AI 中文总结
研究针对携带移动工具的农业机械侧翻风险问题，将预测侧翻风险算法进行适应性调整，通过在自行式喷雾器上的现场测试，包括静态和动态测试，获取数据建立模型，初步结果显示算法能捕捉LTR变化，可用于评估算法预防事故有效性。
AI 中文摘要
携带移动工具或农具的农业机械具有内部动力学特性，使其比固定配置的机械更容易发生侧翻风险。先前的研究已开发出通过估计相关指标（主要是负载转移率LTR）来实时预测侧翻风险的算法，主要针对静态配置车辆。本文介绍了这些算法对带有移动工具的机械的适应性，并描述了在自行式喷雾器上进行的现场测试，以评估其预测侧翻的有效性。测试还旨在获取必要数据以建立车辆的精确数值模型并进行高质量模拟测试。本文描述了所选仪器、测试程序和实验设计。这些现场测试包括静态测试和动态测试。静态测试确定车辆参数，动态测试实时测量与机器位移和轮/地作用力相关的变量。初步结果证明了算法捕捉LTR变化的能力，LTR表示横向和纵向侧翻风险。通过这些测试收集的数据将使车辆位移能够通过模拟回放，产生模拟侧翻事故并评估所开发算法在事故预防中的有效性。
英文摘要
Agricultural machines that carry mobile tools or implements have an internal dynamic that makes them more prone to rollover risk than machines with fixed configurations. Previous research has led to the development of algorithms capable of predicting rollover risk in real time by estimating relevant metrics, primarily the LTR (Load Transfer Ratio), with a focus on vehicles with static configurations. This paper presents the adaptation of these algorithms for machines with mobile tools and describes the field tests, conducted on a self-propelled sprayer, to evaluate their effectiveness in predicting rollover. The tests also aimed to acquire the necessary data to build an accurate numerical model of the vehicle and produce high-quality simulated tests. This paper describes the chosen instrumentation, the test procedure, and the experimental design of these tests. These field tests include static tests and dynamic tests. Static tests determine vehicle parameters, while dynamic tests measure variables related to the machine displacement and wheel/ground forces in real time. The initial results demonstrate the algorithms' ability to capture variations in LTR, which express lateral and longitudinal rollover risk. The data collected through these tests will enable the vehicle displacements to be replayed by simulation, producing simulated rollover accidents and evaluating the effectiveness of the developed algorithms in accident prevention.
Journal refAGRITECHDAY, AXEMA, Oct 2025, Clermont-Ferrand, France. pp.198
