This paper presents an adaptive lookahead pure-pursuit lateral controller for optimizing racing metrics such as lap time, average lap speed, and deviation from a reference trajectory in an autonomous racing scenario. We propose a greedy algorithm to compute and assign optimal lookahead distances for the pure-pursuit controller for each waypoint on a reference trajectory for improving the race metrics. We use a ROS based autonomous racing simulator to evaluate the adaptive pure-pursuit algorithm and compare our method with several other pure-pursuit based lateral controllers. We also demonstrate our approach on a scaled real testbed using a F1/10 autonomous racecar. Our method results in a significant improvement (20%) in the racing metrics for an autonomous racecar.
翻译:本文展示了一个适应性外观的纯纯净的横向控制器,用于优化赛标,比如在自动赛事场景中,使用膝上时间、平均膝上速度和偏离参考轨迹。我们建议了一种贪婪的算法,用于计算和分配每一条路径的纯净控制器的最佳外观距离,用于改进种族标尺的参考轨迹。我们用一个基于ROS的自动赛跑模拟器来评估适应性纯净洁算法,并将我们的方法与其他几个基于纯净的横向控制器进行比较。我们还用一辆F1/10自动赛车展示了我们用一个缩放实际测试台的方法。我们的方法使得自动赛车的赛标有显著的改进(20% )。