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Energy-Conserving Risk-Aware Data Collection Using Ensemble Navigation Network

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10868)

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 

Notes

Acknowledgements

This research was supported in part through computational resources provided by Syracuse University and by NSF award ACI-1541396.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.EECS, College of Engineering and Computer ScienceSyracuse UniversitySyracuseUSA

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