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.

Compared with thrombus dissolution using only thrombolytic agents, an advanced therapy of direct stirring of the blood clot can yield shorter recanalization time and higher recanalization velocity. Our previous research presented the design of a novel micro-stirrer, which can convert longitudinal vibration into transverse vibration and eventually generate opposite transverse vibration at the end-effort, like a scissor, for efficient blood clot stirring and thrombus dissolution acceleration. Transverse vibration is the most effective movement to dissolve thrombi. However, the small size of blood vessels has strict limits, which will greatly affect the output transverse vibration. Therefore, to improve the output performance of the micro-stirrer in curved and narrow vascular spaces, the analysis and structure optimization of the micro-stirrer is expected to increase the vibration mode conversion efficiency from longitudinal to transverse mode. The design concept and theoretical analysis of the micro-stirrer are presented in detail. Aiming to obtain the optimal structure parameters of the micro-stirrer, the mathematical model is established and analyzed. Next, a series of finite element models involving important structure parameters are designed and investigated. Finally, the optimal structure parameters are obtained, and the stirring effect in a blood vessel is verified by simulation and experiment.