OpenSCvx: An Open-Source Modular and Extensible Nonlinear Trajectory Planning Package

2026-08-21

AI Summary

该论文介绍了一个名为OpenSCvx的开源Python框架，用于解决轨迹优化问题，通过提供符号建模接口自动生成并求解轨迹优化问题。

Abstract

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.

Problem

• trajectory optimization
• autonomous systems
• numerical optimization
• constraints

Innovation

• symbolic modeling interface
• continuous-time constraint modeling
• automatic vectorization
• modular architecture