Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24061, United States of America.
Department of Mathematics, Virginia Tech, Blacksburg, VA 24061, United States of America.
Bioinspir Biomim. 2022 Aug 10;17(5). doi: 10.1088/1748-3190/ac7aff.
The ability of certain bat species to navigate in dense vegetation based on trains of short biosonar echoes could provide for an alternative parsimonious approach to obtaining the sensory information that is needed to achieve autonomy in complex natural environments. Although bat biosonar has much lower data rates and spatial (angular) resolution than commonly used human-made sensing systems such as LiDAR or stereo cameras, bat species that live in dense habitats have the ability to reliably detect narrow passageways in foliage. To study the sensory information that the animals may have available to accomplish this, we have used a biomimetic sonar system that was combined with a camera to record echoes and synchronized images from 10 different field sites that featured narrow passageways in foliage. The synchronized camera and sonar data allowed us to create a large data set (130 000 samples) of labeled echoes using a teacher-student approach that used class labels derived from the images to provide training data for echo-based classifiers. The performance achieved in detecting passageways based on the field data closely matched previous results obtained for gaps in an artificial foliage setup in the laboratory. With a deep feature extraction neural network (VGG16) a foliage-versus-passageway classification accuracy of 96.64% was obtained. A transparent artificial intelligence approach (class-activation mapping) indicated that the classifier network relied heavily on the initial rising flank of the echoes. This finding could be exploited with a neuromorphic echo representation that consisted of times where the echo envelope crossed a certain amplitude threshold in a given frequency channel. Whereas a single amplitude threshold was sufficient for this in the previous laboratory study, multiple thresholds were needed to achieve an accuracy of 92.23%. These findings indicate that despite many sources of variability that shape clutter echoes from natural environments, these signals contain sufficient sensory information to enable the detection of passageways in foliage.
某些蝙蝠物种能够根据短生物声纳回波序列在茂密的植被中导航，这为获取在复杂自然环境中实现自主性所需的感觉信息提供了一种替代的简约方法。尽管蝙蝠生物声纳的数据率和空间（角度）分辨率比常用的人造感应系统（如 LiDAR 或立体相机）低得多，但生活在茂密栖息地的蝙蝠物种有能力可靠地检测到树叶中的狭窄通道。为了研究动物可能获得的感觉信息以实现这一目标，我们使用了一种仿生声纳系统，该系统与相机结合使用，从 10 个具有树叶中狭窄通道的不同野外地点记录回声和同步图像。同步的相机和声纳数据使我们能够使用师生方法创建一个包含 130000 个样本的大型标记回声数据集，该方法使用从图像中得出的类别标签为基于回声的分类器提供训练数据。基于野外数据检测通道的性能与之前在实验室人工植被设置中获得的间隙结果非常匹配。使用深度特征提取神经网络（VGG16），在树叶与通道之间的分类精度达到了 96.64%。透明的人工智能方法（类别激活映射）表明，分类器网络严重依赖回声的初始上升沿。这一发现可以通过一种神经形态回声表示来利用，该表示由回声包络在给定频率通道中穿过某个幅度阈值的时间组成。虽然在之前的实验室研究中，单个幅度阈值就足以满足这一要求，但为了达到 92.23%的精度，需要多个阈值。这些发现表明，尽管自然环境中的杂波回波存在许多形成变异性的来源，但这些信号包含足够的感觉信息，可用于检测树叶中的通道。
