/Online Decision Making for Stream-based Robotic Sampling via Submodular Optimization

Online Decision Making for Stream-based Robotic Sampling via Submodular Optimization

Wenhao Luo, Changjoo Nam and Katia Sycara
Conference Paper, IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), November, 2017

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

We consider the problem of online robotic sampling in environmental monitoring tasks where the goal is to collect $k$ best samples from $n$ sequentially occurring measurements. In contrast of many existing works that seek to maximize the utility of the selected samples online, we aim to find the cardinality constrained subset of streaming measurements under irrevocable sampling decisions so that the prediction over untested measurement is most accurate. With the information theoretic criterion, we present an online submodular algorithm for stream-based sample selection with a provable performance bound. We demonstrate the effectiveness of our algorithm via simulations of information gathering from indoor static sensors.

BibTeX Reference
@conference{Luo and Nam and Sycara-2017-107472,
author = {Wenhao Luo and Changjoo Nam and Katia Sycara},
title = {Online Decision Making for Stream-based Robotic Sampling via Submodular Optimization},
booktitle = {IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)},
year = {2017},
month = {November},
}
2018-08-28T14:04:42-04:00