Research on Mobile Robot Bionic Location Algorithm Based on Growing Self-Organizing Map

Mengyuan CHEN; Minghui XU

doi:10.11999/JEIT200025

Volume 43 Issue 4

Apr. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(4): 1003-1013

Mengyuan CHEN, Minghui XU. Research on Mobile Robot Bionic Location Algorithm Based on Growing Self-Organizing Map[J]. Journal of Electronics & Information Technology, 2021, 43(4): 1003-1013. doi: 10.11999/JEIT200025

Citation:

Mengyuan CHEN, Minghui XU. Research on Mobile Robot Bionic Location Algorithm Based on Growing Self-Organizing Map[J]. Journal of Electronics & Information Technology, 2021, 43(4): 1003-1013. doi: 10.11999/JEIT200025

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Research on Mobile Robot Bionic Location Algorithm Based on Growing Self-Organizing Map

doi: 10.11999/JEIT200025

Mengyuan CHEN^{1, 2
,
,},
Minghui XU²

1.
Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Anhui Polytechnic University, Wuhu 241000, China
2.
School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China

Funds: The National Natural Science Foundation of China (61903002), The Natural Science Foundation of Anhui Province (1808085QF215), The Key Research and Development Project of Anhui Province (1804b06020375), The Science and Technology Planning Project of Wuhu，Anhui Province (Key Research and Development, 2020yf59)

Received Date: 2020-01-07
Rev Recd Date: 2021-02-21

Available Online: 2021-03-03

Publish Date: 2021-04-20

Abstract

Abstract

In order to improve the positioning accuracy of mobile robots in Simultaneous Localization And Mapping (SLAM), a bionic localization algorithm based on Growing Self-Organizing Map(GSOM) neural network is proposed. The method connects the activation characteristics of the place cells with the neural network output layer neurons to establish a response, and constructs a spatial topology map through the GSOM neural network, and uses the perceived distance information to realize the activation response of the place cells to estimate the position of the robot. The running path of the robot is restored in this way. The experimental results show that the cell spacing R has a great influence on the positioning accuracy. Choosing the appropriate cell spacing can effectively reduce the learning time of the neural network and improve the positioning accuracy. The average error of the algorithm is less than 0.153 m, and the positioning accuracy is 90.243%, which is better than the original algorithm. It is verified that the model established by the algorithm can realize the spatial position representation of the robot, improves the positioning accuracy of the object under the experimental scene, and shows good position estimation performance.
- Mobile robot,
- Growing Self-Organizing Map(GSOM),
- Place cell,
- Positional representation,
- Positioning accuracy

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References(21)

References

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