FIG-OP: Exploring Large-Scale Unknown Environments on a Fixed Time Budget
Oriana Peltzer, Amanda Bouman, Sung Kyun Kim, Ransalu Senanayake, Joshua Ott, Harrison Delecki, Mamoru Sobue, Mykel J. Kochenderfer, Mac Schwager, Joel W. Burdick, Ali–akbar Agha–mohammadi · 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · 2022
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).