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• W2989791676 abstract "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." @default.
• W2989791676 created "2019-12-05" @default.
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• W2989791676 date "2019-11-22" @default.
• W2989791676 modified "2023-09-27" @default.
• W2989791676 title "Open-Environment Robotic Acoustic Perception for Object Recognition" @default.
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• W2989791676 doi "https://doi.org/10.3389/fnbot.2019.00096" @default.
• W2989791676 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6883290" @default.
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