Holistic visual encoding of ant-like routes: Navigation without waypoints
Holistic visual encoding of ant-like routes: Navigation without waypoints
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DOI：
10.1177/1059712310395410
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发表时间：
2011-02-01
影响因子：
1.6
通讯作者：
Husbands, Philip
中科院分区：
文献类型：
--
作者：
Baddeley, Bart;Graham, Paul;Husbands, Philip
众所周知，蚂蚁通过复杂的地形学习长的视觉引导路线。然而，视觉信息首先被学习然后用于控制路线方向的机制还没有被很好地理解。在这篇文章中，我们提出了一个简约的机制，视觉引导的路线如下。我们调查是否一个简单的方法，包括扫描环境和移动的方向，似乎最熟悉的，可以提供一个模型的视觉引导的路线学习蚂蚁。我们通过训练分类器来确定给定视图是否是路线的一部分，并使用此分类中的置信度作为熟悉度的代理，从而实现视图熟悉度作为导航的手段。通过运动和观看方向的耦合，熟悉的视图指定了熟悉的观看方向，从而指定了要进行的熟悉的运动。我们展示了我们的方法的可行性，作为一个模型的蚂蚁般的路线采集通过学习一系列的非平凡的路线，通过室内环境中使用的大型龙门机器人配备了全景摄像机。
It is known that ants learn long visually guided routes through complex terrain. However, the mechanisms by which visual information is first learned and then used to control a route direction are not well understood. In this article, we propose a parsimonious mechanism for visually guided route following. We investigate whether a simple approach, involving scanning the environment and moving in the direction that appears most familiar, can provide a model of visually guided route learning in ants. We implement view familiarity as a means of navigation by training a classifier to determine whether a given view is part of a route and using the confidence in this classification as a proxy for familiarity. Through the coupling of movement and viewing direction, a familiar view specifies a familiar direction of viewing and thus a familiar movement to make. We show the feasibility of our approach as a model of ant-like route acquisition by learning a series of nontrivial routes through an indoor environment using a large gantry robot equipped with a panoramic camera.