论文标题
在感知感知模型上，用于使用汽车样拖拉机的逆向一般的2拖车的预测路径遵循控制
On sensing-aware model predictive path-following control for a reversing general 2-trailer with a car-like tractor
论文作者
论文摘要
可靠的路径遵循控制器的设计是成功部署自动驾驶车辆的关键要素。由于车辆在向后运动中的结构不稳定的关节角度运动学以及类似汽车的拖拉机的弯曲限制，该控制器设计问题对于具有汽车样拖拉机的一般2拖车尤其具有挑战性，这可能会导致车辆段折叠并输入Jackknife状态。此外，已经提出了有限的视野的高级传感器来解决联合角度估计问题在线，该问题引入了有关哪些车辆状态可以可靠地估算的其他限制。为了将这些限制纳入控制级别，提出了模型预测途径跟随控制器。通过考虑车辆的物理和感应局限性，在现实世界实验中表明，与先前提出的解决方案相比，在抑制干扰和从非平凡初始状态中恢复的拟议路径跟随控制器的性能得到了显着改善。
The design of reliable path-following controllers is a key ingredient for successful deployment of self-driving vehicles. This controller-design problem is especially challenging for a general 2-trailer with a car-like tractor due to the vehicle's structurally unstable joint-angle kinematics in backward motion and the car-like tractor's curvature limitations which can cause the vehicle segments to fold and enter a jackknife state. Furthermore, advanced sensors with a limited field of view have been proposed to solve the joint-angle estimation problem online, which introduce additional restrictions on which vehicle states that can be reliably estimated. To incorporate these restrictions at the level of control, a model predictive path-following controller is proposed. By taking the vehicle's physical and sensing limitations into account, it is shown in real-world experiments that the performance of the proposed path-following controller in terms of suppressing disturbances and recovering from non-trivial initial states is significantly improved compared to a previously proposed solution where the constraints have been neglected.
