Abstract
Online planning and execution of minimum-time maneuvers on three-dimensional (3D) circuits is an open challenge in autonomous vehicle racing. In this paper, we present an artificial race driver (ARD) to learn the vehicle dynamics, plan and execute minimum-time maneuvers on a 3D track. ARD integrates a novel kineto-dynamical (KD) vehicle model for trajectory planning with economic nonlinear model predictive control (E-NMPC). We use a high-fidelity vehicle simulator (VS) to compare the closed-loop ARD results with a minimum-lap-time optimal control problem (MLT-VS), solved offline with the same VS. Our ARD sets lap times close to the MLT-VS, and the new KD model outperforms a literature benchmark. Finally, we study the vehicle trajectories, to assess the re-planning capabilities of ARD under execution errors. A video with the main results is available as supplementary material.
Abstract (translated)
在线规划和执行三维（3D）赛道上最短时间行驶策略，是自主赛车领域的开放性挑战。本文中，我们提出了一种人工赛车手 (ARD)，用于学习车辆动力学，并在3D赛道上进行最短时间内动作的计划与执行。ARD整合了新型动力-动态(KD)车辆模型来进行轨迹规划以及经济型非线性预测控制(E-NMPC)。我们使用高保真度车辆模拟器(VS)，将闭环ARD结果与通过同一VS离线解决的最小圈速最优控制问题(MLT-VS)进行对比。我们的ARD实现了接近于MLT-VS的圈速，并且新的KD模型在性能上优于文献中的基准。最后，我们研究了车辆轨迹，以评估ARD在执行错误情况下的重新规划能力。该文的主要结果视频作为补充材料提供。
URL
https://arxiv.org/abs/2502.03454
