Nonlinear model predictive control (NMPC) is a popular strategy for solving motion planning problems, including obstacle avoidance constraints, in autonomous driving applications. Non-smooth obstacle shapes, such as rectangles, introduce additional local minima in the underlying optimization problem. Smooth over-approximations, e.g., ellipsoidal shapes, limit the performance due to their conservativeness. We propose to vary the smoothness and the related over-approximation by a homotopy. Instead of varying the smoothness in consecutive sequential quadratic programming iterations, we use formulations that decrease the smooth over-approximation from the end towards the beginning of the prediction horizon. Thus, the real-time iterations algorithm is applicable to the proposed NMPC formulation. Different formulations are compared in simulation experiments and shown to successfully improve performance indicators without increasing the computation time.
@article{arxiv.2403.01830,
title = {Progressive Smoothing for Motion Planning in Real-Time NMPC},
author = {Rudolf Reiter and Katrin Baumgärtner and Rien Quirynen and Moritz Diehl},
journal= {arXiv preprint arXiv:2403.01830},
year = {2024}
}