An Efficient Numerical Function Optimization Framework for Constrained Nonlinear Robotic Problems
Abstract
This paper presents a numerical function optimization framework designed for constrained optimization problems in robotics. The tool is designed with real-time considerations and is suitable for online trajectory and control input optimization problems. The proposed framework does not require any analytical representation of the problem and works with constrained block-box optimization functions. The method combines first-order gradient-based line search algorithms with constraint prioritization through nullspace projections onto constraint Jacobian space. The tool is implemented in C++ and provided online for community use, along with some numerical and robotic example implementations presented in this paper.
Cite
@article{arxiv.2501.17349,
title = {An Efficient Numerical Function Optimization Framework for Constrained Nonlinear Robotic Problems},
author = {Sait Sovukluk and Christian Ott},
journal= {arXiv preprint arXiv:2501.17349},
year = {2025}
}
Comments
\c{opyright} 2025 the authors. This work has been accepted to IFAC for publication under a Creative Commons Licence CC-BY-NC-ND. - Implementation: https://github.com/ssovukluk/ENFORCpp