屋内環境における通行可能性変化のパターンを推定する長期ロボットナビゲーション
Long-Term Robot Navigation in Indoor Environments Estimating Patterns in Traversability Changes
ロボットの観測から通行可能性の変化パターンを逐次推定し、確率的グラフィカルモデルで未観測地点を予測して、リスクと情報獲得のトレードオフを考慮した経路計画を行う手法を提案した。
著者: Lorenzo Nardi, Cyrill Stachniss
分類: cs.RO
原文アブストラクト
Nowadays, mobile robots are deployed in many indoor environments, such as offices or hospitals. These environments are subject to changes in the traversability that often happen by following repeating patterns. In this paper, we investigate the problem of navigating in such environments over extended periods of time by capturing these patterns and exploiting this knowledge to make informed decisions. Our approach incrementally estimates a model of the traversability changes from robot's observations and uses a probabilistic graphical model to make predictions at currently unobserved locations. In the belief space defined by the predictions, we plan paths that trade off the risk to encounter obstacles and the information gain of visiting unknown locations. We implemented our approach and tested it in different indoor environments. The experiments suggest that in the long run, our approach leads to navigation along shorter paths compared to following a greedy shortest path policy.
