Multi-Agent-Based Swarm Gas Source Localization Using Nano Aerial Robots

  • Gas source localization (GSL) is crucial for mitigating the impact of industrial accidents and natural disasters, for example finding leaks in oil and gas facilities or survivors in collapsed environments. Traditional GSL methods involving human intervention may be hazardous and time-consuming. Utilizing swarms of agile and cost-effective nano aerial robots holds the potential to enhance the safety and efficiency of GSL operations. This study draws inspiration from biological swarms, particularly colonies of social insects, to coordinate and optimize the performance of nano aerial robotic swarms. While most existing swarm GSL strategies assume gas concentration maxima to be in close proximity to actual gas sources, recent research has highlighted the importance of “bouts” as a more precise indicator of gas source proximity, considering the intermittency of gas distributions. In this paper, a swarm GSL strategy is introduced that incorporates bouts as indicators of source proximity,Gas source localization (GSL) is crucial for mitigating the impact of industrial accidents and natural disasters, for example finding leaks in oil and gas facilities or survivors in collapsed environments. Traditional GSL methods involving human intervention may be hazardous and time-consuming. Utilizing swarms of agile and cost-effective nano aerial robots holds the potential to enhance the safety and efficiency of GSL operations. This study draws inspiration from biological swarms, particularly colonies of social insects, to coordinate and optimize the performance of nano aerial robotic swarms. While most existing swarm GSL strategies assume gas concentration maxima to be in close proximity to actual gas sources, recent research has highlighted the importance of “bouts” as a more precise indicator of gas source proximity, considering the intermittency of gas distributions. In this paper, a swarm GSL strategy is introduced that incorporates bouts as indicators of source proximity, complemented by a bio-inspired pheromone communication system. Specifically, nano aerial robots are deployed as autonomous agents. Upon detecting bouts, the agents emit pheromone markers in an artificial environment, mimicking social insects. Using the concept of artificial potential fields, the agents either exploit the search space by following pheromone gradients or explore the search space. The proposed swarm GSL strategy is implemented and validated in a real-world experiment, conducted in an indoor environment with a single gas source. The experimental results demonstrate the capability of the swarm GSL strategy to perform effectively in indoor environments and that the intermittency of gas distributions is a better source proximity indicator than the mean concentration. It is concluded that this research may provide a methodological basis for improving gas source localization techniques and enhancing disaster response capabilities.zeige mehrzeige weniger

Volltext Dateien herunterladen

  • smarsly2024b.pdf
    eng

Metadaten exportieren

Weitere Dienste

Suche bei Google Scholar Anzahl der Zugriffe auf dieses Dokument
Metadaten
Autor*innen:Jan Stührenberg, Felix S. Häusler, Patrick P. NeumannORCiD, Kosmas Dragos, Kay Smarsly
Persönliche Herausgeber*innen:A. Francis, E. Miresco, S. Melhado
Dokumenttyp:Beitrag zu einem Tagungsband
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Advances in Information Technology in Civil and Building Engineering: Proceedings of ICCCBE 2024, Volume 2, Simulation and Automation
Jahr der Erstveröffentlichung:2025
Organisationseinheit der BAM:8 Zerstörungsfreie Prüfung
8 Zerstörungsfreie Prüfung / 8.1 Sensorik, mess- und prüftechnische Verfahren
Verlag:Springer Nature Switzerland
Verlagsort:Cham
Jahrgang/Band:2025
Erste Seite:337
Letzte Seite:346
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Sanitär- und Kommunaltechnik; Umwelttechnik
Freie Schlagwörter:Bouts; Gas source localization; Mobile robotic olfaction; Nano aerial robots; Swarm robotics
Themenfelder/Aktivitätsfelder der BAM:Umwelt
Veranstaltung:ICCCBE 2024
Veranstaltungsort:Montreal, Canada
Beginndatum der Veranstaltung:25.08.2024
Enddatum der Veranstaltung:28.08.2024
DOI:10.1007/978-3-031-87364-5_28
ISSN:2366-2557
ISBN:978-3-031-87363-8
ISBN:978-3-031-87364-5
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:18.06.2025
Referierte Publikation:Ja
Datum der Eintragung als referierte Publikation:23.06.2025
Einverstanden
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.