Time-optimal motion planning of autonomous vehicles in complex environments is a highly researched topic. This paper describes a novel approach to optimize and execute locally feasible trajectories for the maneuvering of a truck-trailer Autonomous Mobile Robot (AMR), by dividing the environment in a sequence or route of freely accessible overlapping corridors. Multi-stage optimal control generates local trajectories through advancing subsets of this route. To cope with the advancing subsets and changing environments, the optimal control problem is solved online with a receding horizon in a Model Predictive Control (MPC) fashion with an improved update strategy. This strategy seamlessly integrates the computationally expensive MPC updates with a low-cost feedback controller for trajectory tracking, for disturbance rejection, and for stabilization of the unstable kinematics of the reversing truck-trailer AMR. This methodology is implemented in a flexible software framework for an effortless transition from offline simulations to deployment of experiments. An experimental setup showcasing the truck-trailer AMR performing two reverse parking maneuvers validates the presented method.

翻译：复杂环境下自主车辆的时间最优运动规划是一个研究热点。本文提出了一种新颖方法，通过将环境划分为一系列或一条由自由可通行重叠走廊组成的路径，来优化并执行卡车-拖车自主移动机器人（AMR） maneuvering 的局部可行轨迹。多阶段最优控制通过推进该路径的子集生成局部轨迹。为了应对推进子集和动态变化的环境，采用具有改进更新策略的模型预测控制（MPC）方式，以滚动时域在线求解最优控制问题。该策略将计算密集的MPC更新与低成本反馈控制器无缝集成，用于轨迹跟踪、扰动抑制以及倒车行驶中具有不稳定运动学的卡车-拖车AMR的稳定性控制。该方法在一个灵活软件框架中实现，可轻松从离线仿真过渡到实验部署。通过展示卡车-拖车AMR执行两种倒车泊车 maneuvers 的实验装置，验证了所提出方法的有效性。