サンプル平均近似によるリスク回避型軌道最適化
Risk-Averse Trajectory Optimization via Sample Average Approximation
計画期間全体にわたる平均バリュー・アット・リスク制約を導入し、サンプルベースの近似で効率的かつ汎用的なリスク回避型軌道最適化アルゴリズムを提案した。
著者: Thomas Lew, Riccardo Bonalli, Marco Pavone
分類: cs.RO, cs.SY, eess.SY, math.OC
原文アブストラクト
Trajectory optimization under uncertainty underpins a wide range of applications in robotics. However, existing methods are limited in terms of reasoning about sources of epistemic and aleatoric uncertainty, space and time correlations, nonlinear dynamics, and non-convex constraints. In this work, we first introduce a continuous-time planning formulation with an average-value-at-risk constraint over the entire planning horizon. Then, we propose a sample-based approximation that unlocks an efficient and general-purpose algorithm for risk-averse trajectory optimization. We prove that the method is asymptotically optimal and derive finite-sample error bounds. Simulations demonstrate the high speed and reliability of the approach on problems with stochasticity in nonlinear dynamics, obstacle fields, interactions, and terrain parameters.
