Convex Optimization for Trajectory Generation
Convex Optimization for Trajectory Generation
复制标题
轨迹生成的凸优化
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Behçet Açikmese
中科院分区：
文献类型：
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作者：
Danylo Malyuta;Taylor P. Reynolds;Michael Szmuk;T. Lew;Riccardo Bonalli;M. Pavone;Behçet Açikmese
可靠且高效的轨迹生成方法是未来自主动力系统的基本需求。本文的目标是提供三种主要的基于凸优化的轨迹生成方法的综合教程：无损凸化 (LCvx) 和两种称为 SCvx 和 GuSTO 的顺序凸编程算法。在本文中，轨迹生成是对动态可行状态和控制信号的计算，该状态和控制信号在优化关键任务目标的同时满足一组约束。轨迹生成问题几乎总是非凸的，这通常意味着自动驾驶车辆上不容易找到高效可靠的解决方案。我们讨论的三种算法使用问题重构和系统算法策略，通过使用凸优化器来解决非凸轨迹生成任务。凸优化提供的理论保证和计算速度使得该算法在研究界和工业界都很受欢迎。迄今为止，应用列表包括火箭着陆、航天器高超音速再入、航天器交会对接、固定翼和四旋翼飞行器的空中运动规划、机器人运动规划等。这些应用包括由 NASA、Masten Space Systems、SpaceX 和 Blue Origin 等组织进行的备受瞩目的火箭飞行。本文旨在为读者提供使用每种算法所需的工具和理解，并了解每种方法可以做什么和不能做什么。公开可用的源代码存储库支持所提供的数值示例。在本文结束时，读者应该准备好使用这些方法、扩展它们并为其许多令人兴奋的现代应用做出贡献。
Reliable and efficient trajectory generation methods are a fundamental need for autonomous dynamical systems of tomorrow. The goal of this article is to provide a comprehensive tutorial of three major convex optimization-based trajectory generation methods: lossless convexification (LCvx), and two sequential convex programming algorithms known as SCvx and GuSTO. In this article, trajectory generation is the computation of a dynamically feasible state and control signal that satisfies a set of constraints while optimizing key mission objectives. The trajectory generation problem is almost always nonconvex, which typically means that it is not readily amenable to efficient and reliable solution onboard an autonomous vehicle. The three algorithms that we discuss use problem reformulation and a systematic algorithmic strategy to nonetheless solve nonconvex trajectory generation tasks through the use of a convex optimizer. The theoretical guarantees and computational speed offered by convex optimization have made the algorithms popular in both research and industry circles. To date, the list of applications includes rocket landing, spacecraft hypersonic reentry, spacecraft rendezvous and docking, aerial motion planning for fixed-wing and quadrotor vehicles, robot motion planning, and more. Among these applications are high-profile rocket flights conducted by organizations like NASA, Masten Space Systems, SpaceX, and Blue Origin. This article aims to give the reader the tools and understanding necessary to work with each algorithm, and to know what each method can and cannot do. A publicly available source code repository supports the provided numerical examples. By the end of the article, the reader should be ready to use the methods, to extend them, and to contribute to their many exciting modern applications.