閉鎖空間の能動的検査のためのグラフ上での時間割引エルゴード性の急速収束
Rapidly Converging Time-Discounted Ergodicity on Graphs for Active Inspection of Confined Spaces
連続空間をグラフで離散化し、情報の多いノードへの早期訪問を重視する時間割引エルゴード性指標を導入。凸計画で合成したマルコフ連鎖が従来より速く収束し、SLAMベースのベイズ仮説検定と階層的枠組みで閉鎖空間の異常検出を高精度化。
著者: Benjamin Wong, Ryan H. Lee, Tyler M. Paine, Santosh Devasia, Ashis G. Banerjee
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Ergodic exploration has spawned a lot of interest in mobile robotics due to its ability to design time trajectories that match desired spatial coverage statistics. However, current ergodic approaches are for continuous spaces, which require detailed sensory information at each point and can lead to fractal-like trajectories that cannot be tracked easily. This paper presents a new ergodic approach for graph-based discretization of continuous spaces. It also introduces a new time-discounted ergodicity metric, wherein early visitations of information-rich nodes are weighted more than late visitations. A Markov chain synthesized using a convex program is shown to converge more rapidly to time-discounted ergodicity than the traditional fastest mixing Markov chain. The resultant ergodic traversal method is used within a hierarchical framework for active inspection of confined spaces with the goal of detecting anomalies robustly using SLAM-driven Bayesian hypothesis testing. Experiments on a ground robot show the advantages of this framework over three continuous space ergodic planners as well as greedy and random exploration methods for left-behind foreign object debris detection in a ballast tank.
