基于改进ESKF的UWB-IMU无人农业机器人精准定位技术

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中图分类号：S24；S126 文献标志码：A 文章编号：1008-0864（2025）12-0080-14

Abstract：To address the issue of large measurement fluctuations and low accuracy in ultra-wide band（UWB） positioning systems of unmannedagricultural robots operating in complex environments dueto frequent non line of sight（NLOS）communication，an improved error-state Kalman filter（ESKF）tightly coupled UWB and inertial measurement unit （IMU） positioning technique was proposed. Firstly，an asymmetric bidirectional ranging method combined with linear fiting calibration was used to optimize UWB measurement data，and an improved mean filtering algorithm wasdesigned toremoveoutliers.Secondly，UWB-IMUcollaborative positioning wasachieved based on the improved ESKF framework.An adaptive factor was constructed using IMU state prediction information to dynamically adjustthe measurement noise covariance matrix to mitigate the impact ofNLOS erors.Finally，a fourwheeled unmanned agriculturalrobot platform was built，and static and dynamic target positioning experiments were conducted in typical NLOSagricultural scenarios toverify the technology.The results showed that，in dynamic trajectory tracking，the overall positioning accuracywasimproved by 53.38% and 25.15% compared to pure UWB and traditional EKF algorithms，respectively.This method exhibited good robustness in complexocclusion environments and could provide technical support for high-precision autonomous navigation and positioning of unmanned agricultural robots.

KeyWords：ultra-wide band（UWB）；inertial measurement unit（IMU）；non lineof sight（NLOS）；unmanned agricultural robot；error-state Kalman filter（ESKF）； fusion positioning

随着物联网与智能移动装备技术的发展，仓储物流、工业厂房及设施农业等半封闭作业空间对移动平台的自动化作业与精细化管理提出了更高要求。（剩余16057字）