让自动驾驶赛车在三维赛道上实时规划最快过弯路径并精准执行。
Kineto-Dynamical Planning and Accurate Execution of Minimum-Time Maneuvers on Three-Dimensional Circuits
- 用新型动力学模型结合经济型非线性预测控制，实现轨迹规划与执行一体化。
- 实测圈速接近离线最优解，且优于现有基准方法。
- 具备应对执行误差的重规划能力，适合高动态自动驾驶场景。
在自主赛车领域，如何在线规划并执行三维赛道上的最短时间行驶轨迹仍是开放挑战。本文提出一种人工赛车驾驶员（ARD），通过学习车辆动力学，在三维赛道上规划并执行最短时间机动动作。ARD融合了新颖的运动-动力学（KD）车辆模型与经济型非线性模型预测控制（E-NMPC）。我们使用高保真车辆仿真器（VS）将闭环ARD结果与同一仿真器求解的离线最短圈速最优控制问题（MLT-VS）进行对比。实验表明，ARD的圈速接近MLT-VS性能，且新提出的KD模型优于文献中的基准方法。最后，我们分析了车辆轨迹，评估了ARD在执行误差下的重规划能力。相关视频结果作为补充材料提供。
原文摘要 · Abstract (English)
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.
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