Traction parameters, that characterize the ground-wheel contact dynamics, are the central factor in the energy efficiency of vehicles. To optimize fuel consumption, reduce wear of tires, increase productivity etc., knowledge of current traction parameters is unavoidable. Unfortunately, these parameters are difficult to measure and require expensive force and torque sensors. An alternative way is to use system identification to determine them. In this work, we validate such a method in field experiments with a mobile robot. The method is based on an adaptive Kalman filter. We show how it estimates the traction parameters online, during the motion on the field, and compare them to their values determined via a 6-directional force-torque sensor installed for verification. Data of adhesion slip ratio curves is recorded and compared to curves from literature for additional validation of the method. The results can establish a foundation for a number of optimal traction methods.

翻译：表征地面-车轮接触动力学的牵引参数是车辆能量效率的核心因素。为优化燃油消耗、减少轮胎磨损、提升作业效率等，获取当前牵引参数值不可或缺。然而，这些参数难以直接测量，且需配备昂贵的力与扭矩传感器。替代方案是采用系统辨识技术进行参数确定。本研究通过移动机器人野外实验验证了该辨识方法，该方法基于自适应卡尔曼滤波器。我们展示了在田间运动过程中如何在线估计牵引参数，并将其与通过六向力-扭矩传感器（为验证而安装）测得的参数值进行对比。同时记录附着-滑移率曲线数据，并与文献中的曲线进行对比以进一步验证该方法。研究结果可为多种最优牵引控制方法奠定基础。