Mollification for efficient curve smoothing with formal guarantees

Autores/as

  • Alfredo González-Calvin Universidad Complutense de Madrid
  • Juan F. Jiménez Universidad Complutense de Madrid

DOI:

https://doi.org/10.17979/ja-cea.2026.47.13652

Palabras clave:

Path Planning, Trajectory tracking, Autonomous Mobile Robots, Computational methods, Guidance navigation and control

Resumen

This work presents a new alternative to polynomial splines for trajectory and  path generation.  We present a method that, given a non-differentiable trajectory/path, generates  an infinitely differentiable one in a single step---requiring small computational resources---and can be made as close to the original as desired, both pointwise and on compact subsets of the trajectory's domain. These methods---\textit{mollification} and its improvement, \textit{directional mollification}---possess $O(n)$ numerical complexity, can inherit certain geometric properties from splines (such as confinement to the convex hull of the points defining a curve), and allow for simple analytical curvature bounding for curves described as polygonal chains. We demonstrate the feasibility of these methods through a set of experiments in which the smooth curve adapts to the vehicle's dynamics.

Referencias

Bay, T., Cattiaux-Huillard, I., Romani, L., Saini, L., 2023. On g1 and g2 hermite interpolation by spatial algebraic-trigonometric pythagorean hodograph curves with polynomial parametric speed. Applied Mathematics and Computation 458, 128240. URL: https://www.sciencedirect.com/science/article/pii/S0096300323004095 DOI: https://doi.org/10.1016/j.amc.2023.128240

Berglund, T., Brodnik, A., Jonsson, H., Staffanson, M., Söderkvist, I., 2010. Planning smooth and obstacle-avoiding B-spline paths for autonomous mining vehicles. IEEE Transactions on Automation Science and Engineering 7 (1), 167–172.

Evans, L. C., 2022. Partial differential equations. Vol. 19. American mathematical society.

Faraway, J. J., Reed, M. P., Wang, J., 09 2007. Modelling Three-Dimensional Trajectories by Using B´ezier Curves with Application to Hand Motion. Journal of the Royal Statistical Society Series C: Applied Statistics 56 (5), 571–585. URL: https://doi.org/10.1111/j.1467-9876.2007.00592.x DOI: 10.1111/j.1467-9876.2007.00592.x

Farin, G. E., 2002. Curves and surfaces for CAGD: a practical guide. Morgan Kaufmann.

Farouki, R. T., Gentili, G., Giannelli, C., Sestini, A., Stoppato, C., 2017. A comprehensive characterization of the set of polynomial curves with rational rotation-minimizing frames. Advances in Computational Mathematics 43 (1), 1–24.

González-Calvin, A., Jiménez, J. F., de Marina, H. G., 2025. Efficient generation of smooth paths with curvature guarantees by mollification. URL: https://arxiv.org/abs/2512.13183

González-Calvin, A., Jiménez, J. F., de Marina, H. G., 2026. Directional mollification for knot-preserving c∞ smoothing of polygonal chains with explicit curvature bounds. URL: https://arxiv.org/abs/2603.21831

Hastie, T., Tibshirani, R., Friedman, J., 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition. Springer, New York.

Hattenberger, G., Bronz, M., Gorraz, M., 2014. Using the paparazzi uav system for scientific research. In: IMAV 2014, international micro air vehicle conference and competition 2014. pp. pp–247.

Hohage, T., Mar´echal, P., Simar, L., Vanhems, A., 2024. A mollifier approach to the deconvolution of probability densities. Econometric Theory 40 (2), 320–359.

Jahn, J., 2007. Introduction to the theory of nonlinear optimization. Springer.

Lau, B., Sprunk, C., Burgard, W., 2009. Kinodynamic motion planning for mobile robots using splines. In: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 2427–2433.

LaValle, S. M., 2006. Planning Algorithms. Cambridge University Press, Cambridge, UK. URL: http://planning.cs.uiuc.edu/

Meek, D. S., Walton, D. J., 1992. Approximation of discrete data by G1 arc splines. Computer-Aided Design 24 (6), 301–306.

Piegl, L., Tiller, W., 2012. The NURBS book. Springer Science & Business Media.

Rasmussen, C. E., Williams, C. K. I., 2006. Gaussian Processes for Machine Learning. MIT Press, Cambridge, MA.

Schumaker, L., 2007. Spline functions: basic theory. Cambridge university press.

Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G., 2009. Robotics: modelling, planning and control. Springer.

Yang, K., Sukkarieh, S., 2010. An analytical continuous-curvature pathsmoothing algorithm. IEEE Transactions on Robotics 26 (3), 561–568.

Yao, W., de Marina, H. G., Lin, B., Cao, M., 2021. Singularity-free guiding vector field for robot navigation. IEEE Transactions on Robotics 37 (4), 1206–1221. DOI: 10.1109/TRO.2020.3043690

Descargas

Publicado

01-09-2026

Número

Sección

Robótica