GuSTO: Guaranteed Sequential Trajectory optimization via Sequential Convex Programming
GuSTO: Guaranteed Sequential Trajectory optimization via Sequential Convex Programming
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GuSTO：通过顺序凸规划保证顺序轨迹优化
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
M. Pavone
中科院分区：
文献类型：
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作者：
Riccardo Bonalli;A. Cauligi;Andrew Bylard;M. Pavone
序列凸规划(SCP)作为一种轨迹优化工具，近年来引起了人们的极大兴趣。然而，大多数可用的方法缺乏严格的性能保证，而且它们往往是针对特定的最优控制设置而定制的。本文提出了一种求解漂移控制仿射系统轨迹优化问题的算法框架--GUSTO(有保证的顺序轨迹优化)。GUSTO推广了早期基于SCP的轨迹优化方法(例如，通过解决目标集约束和最终时间固定或自由的问题)，并在收敛到至少一个固定点方面享有理论上的收敛保证。理论分析被进一步用来设计一种加速的Gusto实现，它最初将间接最优控制的想法注入到SCP上下文中。在各种轨迹优化设置上的数值实验表明，在成功率、解质量和计算时间方面，GUSTO总体上优于当前最先进的方法。
Sequential Convex Programming (SCP) has recently seen a surge of interest as a tool for trajectory optimization. However, most available methods lack rigorous performance guarantees and they are often tailored to specific optimal control setups. In this paper, we present GuSTO (Guaranteed Sequential Trajectory optimization), an algorithmic framework to solve trajectory optimization problems for control-affine systems with drift. GuSTO generalizes earlier SCP-based methods for trajectory optimization (by addressing, for example, goal-set constraints and problems with either fixed or free final time) and enjoys theoretical convergence guarantees in terms of convergence to, at least, a stationary point. The theoretical analysis is further leveraged to devise an accelerated implementation of GuSTO, which originally infuses ideas from indirect optimal control into an SCP context. Numerical experiments on a variety of trajectory optimization setups show that GuSTO generally outperforms current state-of-the-art approaches in terms of success rates, solution quality, and computation times.