煤矿用无人驾驶辅助运输车辆的蒙特卡罗定位方法

Monte Carlo Localization for autonomous auxiliary transport vehicles used in coal mine

为解决煤矿无人驾驶车辆自主定位这一重要问题,研究了车辆在巷道环境中行驶时的蒙特卡罗定位方法。首先进行了场景分析,综合考虑工作任务和行驶安全性,得出车辆纵向和侧向位姿状态估计的不同要求,其中沿巷道轴线方向的车辆纵向位置在需要时获得,相对巷道中心线基准的车辆侧向位置和转向姿态角应实时精确解算,由此指出基于特征测量的定位方法在巷道环境应用时存在的局限,讨论了侧向激光测距扫描的适用性。进一步将矿井全域地图分解为典型场景区域地图的集合,提出了将车辆自主精确定位问题维持在一个典型场景所对应的区域地图范围内进行的策略。其次论述了蒙特卡罗定位算法的实现方法,分析了煤矿辅助运输车辆的转向结构形式,运用基于车辆的速度运动模型实现了算法的控制更新过程。为使测量概率随车辆位姿变化具有平滑性,建立了基于激光测距传感器的似然域模型实现了算法的测量更新过程,并提出调节似然域模型不确定性参数来避免定位算法失效的方法。最后,通过在典型场景内针对全局定位问题进行蒙特卡罗定位算法仿真,验证了定位策略和算法。通过高斯近似对粒子集进行密度提取来获得置信度分布,并以高斯分布均值作为车辆位姿估计。作为对比,在相同的车辆实际运行轨迹条件下,针对位置跟踪问题完成了基于特征测量的扩展卡尔曼滤波定位算法仿真。结果表明,蒙特卡罗定位中,车辆侧向位置估计和姿态角估计始终能够很好地反映实际状态值,纵向位置的后验概率由多峰分布收敛为单峰分布,反映了全局定位过渡到位置跟踪的过程。扩展卡尔曼滤波定位一方面不能解决全局定位问题;另一方面侧向定位精度受特征方位角测量误差的影响显著。在煤矿巷道应用环境下,基于激光测距扫描的蒙特卡罗定位所获得的侧向定位精度优于基于特征测量的扩展卡尔曼滤波定位情况。在区域地图范围内进行蒙特卡罗定位,实现了矿用车辆的全局定位和位置跟踪,满足了在巷道环境中不同位姿状态的估计及时性差异化要求。

Autonomous vehicles for auxiliary transport become a technology trend of building a smart coal mine.To implement localization,which is one of the important technical components for autonomous vehicles in coal mine,Monte Carlo Localization(MCL)for the vehicle operating in the coal laneway is concentrated on.Firstly,scene analysis is finished.Considering both process characteristics and driving safety,it is analyzed that there are different requirements for pose estimations of the vehicle about the longitudinal and lateral directions of the laneway.In detail,the longitudinal position along the laneway axis should be acquired when necessary,but the lateral position relative to the center line of the laneway and the angular orientation should be calculated in real time.As a result,the limitation of feature-based localization applied to the vehicle in laneways is represented,and availability of the lateral laser range finder is discussed further.Through segmenting the globe map of the coal mine to a set of the typical scene based local maps,the strategy,that accurate localization of the vehicle is always maintained within one of the typical scene based local maps,is put forward.Secondly,the implementation process of MCL is represented.Through analyzing the optional steering mechanism of the auxiliary transport vehicles in coal mine,the control update is implemented with the velocity motion model of the vehicle.To eliminate the lack of smoothness for the measurement probability distribution in pose state,the likelihood fields for the laser range finder are chosen to implement the measurement update,in which the method of variable uncertainty parameter for the likelihood fields is put forward to avoid algorithm failure.Lastly,the simulation verification of strategy and algorithm of localization is achieved.The simulation of MCL for global localization in a typical scene is finished.Belief distributions are computed by extracting a density from particles through a Gaussian approximation,and the Gaussian means are regarded as the pose estimations.As a comparison,on the condition of the same real trajectory of the vehicle as MCL simulation,the simulation of feature-based Extended Kalman Filter(EKF)localization for position tracking is finished.Simulation results show that in MCL the estimations of both the lateral position and the angular orientation fit always the true states of the vehicle,and that the posterior probability distribution of the longitudinal position changes from a multimodal distribution to a unimodal one,so global localization can shift to position tracking.In addition,in EKF localization,which is not suitable for global localization,the accuracy of the lateral position estimation is closely related to the measurement error of the bearing of features.And considering applications in coal mine laneways,the performance of lateral positioning with MCL based on laser range finder is better than that with EKF localization based on a feature measurement.Both the global localization and the position tracking of the vehicle used in coal mine are resolved by MCL in a local map,and the characteristic real-time pose estimation in the laneway is achieved.