论文标题
非线性MPC本地拖车拖车的本地规划师，以前和向后操纵
A Non-linear MPC Local Planner for Tractor-Trailer Vehicles in Forward and Backward Maneuvering
论文作者
论文摘要
在自动驾驶系统的研究界，设计本地规划师以在前和落后操作中控制拖拉机拖车车辆是一个具有挑战性的控制问题。考虑到拖拉机拖车系统稳定性的危急情况，提出了一种实用而新颖的方法，用于设计非线性MPC（NMPC）本地规划师，用于拖拉机拖车自动驾驶汽车，以往前和向后进行操作。拖拉机速度和转向角被认为是控制变量。拟议的NMPC本地规划师旨在处理折刀情况，避免多个静态障碍，并在前进和向后操作中跟随路径。上述挑战将转换为一个受约束的问题，该问题可以由拟议的NMPC本地规划师同时处理。直接的多个拍摄方法用于将最佳控制问题（OCP）转换为IPOPT求解器可以在Casadi中解决的非线性编程问题（NLP）。通过实时的凉亭仿真环境中的不同备份和向前操作的方案来评估控制器性能。它在避免静态障碍和准确的跟踪性能的同时，在尊重路径约束时实现了渐近稳定性。最后，拟议的NMPC本地规划师与一个名为Autowareai的开源自动驾驶软件堆栈集成在一起。
Designing a local planner to control tractor-trailer vehicles in forward and backward maneuvering is a challenging control problem in the research community of autonomous driving systems. Considering a critical situation in the stability of tractor-trailer systems, a practical and novel approach is presented to design a non-linear MPC(NMPC) local planner for tractor-trailer autonomous vehicles in both forward and backward maneuvering. The tractor velocity and steering angle are considered to be control variables. The proposed NMPC local planner is designed to handle jackknife situations, avoiding multiple static obstacles, and path following in both forward and backward maneuvering. The challenges mentioned above are converted into a constrained problem that can be handled simultaneously by the proposed NMPC local planner. The direct multiple shooting approach is used to convert the optimal control problem(OCP) into a non-linear programming problem(NLP) that IPOPT solvers can solve in CasADi. The controller performance is evaluated through different backup and forward maneuvering scenarios in the Gazebo simulation environment in real-time. It achieves asymptotic stability in avoiding static obstacles and accurate tracking performance while respecting path constraints. Finally, the proposed NMPC local planner is integrated with an open-source autonomous driving software stack called AutowareAi.
