FIG-OP：限られた時間で大規模未知環境を探索する手法
FIG-OP: Exploring Large-Scale Unknown Environments on a Fixed Time Budget
時間制約下で大規模未知環境を自律探索するため、情報利得を前倒しするFIG-OPを提案し、トポロジカル・メトリック地図を統合した表現で解く手法。地下鉄や鉱山で実証し、従来手法より探索効率が向上。
著者: Oriana Peltzer, Amanda Bouman, Sung-Kyun Kim, Ransalu Senanayake, Joshua Ott, Harrison Delecki, Mamoru Sobue, Mykel Kochenderfer, Mac Schwager, Joel Burdick, Ali-akbar Agha-mohammadi
分類: cs.RO
原文アブストラクト
We present a method for autonomous exploration of large-scale unknown environments under mission time constraints. We start by proposing the Frontloaded Information Gain Orienteering Problem (FIG-OP) -- a generalization of the traditional orienteering problem where the assumption of a reliable environmental model no longer holds. The FIG-OP addresses model uncertainty by frontloading expected information gain through the addition of a greedy incentive, effectively expediting the moment in which new area is uncovered. In order to reason across multi-kilometre environments, we solve FIG-OP over an information-efficient world representation, constructed through the aggregation of information from a topological and metric map. Our method was extensively tested and field-hardened across various complex environments, ranging from subway systems to mines. In comparative simulations, we observe that the FIG-OP solution exhibits improved coverage efficiency over solutions generated by greedy and traditional orienteering-based approaches (i.e. severe and minimal model uncertainty assumptions, respectively).
