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.

表征接地轮接触动力学特性的牵引力参数是影响车辆能源效率的主要因素。为了优化燃料消耗，减少轮胎磨损，提高生产率等，不可避免地需要了解当前的牵引参数。不幸的是，这些参数难以测量并且需要昂贵的力和扭矩传感器。另一种方法是使用系统标识来确定它们。在这项工作中，我们在移动机器人的现场实验中验证了这种方法。该方法基于自适应卡尔曼滤波器。我们展示了它如何在野外运动期间在线估计牵引力参数，并通过安装的用于验证的6向力-扭矩传感器将其与确定的值进行比较。记录粘附滑移率曲线的数据，并将其与文献中的曲线进行比较，以进一步验证该方法。结果可以为多种最佳牵引方法奠定基础。