Real-Time NMPC for an Automated Valet Parking with Load-Based Safety Constraints and a Path-Parametric Model

As global living standards continue to rise and urbanization accelerates, cities worldwide face high demands for parking. To tackle this issue and foster the efficient use of limited parking capacity, automated valet parking (AVP) solutions have emerged as a resource optimization and hassle-free alternative. This paper addresses the problem of reducing retrieval time in AVP systems while ensuring the integrity of both the robot and the car through critical adherence conditions. The key novelty of our approach is that we dovetail these conditions as acceleration constraints into a multi-stage motion generation framework formulated as a real-time nonlinear model predictive control (NMPC) scheme that approximates time-optimal behavior by maximizing progress on a path. The NMPC generates trajectories based on the motion direction, allowing for convenient parameterization of controller instances and unlimited parking maneuvers. Thanks to high-performance software implementations, the resulting quadratic subprograms can be solved in the order of milliseconds. Comparative analysis against the currently implemented pure pursuit algorithm and an experimental validation on Stanley Robotics' robot have shown a 31% time improvement following a path and constraint satisfaction in the loaded scenario for the proposed controller.