发表机构
Zhejiang University; ETH Zurich; Center for Project-Based Learning（浙江大学; 苏黎世联邦理工学院; 项目式学习中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自主赛车极限操控下几何控制器失效的问题，提出结合MPC与SGP残差校正的MAP2算法，实车实验显示其跟踪误差和单圈时间均显著优于MAP与PP。
AI 中文摘要
自主赛车需要在车辆操控极限附近实现精确的轨迹跟踪，同时保持较低的计算延迟。几何控制器计算效率高，但阿克曼转向几何在极限操控条件下会失效。基于模型和加速度的追踪（MAP）保留了几何方法的简洁性，同时利用了轮胎动力学，但它本质上仍是依赖阿克曼转向几何的几何控制器，其轮胎模型可能无法完全捕捉车辆的实际动态响应。本文提出MAP2，一种结合基于曲率的运动学模型预测控制（MPC）与稀疏高斯过程（SGP）残差校正的基于模型的追踪控制器。该算法利用MPC在预测时域内优化运动学控制输入，并通过经SGP残差校正增强的轮胎动力学模型将其映射为转向指令。实车实验表明，MAP2的横向跟踪误差和单圈时间均大幅降低；与MAP和纯追踪（PP）相比，MAP2的平均横向跟踪误差分别降低37.99%和44.65%，平均单圈时间至少降低1.5%。
英文摘要
Autonomous racing requires accurate trajectory tracking near the handling limits of a vehicle while maintaining low computational latency. Geometric controllers are computationally efficient, but the Ackermann steering geometry becomes invalid under limit-handling conditions. Model- and Acceleration-based Pursuit (MAP) preserves the simplicity of geometric approaches while leveraging tire dynamics. Yet MAP remains fundamentally a geometric controller that relies on Ackermann steering geometry, and its tire model may not fully capture the vehicle's actual dynamic response. This paper presents MAP2, a model-based pursuit controller that combines a curvature-based kinematic MPC and a Sparse Gaussian Process (SGP) residual correction. The proposed algorithm uses MPC to optimize kinematic control inputs over a prediction horizon and maps them to steering commands through a tire dynamics model augmented with SGP residual correction. Real-world vehicle experiments demonstrate substantial reductions in lateral tracking error and lap time. Compared with MAP and Pure Pursuit (PP), MAP2 reduces the average lateral tracking error by 37.99% and 44.65%, respectively, while reducing average lap time by at least 1.5%.
Comments8 pages, 8 figures, 5 tables
