We present a unified framework for path-parametric planning and control. This formulation is universal as it standardizes the entire spectrum of path-parametric techniques -- from traditional path following to more recent contouring or progress-maximizing Model Predictive Control and Reinforcement Learning -- under a single framework. The ingredients underlying this universality are twofold: First, we present a compact and efficient technique capable of computing singularity-free, smooth and differentiable moving frames. Second, we derive a spatial path parameterization of the Cartesian coordinates for any arbitrary curve without prior assumptions on its parametric speed or moving frame, and that perfectly interplays with the aforementioned path parameterization method. The combination of these two ingredients leads to a planning and control framework that unites existing path-parametric techniques in literature.
View on arXiv@article{arrizabalaga2025_2410.04664,
title={ A Universal Formulation for Path-Parametric Planning and Control },
author={ Jon Arrizabalaga and Zbyněk ŠÍR and Zachary Manchester and Markus Ryll },
journal={arXiv preprint arXiv:2410.04664},
year={ 2025 }
}