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Target location detection of mobile robots based on R-FCN deep convolutional neural network

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Abstract

In order to improve the target location detection effect of mobile robots, this paper combines convolutional neural network and recurrent neural network to construct a model for solving abnormal sound event detection. Moreover, this paper constructs a convolutional neural network architecture suitable for feature extraction of audio signals, uses the recurrent neural network to classify each frame of audio signals, and applies the improved R-FCN deep convolutional neural network to the target location detection of mobile robots. In addition, this article uses Matlab to carry out system simulation construction, and design and use the system to carry out performance verification. Through experimental research, it can be seen that the target location system of mobile robot based on R-FCN deep convolutional neural network constructed in this paper can effectively improve the location speed and location accuracy compared with traditional location systems.

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Funding

The research is supported by: the 2018 Guangxi Vocational Education Professional Development Research Project "Development Research Base of Industrial Robotics and Intelligent Manufacturing Technology Professional Group" (No.2018080315); 2019 Guangxi University young and middle-aged teachers' research basic capacity improvement project "Research on unmanned AGV distribution system for non-ferrous metal melting and casting section" (No. 2019KY1572).

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  1. Department of Mechanical and Electrical Engineering, Guangxi Modern Polytechnic College, Hechi, 547000, China

    Hua Cen

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  1. Hua Cen
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Correspondence to Hua Cen.

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The author declared that they have no conflicts of interest to this work. I declare that I do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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Cen, H. Target location detection of mobile robots based on R-FCN deep convolutional neural network. Int J Syst Assur Eng Manag 14, 728–737 (2023). https://doi.org/10.1007/s13198-021-01514-z

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  • DOI: https://doi.org/10.1007/s13198-021-01514-z

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