OpenSCvx：开源模块化可扩展非线性轨迹规划包

中文摘要 AI 辅助

轨迹优化可计算出动态可行的运动，使自主系统在满足操作和环境约束的同时完成复杂任务。本教程介绍OpenSCvx，这是一个开源Python框架，弥合了高层问题规范与高效数值优化之间的差距。OpenSCvx无需用户推导求解器特定的数学公式，而是提供符号建模接口，可从目标、动力学和约束的模块化描述自动构建并求解轨迹优化问题。除简化问题公式外，OpenSCvx还支持：（i）连续时间约束建模；（ii）时间和逻辑规范；（iii）用于可扩展批量优化的自动向量化；（iv）模块化架构，可轻松纳入新算法、模型和求解器后端。这些功能使研究人员和从业者能够快速原型化、求解和扩展最先进的轨迹优化方法。

英文摘要

Trajectory optimization computes dynamically feasible motions that enable autonomous systems to accomplish complex tasks while satisfying operational and environmental constraints. This tutorial presents OpenSCvx, an open-source Python framework that bridges the gap between high-level problem specification and efficient numerical optimization. Rather than requiring users to derive solver-specific mathematical formulations, OpenSCvx provides a symbolic modeling interface that automatically constructs and solves trajectory optimization problems from modular descriptions of objectives, dynamics, and constraints. Beyond simplifying problem formulation, OpenSCvx supports (i) continuous-time constraint modeling, (ii) temporal and logical specifications, (iii) automatic vectorization for scalable and batched optimization, and (iv) a modular architecture that enables new algorithms, models, and solver backends to be incorporated with minimal effort. These capabilities allow researchers and practitioners to rapidly prototype, solve, and extend state-of-the-art trajectory optimization methods.

发表机构

• University of Washington（华盛顿大学）
• General Robotics（通用机器人公司）
• University of California, Berkeley（加州大学伯克利分校）
• Zoox（Zoox公司）

机构由 AI 辅助整理，请以论文原文为准。