only needed to calculate the distance, or range, to the diver at regular intervals to solve the optimization problem of estimating the positions of both the vehicle and diver over time. But with the real ocean forces pushing everything around, this optimization problem blows up quickly."
在感知方面，, Miller 团队一直在开发一种人工智能分类器，可以在任务中处理光学和声纳数据，并为任何不确定的分类对象征求人工输入。
On the perception side, Miller的 team has been developing an AI classifier that can process both optical and sonar data mid-mission and solicit human input for any objects classified with uncertainty.
"这个想法是让分类器向潜水员传递一些信息—，比如,图像—周围的边界框，并指示,"我认为这是一个轮胎,，但我'm不确定。你觉得怎么样?" 那么, 潜水员可以回答, "是, 你'答对了, 或没有, 查看图像中的此处以提高你的分类," 米勒说。
"The idea is for the classifier to pass along some information — say, a bounding box around an image — to the diver and indicate, "I think this is a tire, but I'm not sure. What do you think?" Then, the diver can respond, "Yes, you've got it right, or no, look over here in the image to improve your classification," Miller says.
该反馈回路需要水声调制解调器来支持潜水员与 AUV 通信。水声通信中最先进的数据速率需要数十分钟才能将未压缩的图像从 AUV 发送给潜水员。因此, 团队正在研究的一个方面是如何将信息压缩成最少量的有用,，并在水下通信的低带宽和高延迟以及他们'正在使用的商业现成(COTS)硬件的小尺寸,重量,和功率的限制下工作。对于他们的原型系统,，该团队主要采购了 COTS 传感器，并构建了一个传感器有效载荷，可以轻松集成到美国海军, 常规使用的 AUV 中，以促进技术过渡。除了声纳和光学传感器, 之外，有效负载还具有用于测距潜水员的声学调制解调器以及多个数据处理和计算板。
This feedback loop requires an underwater acoustic modem to support diver-AUV communication. State-of-the-art data rates in underwater acoustic communications would require tens of minutes to send an uncompressed image from the AUV to the diver. So, one aspect the team is investigating is how to compress information into a minimum amount to be useful, working within the constraints of the low bandwidth and high latency of underwater communications and the low size, weight, and power of the commercial off-the-shelf (COTS) hardware they're using. For their prototype system, the team procured mostly COTS sensors and built a sensor payload that would easily integrate into an AUV routinely employed by the U.S. Navy, with the goal of facilitating technology transition. Beyond sonar and optical sensors, the payload features an acoustic modem for ranging to the diver and several data processing and compute boards.
Miller的 团队在新英格兰沿海 — 周围测试了配备传感器的 AUV 和算法，包括在朴茨茅斯, 新罕布什尔州, 附近的公海，以新罕布什尔大学的 (UNH) Gulf Surveyor 和 Gulf Challenger 沿海研究船作为潜水员代理,，并在波士顿地区查尔斯河, 以 MIT Sailing Pavilion 小艇作为潜水员代理人。
Miller的 team has tested the sensor-equipped AUV and algorithms around coastal New England — including in the open ocean near Portsmouth, New Hampshire, with the University of New Hampshire的 (UNH) Gulf Surveyor and Gulf Challenger coastal research vessels as diver surrogates, and on the Boston-area Charles River, with an MIT Sailing Pavilion skiff as the surrogate.
"UNH 船只装备精良，可以进入真实的海洋条件。但假装自己是一艘大船的潜水员是很困难的。有了小船,，我们可以更慢地移动，并使相对运动与潜水员和 AUV 一起导航的方式保持一致。"
"The UNH boats are well-equipped and can access realistic ocean conditions. But pretending to be a diver with a large boat is hard. With the skiff, we can move more slowly and get the relative motion in tune with how a diver and AUV would navigate together."
去年夏天, 该团队开始在密歇根理工大学 五大湖研究中心与人类潜水员一起测试设备。尽管潜水员缺乏向 AUV, 反馈信息的接口，但每个人都拿着团队的 管状原型平板电脑, 游泳，称为 "tube-let。" 该 tube-let 配备了压力和深度传感器, 惯性测量单元 ( 来跟踪相对运动), 和测距调制解调器 — 所有必要的组件都是导航算法解决优化问题的必要组件。
Last summer, the team started testing equipment with human divers at Michigan Technological University的 Great Lakes Research Center. Although the divers lacked an interface to feed back information to the AUV, each swam holding the team的 tube-shaped prototype tablet, dubbed a "tube-let." The tube-let was equipped with a pressure and depth sensor, inertial measurement unit (to track relative motion), and ranging modem — all necessary components for the navigation algorithms to solve the optimization problem.
"测试期间的一个挑战是协调潜水员和车辆的运动,，因为他们'尚未协作,"米勒说。 "一旦潜水员进入水下,，就无法与水面团队进行通信。所以,你必须计划把潜水员和车辆放在哪里，这样他们就不会'发生碰撞。"
"A challenge during testing was coordinating the motion of the diver and vehicle, because they don't yet collaborate," Miller says. "Once the divers go underwater, there is no communication with the team on the surface. So, you have to plan where to put the diver and vehicle so they don't collide."
随着内部资助的研究计划即将结束, Miller的 团队现在正在寻求外部赞助来改进技术并将其转让给军事或商业合作伙伴。
With the internally funded research program coming to an end, Miller的 team is now seeking external sponsorship to refine and transition the technology to military or commercial partners.
"现代世界依靠海底电信和电力电缆, 运行，这些电缆很容易受到破坏性行为者的攻击。随着越来越多的国家开发和推进自主海上系统的能力，海底领域的竞争变得越来越激烈。维持全球经济安全和美国在海底领域的战略优势需要利用和结合人工智能和人类的最佳能力," Miller 说。
"The modern world runs on undersea telecommunication and power cables, which are vulnerable to attack by disruptive actors. The undersea domain is becoming increasingly contested as more nations develop and advance the capabilities of autonomous maritime systems. Maintaining global economic security and U.S. strategic advantage in the undersea domain will require leveraging and combining the best of AI and human capabilities," Miller says.