Adaptive Lookahead Pure-Pursuit for Autonomous Racing
This addresses performance optimization for autonomous racing vehicles, though it appears incremental as it builds on existing pure-pursuit methods.
The paper tackles the problem of optimizing racing metrics like lap time and trajectory deviation for autonomous racing by proposing an adaptive lookahead pure-pursuit controller with a greedy algorithm to assign optimal lookahead distances, resulting in a 20% improvement in racing metrics.
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.