van Breugel Floris, Morgansen Kristi, Dickinson Michael H
California Institute of Technology, Pasadena, CA, USA.
Bioinspir Biomim. 2014 Jun;9(2):025002. doi: 10.1088/1748-3182/9/2/025002. Epub 2014 May 22.
Vision is arguably the most widely used sensor for position and velocity estimation in animals, and it is increasingly used in robotic systems as well. Many animals use stereopsis and object recognition in order to make a true estimate of distance. For a tiny insect such as a fruit fly or honeybee, however, these methods fall short. Instead, an insect must rely on calculations of optic flow, which can provide a measure of the ratio of velocity to distance, but not either parameter independently. Nevertheless, flies and other insects are adept at landing on a variety of substrates, a behavior that inherently requires some form of distance estimation in order to trigger distance-appropriate motor actions such as deceleration or leg extension. Previous studies have shown that these behaviors are indeed under visual control, raising the question: how does an insect estimate distance solely using optic flow? In this paper we use a nonlinear control theoretic approach to propose a solution for this problem. Our algorithm takes advantage of visually controlled landing trajectories that have been observed in flies and honeybees. Finally, we implement our algorithm, which we term dynamic peering, using a camera mounted to a linear stage to demonstrate its real-world feasibility.
视觉可以说是动物用于位置和速度估计的最广泛使用的传感器，并且它在机器人系统中也越来越多地被使用。许多动物利用立体视觉和物体识别来对距离进行准确估计。然而，对于果蝇或蜜蜂这样的微小昆虫来说，这些方法并不适用。相反，昆虫必须依靠光流计算，光流可以提供速度与距离的比率，但不能单独提供任何一个参数。尽管如此，苍蝇和其他昆虫擅长降落在各种基质上，这种行为本质上需要某种形式的距离估计，以便触发诸如减速或腿部伸展等与距离相适应的运动动作。先前的研究表明，这些行为确实受视觉控制，这就引出了一个问题：昆虫如何仅利用光流来估计距离？在本文中，我们使用非线性控制理论方法为这个问题提出一个解决方案。我们的算法利用了在苍蝇和蜜蜂中观察到的受视觉控制的着陆轨迹。最后，我们使用安装在线性平台上的摄像头实现了我们的算法，我们将其称为动态凝视，以证明其在现实世界中的可行性。
