发表机构
Technical University of Munich（慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对有机肥撒施机中异质物料与倾斜角导致的流量预测难题，提出基于扩展卡尔曼滤波的传感器融合方法，集成称重、速度与惯性测量及动力学模型，在牛粪撒施试验中质量估计RMSE为11.9千克，流量误差在8.4%以内。
AI 中文摘要
精准农业需要准确了解机具状态。在有机肥撒施机中，异质物料和变化的田间条件使流量预测复杂化，倾斜角度会改变输送底板与物料之间的滑移行为。本文提出一种基于模型的传感器融合方法，用于滑移和质量流量估计，将称重、输送速度及惯性测量集成于扩展卡尔曼滤波器中，并结合体积流量模型和纵向动力学模型。在真实牛粪撒施试验中的验证表明，质量估计跟踪原始重量，均方根误差为11.9千克，积分质量流量在观测质量损失的8.4%以内。
英文摘要
Precision farming requires accurate knowledge of implement states. In organic fertilizer spreaders, heterogeneous material and variable field conditions complicate flow-rate prediction, and tilt angles alter the slip behaviour between transport floor and material. This paper presents a model-based sensor fusion method for slip and mass-flow estimation, integrating weighing, conveyor-speed, and inertial measurements in an Extended Kalman Filter with a volumetric flow model and a longitudinal dynamics model. Validation on real-world cattle-manure spreading shows mass estimates tracking the raw weight with an RMSE of 11.9 kg and integrated mass flow within 8.4 % of the observed mass loss.
