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An Efficient Numerical Function Optimization Framework for Constrained Nonlinear Robotic Problems
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DOI：10.1016/j.ifacol.2025.10.254.png)
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Abstract
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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. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Keywords:
Nonlinear System Optimization
Constrained Robotic Problem Optimization