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DeepIG: Multi-Robot Information Gathering with Deep Reinforcement Learning

Viseras, Alberto and Garcia, Ricardo (2019) DeepIG: Multi-Robot Information Gathering with Deep Reinforcement Learning. IEEE Robotics and Automation Letters. IEEE - Institute of Electrical and Electronics Engineers. ISSN 2377-3766

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Abstract

State-of-the-art multi-robot information gathering (MR-IG) algorithms often rely on a model that describes the structure of the information of interest to drive the robots motion. This causes MR-IG algorithms to fail when they are applied to new IG tasks, as existing models cannot describe the information of interest. Therefore, we propose in this paper a MR-IG algorithm that can be applied to new IG tasks with little algorithmic changes. To this end, we introduce DeepIG: a MR-IG algorithm that uses Deep Reinforcement Learning to allow robots to learn how to gather information. Nevertheless, there are IG tasks for which accurate models have been derived. Therefore, we extend DeepIG to exploit existing models for such IG tasks. This algorithm we term it modelbased DeepIG (MB-DeepIG). First, we evaluate DeepIG in simulations, and in an indoor experiment with three quadcopters that autonomously map an unknown terrain profile built in our lab. Results demonstrate that DeepIG can be applied to different IG tasks without algorithmic changes, and that it is robust to measurement noise. Then, we benchmark MB-DeepIG against state-of-the-art information-driven Gaussian-processesbased IG algorithms. Results demonstrate that MB-DeepIG outperforms the considered benchmarks.

Item URL in elib:https://elib.dlr.de/132180/
Document Type:Article
Title:DeepIG: Multi-Robot Information Gathering with Deep Reinforcement Learning
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Viseras, AlbertoAlberto.ViserasRuiz (at) dlr.deUNSPECIFIED
Garcia, Ricardoricardo.gpinel (at) alumnos.upm.esUNSPECIFIED
Date:June 2019
Journal or Publication Title:IEEE Robotics and Automation Letters
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:2377-3766
Status:Published
Keywords:robotics, deep learning, deep reinforcement learning, information gathering, multi-robot systems
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Communication and Navigation
DLR - Research area:Raumfahrt
DLR - Program:R KN - Kommunikation und Navigation
DLR - Research theme (Project):R - Vorhaben GNSS2/Neue Dienste und Produkte
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Communication and Navigation > Communications Systems
Deposited By: Viseras Ruiz, Alberto
Deposited On:10 Dec 2019 17:05
Last Modified:14 Dec 2019 04:23

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