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Dynamic Plume Tracking Utilizing Symbiotic Heterogeneous Remote Sensing Platforms

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Artificial Intelligence Applications and Innovations (AIAI 2021)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 627))

Abstract

The current study focuses on the problem of continuously tracking a dynamically evolving \(CH_4\) plume utilizing a mutually built consensus by heterogeneous sensing platforms: mobile and static sensors. Identifying the major complexities and emergent dynamics (leakage source, intensity, time) of such problem, a distributed, multi-agent, optimization algorithm was developed and evaluated in an indoor continuous plume-tracking application (where reaction time is critical due to the limited volume available for air saturation by the \(CH_4\) dispersion). The high-fidelity ANSYS Fluent suite realistic simulation environment was used to acquire the gas diffusion evolution through time. The analysis of the simulation results indicated that the proposed algorithm was capable of continuously readapting the mobile sensing platforms formation according to the density and the dispersed volume plume; combining additive information from the static sensors. Moreover, a scalability analysis with respect to the number of mobile platforms revealed the flexibility of the proposed algorithm to different numbers of available assets.

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Acknowledgments

This research is carried out/funded in the context of the project “Development and evaluation of an optimal decision-making algorithm for cooperative autonomous vehicles” (MIS 5050057) under the call for proposals “Researchers’ support with an emphasis on young researchers- 2nd Cycle” (EDULLL 103). The project is co-financed by Greece and the European Union (European Social Fund- ESF) by the Operational Programme Human Resources Development, Education and Lifelong Learning 2014–2020.

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Authors and Affiliations

  1. Electrical and Computer Engineering Department (ECE) of the Democritus University of Thrace (D.U.TH.), Kimmeria Campus, 67100, Xanthi, Greece

    Iakovos T. Michailidis, Athanasios Ch. Kapoutsis, Elias B. Kosmatopoulos & Yiannis Boutalis

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  1. Iakovos T. Michailidis
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  2. Athanasios Ch. Kapoutsis
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  3. Elias B. Kosmatopoulos
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  4. Yiannis Boutalis
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Corresponding author

Correspondence to Iakovos T. Michailidis .

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Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  2. University of Sunderland, Sunderland, UK

    John Macintyre

  3. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

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Michailidis, I.T., Kapoutsis, A.C., Kosmatopoulos, E.B., Boutalis, Y. (2021). Dynamic Plume Tracking Utilizing Symbiotic Heterogeneous Remote Sensing Platforms. In: Maglogiannis, I., Macintyre, J., Iliadis, L. (eds) Artificial Intelligence Applications and Innovations. AIAI 2021. IFIP Advances in Information and Communication Technology, vol 627. Springer, Cham. https://doi.org/10.1007/978-3-030-79150-6_48

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  • DOI: https://doi.org/10.1007/978-3-030-79150-6_48

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-79150-6

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