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Model-based gas source localization strategy for a cooperative multi-robot system-A probabilistic approach and experimental validation incorporating physical knowledge and model uncertainties
German Aerospace Center, Oberpfaffenhofen, Germany.
German Aerospace Center, Oberpfaffenhofen, Germany.
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0003-0217-9326
2019 (English)In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 118, p. 66-79Article in journal (Refereed) Published
Abstract [en]

Sampling gas distributions by robotic platforms in order to find gas sources is an appealing approach to alleviate threats for a human operator. Different sampling strategies for robotic gas exploration exist. In this paper we investigate the benefit that could be obtained by incorporating physical knowledge about the gas dispersion. By exploring a gas diffusion process using a multi-robot system. The physical behavior of the diffusion process is modeled using a Partial Differential Equation (PDE) which is integrated into the exploration strategy. It is assumed that the diffusion process is driven by only a few spatial sources at unknown locations with unknown intensity. The objective of the exploration strategy is to guide the robots to informative measurement locations and by means of concentration measurements estimate the source parameters, in particular, their number, locations and magnitudes. To this end we propose a probabilistic approach towards PDE identification under sparsity constraints using factor graphs and a message passing algorithm. Moreover, message passing schemes permit efficient distributed implementation of the algorithm, which makes it suitable for a multi-robot system. We designed an experimental setup that allows us to evaluate the performance of the exploration strategy in hardware-in-the-loop experiments as well as in experiments with real ethanol gas under laboratory conditions. The results indicate that the proposed exploration approach accelerates the identification of the source parameters and outperforms systematic sampling. (C) 2019 Elsevier B.V. All rights reserved.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 118, p. 66-79
Keywords [en]
Robotic exploration, Gas source localization, Multi-agent-system, Partial differential equation, Mobile robot olfaction, Sparse Bayesian learning, Factor graph, Message passing
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:oru:diva-75365DOI: 10.1016/j.robot.2019.03.014ISI: 000474324100006Scopus ID: 2-s2.0-85065544153OAI: oai:DiVA.org:oru-75365DiVA, id: diva2:1339325
Funder
EU, European Research Council, 645101Available from: 2019-07-29 Created: 2019-07-29 Last updated: 2019-07-29Bibliographically approved

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Lilienthal, Achim

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