滑移转向机器人不确定度分析及路面识别方法

传感器的不确定度是移动机器人定位中的关键问题。文章对Pineer3-AT滑移转向机器人在转弯时运动学状态进行分析,发现了滑动偏差受地面与轮的摩擦系数及左右两轮的速度影响,滑动偏差的大小即为里程计的不确定度,通过Adams与Matlab/Simulink联合仿真实验得到了在不同地面不同轮速的滑动偏差的大小多组数据,并对结果拟合,建立了滑动偏差模型,并通过实验进行了验证对比。对结果分析,可以得出,摩擦系数越大,在相同速度下滑动偏差越小,根据这一特性,提取小车在不同轮速下的滑动偏差作为地面分类的原始数据。通过k-近邻(KNN)方法,对地面进行分类,识别率达到70%以上。

Uncertainty analysis of slip steering robot and road recognition method

Sensor uncertainty is a key issue in mobile robot positioning. The paper analyzes the kinematics of the Pineer3-AT sliding-steering robot during cornering and finds that the sliding amount is affected by the friction coefficient of the ground and the wheels and the speed of the left and right wheels. The magnitude of the sliding amount affects the odometer"s uncertainty. Adams and Matlab/Simulink co-simulation experiments have obtained multiple sets of data for different sliding speeds at different ground speeds. Fitting the results, a sliding quantity model has been established and verified by experiments. Based on the analysis of the results, it can be concluded that the larger the friction coefficient, the smaller the sliding amount at the same speed. According to this characteristic, the sliding amount of the trolley at different wheel speeds is extracted as the raw data of the ground classification. The k-nearest neighbor (KNN) method was used to classify the ground and the recognition rate reached over 70%.