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

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 envir...

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Bibliographic Details
Main Authors: 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
Format: Conference Proceeding
Language:English
Subjects:
Distance measurement
Intelligent robots
Planning
Public transportation
Reliability
Time factors
Uncertainty
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Summary: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).
ISSN:2153-0866
DOI:10.1109/IROS47612.2022.9981271