Abstract
The use of Autonomous Systems to monitor pollution of TIC/TIM (Toxic Industrial Materials/Toxic Industrial Chemical) or to track a CBRN (Chemical, Biological, Radiological and Nuclear) crisis could help to quickly and effectively assess the risks, in addition the combination of these assets with Simulation and Artificial Intelligence further reinforces their effectiveness. This paper proposes an approach to apply their coordinated uses combining the Simulation results of diffusion models able to suggest where to go and what to measure with a Smart Control based on Intelligent Agents to direct their movements and actions. In this paper scenario are proposed different potential cases related to release of contaminants; the hypotheses move from an industrial Plants to hazardous material spill during transportations, while the specific case analyzed is related to Maritime Logistics. In this paper, it is introduced the scheme of the AI solution to be adopted in joint cooperation with simulation to control the drones devoted to assess the situation and improve reliable evaluation of crisis evolution.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fu, Z., Chen, Y., Ding, Y., He, D.: pollution source localization based on multi-UAV cooperative communication. IEEE Access 7, 29304–29312 (2019). https://doi.org/10.1109/ACCESS.2019.2900475
Zang, W., Lin, J., Wang, Y., Tao, H.: Investigating small-scale water pollution with UAV remote sensing technology. World Autom. Congr. 2012, 1–4 (2012)
Liu, T., Han, D., Lin, Y., Liu, K.: Distributed multi-UAV trajectory optimization over directed networks. J. Franklin Inst. 358(10), 5470–5487 (2021). ISSN 0016–0032
Villa, T.F., Salimi, F., Morton, K., Morawska, L., Gonzalez, F.: Development and validation of a UAV based system for air pollution measurements. Sensors 16, 2202 (2016). https://doi.org/10.3390/s16122202
Altshuler, Y., Yanovsky, V., Wagner, I.A., Bruckstein, A.M.: The cooper- ative hunters-efficient cooperative search for smart targets using UAV swarms. In: Second In- ternational Conference on Informatics in Control, Automation and Robotics (ICINCO), the First International Workshop on Multi-Agent Robotic Systems (MARS), pp. 165–170 (2005)
Kolling, A., Kleiner, A.: Multi-UAV Trajectory planning for guaranteed search. In: 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), pp. 79–86. The International Foundation for Autonomous Agents and Multiagent Sys- tems (IFAAMAS) (2013)
Stodola, Petr, Mazal, Jan: Tactical decision support system to aid commanders in their decision-making. In: Hodicky, J. (ed.) MESAS 2016. LNCS, vol. 9991, pp. 396–406. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47605-6_32
Kung, C.M., Yang, W.S., Wei, T.Y., Chao, S.T.:The fast flight trajectory verification algorithm for drone dance system. In: 2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), pp. 97–101 (2020). https://doi.org/10.1109/IAICT50021.2020
D'Ursol, F., Grasso, C., Santoro, C., Santoro, F.F., Schembra, G.: The tactile internet for the flight control of UAV flocks. In: 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft), pp. 470–475 (2018). https://doi.org/10.1109/NETSOFT.2018.8458493
Keramea, P., Spanoudaki, K., Zodiatis, G., Gikas, G., Sylaios, G.:Oil spill modeling: a critical review on current trends, perspectives, and challenges. J. Mar. Sci. Eng. 9(2), 181 (2021)
Gomez, F., Ribstein, B., Makké, L., Armand, P., Moussafir, J., Nibart, M.: Simulation of a dense gas chlorine release with a Lagrangian particle dispersion mod- el (LPDM). Atmos. Environ. 244, 117791 (2021). ISSN 1352-2310 9172016
Bruzzone, A.G., et al.: Innovative virtual lab for improving safety and port operations. In: Proc. of HMS, Rome (2022)
Bruzzone, A.G., Massei, M., Di Matteo, R., Kutej, L.: Introducing intelligence and autonomy into industrial robots to address operations into dangerous area. In: Mazal, J. (ed.) Modelling and Simulation for Autonomous Systems. MESAS 2018. Lecture Notes in Computer Science, vol 11472, pp. 433–444. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-14984-0_32
Bruzzone, A.G., Massei, M.: Simulation-based military training. In: Mittal, S., Durak, U., Ören, T. (eds.) Guide to Simulation-Based Disciplines, Simulation Foundations, Methods and Applications, pp. 315–361. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61264-5_14
Bruzzone, A.G., Massei, M., Sinelshchikov, K., Giovannetti, A., Gadupuri, B.K.: Strategic engineering applied to complex systems within marine environment. In: 2021 Annual Modeling and Simulation Conference, Proc. of ANNSIM IEEE, July, pp. 1–10 2021
Vairo, T., Quagliati, M., Del Giudice, T., Barbucci, A., Fabiano, B.: From land-to water-use-planning: a consequence based case-study related to cruise ship risk. Saf. Sci. 97, 120–133 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bruzzone, A.G. et al. (2023). Cooperative Use of Autonomous Systems to Monitor Toxic Industrial Materials and Face Accidents & Contamination Crises. In: Mazal, J., et al. Modelling and Simulation for Autonomous Systems. MESAS 2022. Lecture Notes in Computer Science, vol 13866. Springer, Cham. https://doi.org/10.1007/978-3-031-31268-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-31268-7_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-31267-0
Online ISBN: 978-3-031-31268-7
eBook Packages: Computer ScienceComputer Science (R0)