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

Jingjing Yang, Lihong Wan, Junbing Qian, Zonglun Li, Zhijie Mao, Xueming Zhang and Junjie Lei ()
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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

Agriculture, 2025, vol. 15, issue 8, 1-26

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.

Keywords: base station identification algorithm; backpropagation neural network; intelligent agricultural machinery; improved black kite algorithm; received signal strength indication localization (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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