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An Innovative Indoor Localization Method for Agricultural Robots Based on the NLOS Base Station Identification and IBKA-BP Integration

Author

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  • Jingjing Yang

    (Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, China)

  • Lihong Wan

    (Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, China)

  • Junbing Qian

    (Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, China)

  • Zonglun Li

    (Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, China)

  • Zhijie Mao

    (Department of Intelligent Science and Engineering, Yantai Nanshan University, Yantai 264000, China)

  • Xueming Zhang

    (Yunyi Aviation Technology (Yunnan) Co., Ltd., Dabanqiao Subdistrict, Guandu District, Kunming 650000, China)

  • Junjie Lei

    (Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, China)

Abstract

This study proposes an innovative indoor localization algorithm based on the base station identification and improved black kite algorithm–backpropagation (IBKA-BP) integration to address the problem of low positioning accuracy in agricultural robots operating in agricultural greenhouses and breeding farms, where the Global Navigation Satellite System is unreliable due to weak or absent signals. First, the density peaks clustering (DPC) algorithm is applied to select a subset of line-of-sight (LOS) base stations with higher positioning accuracy for backpropagation neural network modeling. Next, the collected received signal strength indication (RSSI) data are processed using Kalman filtering and Min-Max normalization, suppressing signal fluctuations and accelerating the gradient descent convergence of the distance measurement model. Finally, the improved black kite algorithm (IBKA) is enhanced with tent chaotic mapping, a lens imaging reverse learning strategy, and the golden sine strategy to optimize the weights and biases of the BP neural network, developing an RSSI-based ranging algorithm using the IBKA-BP neural network. The experimental results demonstrate that the proposed algorithm can achieve a mean error of 16.34 cm, a standard deviation of 16.32 cm, and a root mean square error of 22.87 cm, indicating its significant potential for precise indoor localization of agricultural robots.

Suggested Citation

  • Jingjing Yang & Lihong Wan & Junbing Qian & Zonglun Li & Zhijie Mao & Xueming Zhang & Junjie Lei, 2025. "An Innovative Indoor Localization Method for Agricultural Robots Based on the NLOS Base Station Identification and IBKA-BP Integration," Agriculture, MDPI, vol. 15(8), pages 1-26, April.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:8:p:901-:d:1639046
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    References listed on IDEAS

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    1. Kaiting Xie & Zhaoguo Zhang & Shiliang Zhu, 2024. "Enhanced Agricultural Vehicle Positioning through Ultra-Wideband-Assisted Global Navigation Satellite Systems and Bayesian Integration Techniques," Agriculture, MDPI, vol. 14(8), pages 1-17, August.
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