位置推定不確実性を考慮した安全ナビゲーションのためのベイズ最適化
Bayesian Optimisation for Safe Navigation under Localisation Uncertainty
位置推定の不確実性をガウス過程の事前分布としてベイズ最適化に組み込み、不整地での安全な走行領域を学習する手法を提案し、実機実験で標準的なベイズ最適化と比較した。
著者: Rafael Oliveira, Lionel Ott, Vitor Guizilini, Fabio Ramos
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
In outdoor environments, mobile robots are required to navigate through terrain with varying characteristics, some of which might significantly affect the integrity of the platform. Ideally, the robot should be able to identify areas that are safe for navigation based on its own percepts about the environment while avoiding damage to itself. Bayesian optimisation (BO) has been successfully applied to the task of learning a model of terrain traversability while guiding the robot through more traversable areas. An issue, however, is that localisation uncertainty can end up guiding the robot to unsafe areas and distort the model being learnt. In this paper, we address this problem and present a novel method that allows BO to consider localisation uncertainty by applying a Gaussian process model for uncertain inputs as a prior. We evaluate the proposed method in simulation and in experiments with a real robot navigating over rough terrain and compare it against standard BO methods.
