Energy-conserving risk-aware data collection using ensemble navigation network

Zhi Xing, Jae C Oh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationRecent Trends and Future Technology in Applied Intelligence - 31st International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2018, Proceedings
PublisherSpringer Verlag
Pages613-625
Number of pages13
ISBN (Print)9783319920573
DOIs
StatePublished - Jan 1 2018
Event31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems IEA/AIE 2018 - Montreal, Canada
Duration: Jun 25 2018Jun 28 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10868 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems IEA/AIE 2018
CountryCanada
CityMontreal
Period6/25/186/28/18

Keywords

  • Deep reinforcement learning
  • Ensemble methods

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Xing, Z., & Oh, J. C. (2018). Energy-conserving risk-aware data collection using ensemble navigation network. In 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 (pp. 613-625). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10868 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-92058-0_59