Object recognition in containers is extremely difficult for robots. Dynamic audio signals are more responsive to an object's internal property. Therefore, we adopt the dynamic contact method to collect acoustic signals in the container and recognize objects in containers. Traditional machine learning is to recognize objects in a closed environment, which is not in line with practical applications. In real life, exploring objects is dynamically changing, so it is necessary to develop methods that can recognize all classes of objects in an open environment. A framework for recognizing objects in containers using acoustic signals in an open environment is proposed, and then the kernel k nearest neighbor algorithm in an open environment (OSKKNN) is set. An acoustic dataset is collected, and the feasibility of the method is verified on the dataset, which greatly promotes the recognition of objects in an open environment. And it also proves that the use of acoustic to recognize objects in containers has good value.

中文翻译：

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用于物体识别的开放环境机器人声感知。

对于机器人而言，容器中的对象识别非常困难。动态音频信号对对象的内部属性更敏感。因此，我们采用动态接触法来收集容器中的声音信号并识别容器中的物体。传统的机器学习是在封闭的环境中识别对象，这与实际应用不符。在现实生活中，探索对象是动态变化的，因此有必要开发一种可以识别开放环境中所有类别的对象的方法。提出了一种在开放环境中利用声信号识别容器中物体的框架，然后设置了开放环境中的核k最近邻算法（OSKKNN）。收集了一个声学数据集，并在该数据集上验证了该方法的可行性，这极大地促进了开放环境中对象的识别。并且还证明了利用声学识别容器中的物体具有很好的价值。