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Methods ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Neurorobot. | doi: 10.3389/fnbot.2019.00096

Open-Enviroment Robotic Acoustic Perception for Object Recognition

 Shaowei Jin1, 2,  Huaping Liu3*, Bowen Wang1, 2 and  Fuchun Sun4
  • 1State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Department of Biomedical Engineering, Hebei University of Technology, China
  • 2Hebei Province Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, China
  • 3Research State Key Laboratory of Intelligent Technology and Systems, National Laboratory for Information Science and Technology, Tsinghua University, China
  • 4State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, China

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 signal 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 object is dynamically changing, so it is necessary to develop methods that can recognize all class 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.

Keywords: Open environment, interactive perception, objects in containers, Acoustic features, object recognition, kernel k nearest neighbor

Received: 07 Aug 2019; Accepted: 01 Nov 2019.

Copyright: © 2019 Jin, Liu, Wang and Sun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Huaping Liu, Research State Key Laboratory of Intelligent Technology and Systems, National Laboratory for Information Science and Technology, Tsinghua University, Beijing, China, hpliu@tsinghua.edu.cn