Dynamic Vehicle Drifting With Nonlinear MPC and a Fused Kinematic-Dynamic Bicycle Model
Dynamic Vehicle Drifting With Nonlinear MPC and a Fused Kinematic-Dynamic Bicycle Model
复制标题
使用非线性 MPC 和融合运动学-动态自行车模型的动态车辆漂移
DOI：
10.1109/lcsys.2021.3136142
复制
发表时间：
2022
影响因子：
3
通讯作者：
Quan Nguyen
中科院分区：
文献类型：
--
作者：
Guillaume Bellegarda;Quan Nguyen
在这封信中，我们提出了一个多功能轨迹优化框架，该框架利用融合的运动学动态自行车模型来实现高动态车辆漂移操作。我们的框架可以在线用于生成漂移操作，离线用于规划漂移停车，并且还可以在线跟踪离线计算的停车操作。重要的是，无论是单独的运动学还是动态自行车模型都不能直接在优化框架中直接使用来规划或执行所提出的运动，因为前者无法模拟漂移，而后者在低速时变得不明确。我们在 MIT RACECAR 的 Gazebo 模拟中验证了我们的框架，其中我们展示了几种漂移场景，例如具有一系列所需偏航速率的稳态漂移以及噪声条件下的动态漂移停车操作，视频结果可以在 https://youtu.be/MF1_fS6CQQs 上找到。
In this letter we present a versatile trajectory optimization framework that leverages a fused kinematic-dynamic bicycle model for highly dynamic vehicle drifting maneuvers. Our framework can be used online to generate drifting maneuvers, offline to plan drift parking, and additionally enables online tracking of the offline computed parking maneuvers. Importantly, neither individual kinematic nor dynamic bicycle models alone can be used straightforwardly in an optimization framework to plan nor execute the presented motions, as the former cannot model drifting, and the latter becomes ill-defined at low speeds. We validate our framework in a Gazebo simulation of the MIT RACECAR, where we show several drifting scenarios such as steady-state drifting with a range of desired yaw rates as well as a dynamic drift parking maneuver under noisy conditions, and video results can be found at https://youtu.be/MF1_fS6CQQs.