Insects have important roles globally in ecology, economy, and health, yet our understanding of their behavior remains limited. Bees, for example, use vision and a tiny brain to find flowers and return home, but understanding how they perform these impressive tasks has been hampered by limitations in recording technology. Here, we present Fast Lock-On (FLO) tracking. This method moves an image sensor to remain focused on a retroreflective marker affixed to an insect. Using paraxial infrared illumination, simple image processing can localize the sensor location of the insect in a few milliseconds. When coupled with a feedback system to steer a high-magnification optical system to remain focused on the insect, a high–spatiotemporal resolution trajectory can be gathered over a large region. As the basis for several robotic systems, we show that FLO is a versatile idea that can be used in combination with other components. We demonstrate that the optical path can be split and used for recording high-speed video. Furthermore, by flying an FLO system on a quadcopter drone, we track a flying honey bee and anticipate tracking insects in the wild over kilometer scales. Such systems have the capability to provide higher-resolution information about insects behaving in natural environments and as such will be helpful in revealing the biomechanical and neuroethological mechanisms used by insects in natural settings.

中文翻译：

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使用 Fast Lock-On 跟踪对昆虫进行高分辨率户外摄像


昆虫在生态、经济和健康方面在全球范围内发挥着重要作用，但我们对它们行为的了解仍然有限。例如，蜜蜂使用视觉和微小的大脑来寻找花朵并回家，但由于记录技术的限制，了解它们如何执行这些令人印象深刻的任务受到了阻碍。在这里，我们介绍快速锁定 （FLO） 跟踪。这种方法使图像传感器保持对贴在昆虫上的反光标记的对焦。使用近轴红外照明，简单的图像处理可以在几毫秒内定位昆虫的传感器位置。当与反馈系统耦合以引导高倍率光学系统保持对昆虫的关注时，可以在大面积上收集高时空分辨率轨迹。作为多个机器人系统的基础，我们表明 FLO 是一个可以与其他组件结合使用的多功能想法。我们证明了光路可以分割并用于录制高速视频。此外，通过在四轴飞行无人机上飞行 FLO 系统，我们可以跟踪飞行的蜜蜂，并预计在野外跟踪昆虫的公里尺度。此类系统能够提供有关昆虫在自然环境中行为的更高分辨率信息，因此将有助于揭示昆虫在自然环境中使用的生物力学和神经行为学机制。