MPC-based Motion Planning for Autonomous Truck-Trailer Maneuvering

TL;DR

This paper presents a novel MPC-based motion planning approach for autonomous truck-trailer maneuvering, combining multi-stage optimal control with real-time updates and a feedback controller, validated through experiments.

Contribution

It introduces a new MPC-based framework for local trajectory optimization and execution in complex environments for truck-trailer robots, integrating online control with disturbance rejection.

Findings

Successful reverse parking maneuvers demonstrated in experiments

Seamless integration of MPC updates with feedback control

Flexible software framework enabling offline and online deployment

Abstract

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.