Smarsly, KayKaySmarslyStührenberg, JanJanStührenbergHäusler, FelixFelixHäuslerNeumann, PatrickPatrickNeumannDragos, KosmasKosmasDragos2025-01-242025-01-242024-08International Conference on Computing in Civil and Building Engineering, ICCCBE 2024https://tore.tuhh.de/handle/11420/53546Gas source localization (GSL) is crucial for mitigating the impact of industrial accidents and natural disasters. 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 in an artificial environment. Upon detecting bouts, the agents emit pheromone markers, mimicking social insects. 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.enConference Paperhttps://www.xcdsystem.com/proceedings/isccbe/JRvh2J7/presentation/89129.cfm?uuid=E58ED4C0-E623-A7C8-8B823D2663B5ACDDConference Paper