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：牵引参数表征了地面-车轮接触动力学特性，是影响车辆能量效率的核心因素。为优化燃料消耗、降低轮胎磨损、提升作业效率等，必须掌握当前牵引参数。然而这些参数难以直接测量，需要昂贵的力和力矩传感器。替代方案是采用系统辨识技术进行参数确定。本研究在移动机器人野外实验中验证了该方法。该方法基于自适应卡尔曼滤波器，展示了如何在线估计运动过程中的牵引参数，并与为验证而安装的六向力-力矩传感器测量值进行对比。采集了黏附-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方法。研究结果可为多种最优牵引方法的开发奠定基础。