GuSTO: 逐次凸計画法による保証付き逐次軌道最適化
GuSTO: Guaranteed Sequential Trajectory Optimization via Sequential Convex Programming
制御アフィン系に対する軌道最適化を逐次凸計画法で解く枠組みGuSTOを提案し、少なくとも停留点への収束を理論的に保証するとともに、間接最適制御のアイデアを取り入れた高速実装で従来手法を上回る性能を示した。
著者: Riccardo Bonalli, Abhishek Cauligi, Andrew Bylard, Marco Pavone
分類: math.OC, cs.RO
原文アブストラクト
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.
