<div class="csl-bib-body">
<div class="csl-entry">Sovukluk, S., & Ott, C. (2025). An Efficient Numerical Function Optimization Framework for Constrained Nonlinear Robotic Problems. In H. Choi (Ed.), <i>14th IFAC Symposium on Robotics ROBOTICS 2025 : Proceedings</i> (pp. 403–408). International Federation of Automatic Control (IFAC). https://doi.org/10.1016/j.ifacol.2025.10.254</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/223606
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dc.description.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 the end.
en
dc.description.sponsorship
Ministry of Trade, industry and Energy, South Korea