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Experimental Validation of Entropy Driven Swarm Exploration under Sparsity Constraints with Sparse Bayesian Learning

Manss, Christoph and Kuehner, Isabel and Shutin, Dmitriy (2022) Experimental Validation of Entropy Driven Swarm Exploration under Sparsity Constraints with Sparse Bayesian Learning. Entropy, pp. 1-22. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/e24050580. ISSN 1099-4300.

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Official URL: https://www.mdpi.com/1099-4300/24/5/580

Abstract

Increasing the autonomy of multi-agent systems or swarms for exploration missions requires tools for efficient information gathering. This work studies this problem from theoretical and experimental perspectives and evaluates an exploration system for multiple ground robots that cooperatively explore a stationary spatial process. For the distributed model, two conceptually different distribution paradigms are considered. The exploration is based on fusing distributively gathered information using Sparse Bayesian Learning (SBL), which permits representing the spatial process in a compressed manner and thus reduces the model complexity and communication load required for the exploration. An entropy-based exploration criterion is formulated to guide the agents. This criterion uses an estimation of a covariance matrix of the model parameters, which is then quantitatively characterized using a D-optimality criterion. The new sampling locations for the agents are then selected to minimize this criterion. To this end, a distributed optimization of the D-optimality criterion is derived. The proposed entropy-driven exploration is then presented from a system perspective and validated in laboratory experiments with two ground robots. The experiments show that SBL together with the distributed entropy-driven exploration is real-time capable and leads to a better performance with respect to time and accuracy compared with similar state-of-the-art algorithms.

Item URL in elib:https://elib.dlr.de/186164/
Document Type:Article
Title:Experimental Validation of Entropy Driven Swarm Exploration under Sparsity Constraints with Sparse Bayesian Learning
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Manss, ChristophUNSPECIFIEDhttps://orcid.org/0000-0003-4851-2622UNSPECIFIED
Kuehner, IsabelUNSPECIFIEDhttps://orcid.org/0000-0002-8472-3286UNSPECIFIED
Shutin, DmitriyUNSPECIFIEDhttps://orcid.org/0000-0002-6065-6453UNSPECIFIED
Date:20 April 2022
Journal or Publication Title:Entropy
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI:10.3390/e24050580
Page Range:pp. 1-22
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Series Name:MDPI
ISSN:1099-4300
Status:Published
Keywords:Swarm exploration, multi-agent systems, entropy-driven exploration, optimal experiment design
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Communication, Navigation, Quantum Technology
DLR - Research area:Raumfahrt
DLR - Program:R KNQ - Communication, Navigation, Quantum Technology
DLR - Research theme (Project):R - Swarm navigation
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Communication and Navigation > Communications Systems
Deposited By: Shutin, Dmitriy
Deposited On:22 Apr 2022 14:54
Last Modified:22 Apr 2022 14:54

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