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Neural Network-Based Indoor Autonomously-Navigated AGV Motion Trajectory Data Fusion

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Abstract

Owing to the possibility of skidding with the ground in motion, the positioning of the autonomously-navigated automated guided vehicle (AGV) based on the wheel odometry (Odom) will bring errors, meanwhile, the rotation error is remarkably larger than the linear motion error. Three neural network methods were proposed in this study for the data fusion of the Odom and IMU, i.e., back-propagation (BP) neural network method, one-dimensional convolutional (1D-CNN) method, and long short-term memory (LSTM) method. The data fusion results of the Odom and IMU obtained by the extended Kalman filtering method were taken as a reference. The performance of the aforementioned three neural networks for the data fusion of the Odom and IMU was compared respectively. The experimental results indicate that all three neural networks can reduce the rotation error of AGV to some extent. It was also found that 1D-CNN possesses the fastest training speed, and the positioning accuracy is the highest when employing 1D-CNN for data fusion in the application.

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Funding

This work is supported by the Natural Science Foundation of Guangdong Province, China, project no. 2020A 1515011503.

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Authors and Affiliations

  1. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China

    Yanming Quan, Lingwei Huang, Lei Ma, Yiming He & Ronghui Wang

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  1. Yanming Quan
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  2. Lingwei Huang
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  3. Lei Ma
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  4. Yiming He
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  5. Ronghui Wang
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Correspondence to Lingwei Huang.

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The authors declare that they have no conflicts of interest.

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Yanming Quan, Huang, L., Ma, L. et al. Neural Network-Based Indoor Autonomously-Navigated AGV Motion Trajectory Data Fusion. Aut. Control Comp. Sci. 55, 334–345 (2021). https://doi.org/10.3103/S0146411621040076

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