作用素分割による安全な確率非線形制御のための共分散ステアリング
Operator Splitting Covariance Steering for Safe Stochastic Nonlinear Control
非線形動力学と確率制約を伴う分布ステアリング問題に対し、問題を並列に解ける部分問題へ一時的に分割する作用素分割型共分散ステアリングを提案し、厳しい安全制約下でも従来法より良い解が得られることを示した。
著者: Akash Ratheesh, Vincent Pacelli, Augustinos D. Saravanos, Evangelos A. Theodorou
分類: cs.RO, math.OC
原文アブストラクト
This paper presents a novel algorithm for solving distribution steering problems featuring nonlinear dynamics and chance constraints. Covariance steering (CS) is an emerging methodology in stochastic optimal control that poses constraints on the first two moments of the state distribution -- thereby being more tractable than full distributional control. Nevertheless, a significant limitation of current approaches for solving nonlinear CS problems, such as sequential convex programming (SCP), is that they often generate infeasible or poor results due to the large number of constraints. In this paper, we address these challenges, by proposing an operator splitting CS approach that temporarily decouples the full problem into subproblems that can be solved in parallel. This relaxation does not require intermediate iterates to satisfy all constraints simultaneously prior to convergence, which enhances exploration and improves feasibility in such non-convex settings. Simulation results across a variety of robotics applications verify the ability of the proposed method to find better solutions even under stricter safety constraints than standard SCP. Finally, the applicability of our framework on real systems is also confirmed through hardware demonstrations
