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The zero-velocity update (ZUPT) algorithm is a pivotal advancement in pedestrian navigation accuracy, utilizing foot-mounted inertial sensors. Its key issue hinges on accurately identifying periods of zero-velocity during human movement. This paper introduces an innovative adaptive sliding window technique, leveraging the Fourier Transform to precisely isolate the pedestrian’s gait frequency from spectral data. Building on this, the algorithm adaptively adjusts the zero-velocity detection threshold in accordance with the identified gait frequency. This adaptation significantly refines the accuracy in detecting zero-velocity intervals. Experimental evaluations reveal that this method outperforms traditional fixed-threshold approaches by enhancing precision and minimizing false positives. Experiments on single-step estimation show the adaptability of the algorithm to motion states such as slow, fast, and running. Additionally, the paper demonstrates pedestrian trajectory localization experiments under a variety of walking conditions. These tests confirm that the proposed method substantially improves the performance of the ZUPT algorithm, highlighting its potential for pedestrian navigation systems.

The accuracy, speed, and robustness of object detection and recognition are directly related to the harvesting efficiency, quality, and speed of fruit and vegetable harvesting robots. In order to explore the development status of object detection and recognition techniques for fruit and vegetable harvesting robots based on digital image processing and traditional machine learning, this article summarizes and analyzes some representative methods. This article also demonstrates the current challenges and future potential developments. This work aims to provide a reference for future research on object detection and recognition techniques for fruit and vegetable harvesting robots based on digital image processing and traditional machine learning.

Active steering technology is a key technology for automatic driving vehicles to achieve route tracking and obstacle avoidance and risk avoidance, and its performance will affect the stability control of the vehicle. For solving the stability control issues of vehicles, which have uncertainty in model and robustness in system, this paper proposes an active steering control method based on the receding horizon control model. It calculates the optimal control law by this method by using the real-time vehicle state so that it can compensate for the uncertainty caused by model mismatch, interference, etc. The design of the controller is implemented by using the yaw rate deviation of the vehicle as the input of the receding horizon linear quadratic controller model and then inputting the calculated superposition angle into the vehicle model in real time. We built a Simulink control model to implement co-simulation with CarSim to verify the control effect of the controller. In addition, we built a steering hardware-in-the-loop platform based on the LabVIEW RT system. The experimental results show that the active steering system adopting a receding horizon control method had better system robustness and robust stability.