Tractor Side-slip Estimation Method Based on Multi-sensor Fusion and Its Validation
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摘要: 针对丘陵山地中拖拉机的侧滑估计，提出了一种融合机器视觉与全球卫星导航定位系统(Global navigation satellite system, GNSS)的多传感器信息融合算法。首先提出了简化的拖拉机运动学模型，再阐述基于GNSS与机器视觉技术的侧滑量估计方法。并通过CarSim和Simulink的联合仿真验证侧滑估计方法的可行性。引入卡尔曼滤波和权重函数对传感器数据进行融合和动态调节。搭建模拟丘陵山地实验平台，在不同的地面倾角、GNSS遮挡条件以及路面条件下进行了实验。实验结果表明，在干燥路面且GNSS遮挡条件下，拖拉机在9°、18°路面条件下行驶时最终融合后的总侧滑量分别为0.322 m和0.432 m,相对误差分别为7.86%和6.00%,即在GNSS信号遮挡的情况下依然能够准确地估计出拖拉机的侧滑量。研究可为拖拉机的精确横向控制提供新的方法和实验基础。Abstract: To address the issue of estimating tractor side-slip in hilly and mountainous terrains, a multi-sensor information fusion algorithm that integrated machine vision and the global navigation satellite system（GNSS） was proposed. Initially, a simplified kinematic model of the tractor was presented, followed by separate discussions on skid estimation methods based on GNSS and machine vision technologies. The feasibility of the skid estimation methods was validated through joint simulation using CarSim and Simulink. Kalman filtering and weighting functions were introduced to dynamically fuse and adjust sensor data. An experimental platform mimicking hilly terrains was set up to conduct tests under varying road slopes, GNSS coverage conditions, and road surface conditions. The experimental results showed that under dry road conditions and GNSS blockage, the total skid amounts for tractors driving on 9° and 18° slopes were 0.322 m and 0.432 m, respectively, with relative error of 7.86% and 6.00%, which indicated that accurate skid estimation was still achievable even when GNSS signals were obstructed. The research result can provide methods and experimental foundations for precise lateral control of tractors.
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