Pant Vivek, Higgins Charles M
Department of Electrical and Computer Engineering, The University of Arizona, Tucson, AZ 85721, USA.
Biol Cybern. 2012 Jul;106(4-5):307-22. doi: 10.1007/s00422-012-0499-1. Epub 2012 Jun 29.
Collision avoidance models derived from the study of insect brains do not perform universally well in practical collision scenarios, although the insects themselves may perform well in similar situations. In this article, we present a detailed simulation analysis of two well-known collision avoidance models and illustrate their limitations. In doing so, we present a novel continuous-time implementation of a neuronally based collision avoidance model. We then show that visual tracking can improve performance of these models by allowing an relative computation of the distance between the obstacle and the observer. We compare the results of simulations of the two models with and without tracking to show how tracking improves the ability of the model to detect an imminent collision. We present an implementation of one of these models processing imagery from a camera to show how it performs in real-world scenarios. These results suggest that insects may track looming objects with their gaze.
从昆虫大脑研究中得出的避撞模型在实际碰撞场景中并非普遍表现良好，尽管昆虫自身在类似情况下可能表现出色。在本文中，我们对两个著名的避撞模型进行了详细的模拟分析，并阐述了它们的局限性。在此过程中，我们提出了一种基于神经元的避撞模型的新型连续时间实现方式。然后我们表明，视觉跟踪可以通过允许对障碍物与观察者之间的距离进行相对计算来提高这些模型的性能。我们比较了有跟踪和无跟踪情况下两个模型的模拟结果，以展示跟踪如何提高模型检测即将发生碰撞的能力。我们展示了其中一个处理来自相机图像的模型的实现方式，以说明它在现实场景中的表现。这些结果表明昆虫可能会用它们的目光跟踪逼近的物体。
