@inproceedings{5abd2eb4ba6a48bdb3ab0dfe40e2a502,
title = "Energy-conserving risk-aware data collection using ensemble navigation network",
abstract = "The Data-collection Problem (DCP) models robotic agents collecting digital data in a risky environment under energy constraints. A good solution for DCP needs a balance between safety and energy use. We develop an Ensemble Navigation Network (ENN) that consists of a Convolutional Neural Network and several heuristics to learn the priorities. Experiments show ENN has superior performance than heuristic algorithms in all environmental settings. In particular, ENN has better performance in environments with higher risks and when robots have low energy capacity.",
keywords = "Deep reinforcement learning, Ensemble methods",
author = "Zhi Xing and Oh, {Jae C.}",
note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG, part of Springer Nature.; 31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems IEA/AIE 2018 ; Conference date: 25-06-2018 Through 28-06-2018",
year = "2018",
doi = "10.1007/978-3-319-92058-0_59",
language = "English (US)",
isbn = "9783319920573",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "613--625",
editor = "{Ait Mohamed}, Otmane and Malek Mouhoub and Samira Sadaoui and Moonis Ali",
booktitle = "Recent Trends and Future Technology in Applied Intelligence - 31st International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2018, Proceedings",
}