An Innovative Indoor Localization Method for Agricultural Robots Based on the NLOS Base Station Identification and IBKA-BP Integration DOI Creative Commons
Jingjing Yang, Lihong Wan,

Junbing Qian

et al.

Agriculture, Journal Year: 2025, Volume and Issue: 15(8), P. 901 - 901

Published: April 21, 2025

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

Language: Английский

Gauss-AUKF based UWB/IMU fusion localization approach DOI

Mingsheng Wei,

L. Liu, Shidang Li

et al.

Ad Hoc Networks, Journal Year: 2025, Volume and Issue: unknown, P. 103855 - 103855

Published: April 1, 2025

Language: Английский

Citations

0
An Innovative Indoor Localization Method for Agricultural Robots Based on the NLOS Base Station Identification and IBKA-BP Integration DOI Creative Commons
Jingjing Yang, Lihong Wan,

Junbing Qian

et al.

Agriculture, Journal Year: 2025, Volume and Issue: 15(8), P. 901 - 901

Published: April 21, 2025

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

Language: Английский

Citations

0