Details

Autor(en) / Beteiligte

• Peltzer, Oriana
• Bouman, Amanda
• Kim, Sung-Kyun
• Senanayake, Ransalu
• Ott, Joshua
• Delecki, Harrison
• Sobue, Mamoru
• Kochenderfer, Mykel J.
• Schwager, Mac
• Burdick, Joel
• Agha-mohammadi, Ali-akbar

Titel

FIG-OP: Exploring Large-Scale Unknown Environments on a Fixed Time Budget

Ist Teil von

• 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, p.8754-8761

Ort / Verlag

IEEE

Erscheinungsjahr

2022

Link zum Volltext

• IEEE_Xplore

Quelle

IEL

Beschreibungen/Notizen

• We present a method for autonomous exploration of large-scale unknown environments under mission time con-straints. 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 ad-dresses model uncertainty by frontloading expected information gain through the addition of a greedy incentive, effectively expe-diting the moment in which new area is uncovered. In order to reason across multi-kilometer 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).