ABB 48990001-PC DSSR 170 直流输入电源单元

　　在任何一种情况下，无人机都需要能够自行决定去哪里以及到达那里的最有效方式。要做到这一点，他们可能只会拥有他们自己可以收集的数据——关于他们目前正在经历的水流的信息。

　　为了应对这一挑战，研究人员转向强化学习 (RL) 网络。与传统的神经网络相比，强化学习网络不是在静态数据集上训练，而是尽可能快地训练，因为它们可以收集经验。这种方案允许它们存在于更小的计算机上。出于这个项目的目的，该团队编写了可以在 Teensy 上安装和运行的软件——一个 2.4 英寸 x 0.7 英寸的微控制器，任何人都可以在亚马逊上以不到 30 美元的价格购买，并且只使用大约 0.5 瓦的功率。

　　使用计算机模拟，在水中流过障碍物时会产生几个向相反方向移动的漩涡，该团队教人工智能以这样一种方式导航，即它利用漩涡后的低速区域滑行到以最小的功率使用目标位置。为了帮助其导航，模拟的游泳者只能获得有关其附近水流的信息，但它很快就学会了如何利用漩涡向所需目标滑行。在物理机器人中，人工智能同样只能访问可以从机载陀螺仪和加速度计收集的信息，这对于机器人平台来说都是相对较小且成本较低的传感器。

　　这种导航类似于鹰和鹰在空中乘热的方式，从气流中提取能量，以最小的能量机动到所需的位置。令人惊讶的是，研究人员发现他们的强化学习算法可以学习比海洋中真鱼使用的导航策略更有效的导航策略。

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　　In either scenario, drones would need to be able to make decisions on their own about where to go and the most efficient way to get there. To do so, they will likely only have data that they can gather themselves — information about the water currents they are currently experiencing.

　　To tackle this challenge, researchers turned to reinforcement learning (RL) networks. Compared to conventional neural networks, reinforcement learning networks do not train on a static data set but rather train as fast as they can collect experience. This scheme allows them to exist on much smaller computers. For the purposes of this project, the team wrote software that can be installed and run on a Teensy — a 2.4-inch by 0.7-inch microcontroller that anyone can buy for less than $30 on Amazon and only uses about a half watt of power.

　　Using a computer simulation in which flow past an obstacle in water created several vortices moving in opposite directions, the team taught the AI to navigate in such a way that it took advantage of low-velocity regions in the wake of the vortices to coast to the target location with minimal use of power. To aid its navigation, the simulated swimmer only had access to information about the water currents at its immediate location, yet it soon learned how to exploit the vortices to coast toward the desired target. In a physical robot, the AI would similarly only have access to information that could be gathered from an onboard gyroscope and accelerometer, which are both relatively small and low-cost sensors for a robotic platform.

　　This kind of navigation is analogous to the way eagles and hawks ride thermals in the air, extracting energy from air currents to maneuver to a desired location with minimum energy. Surprisingly, the researchers discovered that their reinforcement learning algorithm could learn navigation strategies that are even more effective than those thought to be used by real fish in the ocean.