  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.
