Progressive Smoothing for Motion Planning in Real-Time NMPC
<|reference_start|>Progressive Smoothing for Motion Planning in Real-Time NMPC: 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.<|reference_end|>
@article{reiter2024progressive,
    title={Progressive Smoothing for Motion Planning in Real-Time NMPC},
    author={Rudolf Reiter, Katrin Baumg"artner, Rien Quirynen, Moritz Diehl},
    journal={arXiv preprint arXiv:2403.01830},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.01830},
    primaryClass={eess.SY cs.SY math.OC}
}