无人驾驶矿用运输车辆感知及控制方法

Abstract:

In order to solve the problems of low production efficiency and frequent safety accidents in mining areas, a driverless perception and control method for mining vehicles was proposed. In the part of perception, a multi-target recognition architecture based on the fusion of lidar and millimeter-wave radar was designed. On the basis of data association, the joint probabilistic data association (JPDA) algorithm based on Kalman filter was applied to realize multi-target recognition in mining environment. In the control part, the lateral control and longitudinal control were decoupled by the way of path preview tracking, and the deviation was corrected in real time through the feedback mechanism to realize the accurate lateral and longitudinal control of the driverless mining vehicle. In addition, the driverless system platform of real mine vehicle was built, and the above perception and control methods were tested in different scenarios in the mining area. The experimental results show that the perception algorithm realize the accurate detection of the drivable area of the mining road, and identify a variety of obstacle types. The control algorithm realize the accurate control of the longitudinal speed and lateral position of driverless mining vehicles in uphill and downhill scenarios, so as to meet the of practical applications.