適応的重要度サンプリングによる軌道衝突確率評価と安全な動作計画
Evaluating Trajectory Collision Probability through Adaptive Importance Sampling for Safe Motion Planning
閉ループ制御下のロボット軌道が確率的衝突回避制約を満たすかを、適応的重要度サンプリングを用いたモンテカルロ法で評価し、信頼区間付きの精度保証を与える手法を提案した。
著者: Edward Schmerling, Marco Pavone
分類: cs.RO
原文アブストラクト
This paper presents a tool for addressing a key component in many algorithms for planning robot trajectories under uncertainty: evaluation of the safety of a robot whose actions are governed by a closed-loop feedback policy near a nominal planned trajectory. We describe an adaptive importance sampling Monte Carlo framework that enables the evaluation of a given control policy for satisfaction of a probabilistic collision avoidance constraint which also provides an associated certificate of accuracy (in the form of a confidence interval). In particular this adaptive technique is well-suited to addressing the complexities of rigid-body collision checking applied to non-linear robot dynamics. As a Monte Carlo method it is amenable to parallelization for computational tractability, and is generally applicable to a wide gamut of simulatable systems, including alternative noise models. Numerical experiments demonstrating the effectiveness of the adaptive importance sampling procedure are presented and discussed.
