滑移转向机器人不确定度分析及路面识别方法
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摘要
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,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;为了使仿真结果对研究里程计不确定度有一定的参考价值,在仿真轨迹重复性偏差中选取最大值,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;对仿真结果分析可以得出,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。
Sensor uncertainty is a key issue in mobile robot positioning.The paper analyzes the kinematics of the Pioneer3-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.Adams and Matlab/Simulink co-simulation experiments have obtained multiple sets of data for different sliding speeds at different ground speeds.In order to make the simulation results have a certain reference value for the study of odometer uncertainty,the maximum value is selected in the repetitive deviation of the simulation trajectory,and the results are fitted.The sliding deviation model is 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%.
Sensor uncertainty is a key issue in mobile robot positioning.The paper analyzes the kinematics of the Pioneer3-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.Adams and Matlab/Simulink co-simulation experiments have obtained multiple sets of data for different sliding speeds at different ground speeds.In order to make the simulation results have a certain reference value for the study of odometer uncertainty,the maximum value is selected in the repetitive deviation of the simulation trajectory,and the results are fitted.The sliding deviation model is 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%.
引文
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[12]Weiss C,Fechner N,Stark M,et al.Comparison of different approaches to vibration-based terrain classification[A].The European Conference on Mobile Robots[C].Freiburg,Germany,2007.
[13]Tick D,Rahman T,Busso C,et al.Indoor robotic terrain classification via angular velocity based hierarchical classifier selection[A].IEEE International Conference on Robotics&Automation[C].River Centre,Saint Paul, Minnesota,USA,2012:3594-3600.
[14]宋宇,陈无畏,陈黎卿.基于ADAMS与Matlab的车辆稳定性控制联合仿真研究[J].机械工程学报,2011,47(16):86-92.
[15]张高峰.轮式滑移装载机转向过程分析及仿真[D].太原:太原科技大学,2014.
[16]李强.基于振动信号的轮式机器人地面分类方法研究[D].哈尔滨:哈尔滨工程大学,2013.
[2]杨晶东,杨敬辉,洪炳熔.一种有效的移动机器人里程计误差建模方法[J].自动化学报,2009,35(2):168-173.
[3]Wu Y,Wang T M,Liang J H,et al.Experimental kinematics modeling estimation for wheeled skid-steering mobile robots[A].IEEE International Conference on Robotics and Biomimetics[C].2014.
[4]吴耀,王田苗,王晓刚,等.基于自适应卡尔曼滤波的侧滑移动机器人运动模型估计[J].电子与信息学报,2015,37(12):3016-3024.
[5]郭晓林,潘家平,于诏城.轮式车辆稳态滑移转向特性研究[J].农业装备与车辆工程,2012,50(12):27-30.
[6]陈晋市.滑移装载机行走系统研究[D].吉林:吉林大学,2012.
[7]邵春梅.路面摩擦系数检测方法及纵、横向摩擦系数关联性研究[D].西安:长安大学,2011.
[8]张高峰.轮式滑移装载机转向过程分析及仿真[D].太原:太原科技大学,2014.
[9]崔明月,孙棣华,李永福,等.轮子纵向打滑条件下的移动机器人自适应跟踪控制[J].控制与决策,2013,28(5):664-670.
[10]安雷,张国良,张维平,等.移动机器人扩展卡尔曼滤波定位与传感器误差建模[J].信息与控制,2012,41(4):406-412.
[11]Reinstein M,Kubelka V,Zimmermann K.Terrain adaptive odometry for mobile skid-steer robots[A].IEEEInternational Conference on Robotics&Automation[C].Karlsruhe,Germany,2013:4691-4696.
[12]Weiss C,Fechner N,Stark M,et al.Comparison of different approaches to vibration-based terrain classification[A].The European Conference on Mobile Robots[C].Freiburg,Germany,2007.
[13]Tick D,Rahman T,Busso C,et al.Indoor robotic terrain classification via angular velocity based hierarchical classifier selection[A].IEEE International Conference on Robotics&Automation[C].River Centre,Saint Paul, Minnesota,USA,2012:3594-3600.
[14]宋宇,陈无畏,陈黎卿.基于ADAMS与Matlab的车辆稳定性控制联合仿真研究[J].机械工程学报,2011,47(16):86-92.
[15]张高峰.轮式滑移装载机转向过程分析及仿真[D].太原:太原科技大学,2014.
[16]李强.基于振动信号的轮式机器人地面分类方法研究[D].哈尔滨:哈尔滨工程大学,2013.