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:牵引参数,即地轮接触动力学表征,是影响车辆能源效率的中心因素。为了优化燃油消耗,减少轮胎磨损,提高生产力等,了解当前牵引参数是不可避免的。不幸的是,这些参数难以测量,需要昂贵的力和扭矩传感器。另一个方法是使用系统辨识来确定它们。在这项工作中,我们使用移动机器人进行野外实验证明了这种方法。该方法基于自适应卡尔曼滤波器。我们展示了它如何在线估计牵引参数,在领域内运动期间,并将其与通过安装用于验证的6向力矩传感器确定的值进行比较。粘附滑移比曲线数据被记录,并与文献中的曲线进行比较以进行额外的验证。结果可以为多种最优牵引方法奠定基础。