OpenSCvx让轨迹规划更简单，一键生成复杂运动路径。
OpenSCvx: An Open-Source Modular and Extensible Nonlinear Trajectory Planning Package
- 用模块化符号接口自动构建优化问题，无需手动推导公式。
- 支持连续时间约束、逻辑时序描述和批量并行计算。
- 开源可扩展，适合机器人、自动驾驶等领域的快速开发。
轨迹优化能计算出满足动力学和环境约束的可行运动路径，使自主系统完成复杂任务。本文介绍 OpenSCvx，一个开源的 Python 框架，弥合了高层问题描述与高效数值优化之间的差距。用户无需推导针对特定求解器的数学形式，OpenSCvx 提供符号化建模接口，可从目标、动力学和约束的模块化描述中自动生成并求解轨迹优化问题。除简化问题建模外，OpenSCvx 还支持（i）连续时间约束建模，（ii）时间与逻辑规范，（iii）自动向量化以实现可扩展的批量优化，（iv）模块化架构，使新算法、模型和求解器后端可轻松集成。这些能力使研究人员和从业者能够快速原型设计、求解并扩展前沿轨迹优化方法。
原文摘要 · Abstract (English)
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.
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