无需解析模型，实时优化机器人约束问题。
An Efficient Numerical Function Optimization Framework for Constrained Nonlinear Robotic Problems
- 结合梯度线搜索与约束空间投影，处理复杂约束。
- 支持在线轨迹与控制优化，适用于真实场景。
- 开源C++实现，适合机器人研发人员使用。
本文提出一种面向机器人约束非线性优化问题的数值优化框架，专为实时计算设计，适用于在线轨迹与控制输入优化。该工具无需问题的解析表达式，可直接处理带有约束的黑箱函数优化。方法融合一阶梯度线搜索与通过零空间投影到约束雅可比空间的约束优先级机制，有效处理多约束条件。框架以C++实现，并公开提供给社区使用。论文中还展示了若干数值实验与机器人应用实例，验证了其在实际系统中的有效性与实用性。
原文摘要 · Abstract (English)
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.
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