Model Predictive Control for Frenet-Cartesian Trajectory Tracking of a Tricycle Kinematic Automated Guided Vehicle

Abstract

This work proposes an optimal control scheme for a trajectory-tracking Automated Guided Vehicle considering motion and collision constraints in a warehouse environment. We outline how the simpler obstacle avoidance constraints in the Cartesian Coordinate Frame (CCF) can be retained, while projecting the tricycle kinematics to the Frenet Coordinate Frame (FCF) for track progress. The Nonlinear Model Predictive Control (NMPC) scheme is subsequently implemented using acados and its real-time feasibility is demonstrated in simulation and aboard a test vehicle at a warehouse.