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首页> 外文会议>International Joint Conference on Neural Networks >Terrain Classification for Autonomous Vehicles Using Bat-Inspired Echolocation
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Terrain Classification for Autonomous Vehicles Using Bat-Inspired Echolocation

机译：使用蝙蝠启发回声的自动驾驶汽车地形分类

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Many types of bats use echolocation to sense their environment. Despite often have little or no visual acuity, they are able to acquire very detailed views of their surroundings through emission, receipt, and analysis of acoustic pulses. In this study autonomous navigation was examined with respect to classification of nearby terrain. The goal of this effort was to demonstrate that a bat-inspired acoustic sensor could be built, and when trained using advanced signal filtering and machine learning techniques, could be used to accurately classify terrain types for a small mobile robot. A dual channel in-air sonar was constructed using two common piezoelectric transmitter elements with 25 kHz and 32 kHz nominal center frequencies, and echo data was collected from grass, concrete, sand, and gravel terrain substrates. Higher dimension time, frequency, and time-frequency PCA scores were used to discriminate between terrain substrates. These features were used to train a support vector machine (SVM) to classify the terrain types. The SVM-based classifier was able to classify terrain types at a greater than 97% success rate using the constructed bat-inspired echolocation sensor.
机译：许多类型的蝙蝠都使用回声定位来感知其环境。尽管通常视力很少或没有视力，但他们仍然可以通过发射，接收和分析声脉冲来获取周围环境的非常详细的视图。在这项研究中，针对附近地形的分类检查了自主导航。这项工作的目的是证明可以构建蝙蝠风格的声音传感器，并且在使用高级信号过滤和机器学习技术进行训练时，可以用来为小型移动机器人准确地分类地形类型。使用两个常见的压电发射器元件（标称中心频率为25 kHz和32 kHz）构造了一个双通道空中声纳，并从草，混凝土，沙子和砾石地形基底中收集了回波数据。较高维度的时间，频率和时频PCA分数用于区分地形基底。这些功能用于训练支持向量机（SVM）来对地形类型进行分类。基于SVM的分类器使用蝙蝠启发式回声定位传感器，能够以超过97％的成功率对地形类型进行分类。

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