Autonomous Vehicles & Artificial Intelligence Laboratory (AVAILab)

Multidisciplinary laboratory focused on developments and applications within the fields of Mathematical Modelling, Optimisation, Soft & Natural Computing, Self-Organisation & Swarm Robotics, Autonomous Navigation, Computer Vision, and Positioning Systems.

Self-Organising Swarms of Firefighting UAVs

Lead ResearcherDr Mauro S. Innocente
Paolo Grasso
Mohammad Tavakol Sadrabadi
Ioannis Papagiannis
Dr Evangelos Gkanas
Prof Guillermo Rein (and HazeLab)

Figure created by PhD student Mohammad Tavakol Sadrabadi


Fire Propagation Modelling (FireProM)

Self-Organisation for Wildfire Suppression

Multi-Agent Collision Avoidance

Efficiency Enhancement of Class-A Foams with Nanoparticles to Fight Wildfires

Derived Publications

  1. M. Tavakol Sadrabadi, & M.S. Innocente (2023). Vegetation Cover Type Classification Using Cartographic Data for Prediction of Wildfire Behaviour. Fire, 6, no. 2: 76, MDPI.
    DOI: 10.3390/fire6020076.
  2. M. Tavakol Sadrabadi, M.S. Innocente, E.I. Gkanas, & I. Papagiannis (2022). Comparison of the effect of one-way and two-way fire-wind coupling on the modelling of wildland fire propagation dynamics. In: Viegas, D.X., Ribeiro, L.M. (eds.) Advances in Forest Fire Research 2022 (115–121). Imprensa da Universidade de Coimbra.
    DOI: 10.14195/978-989-26-2298-9_18.
  3. I. PapagiannisM.S. Innocente, & E.I. Gkanas (2022). Synthesis and Characterisation of Iron Oxide Nanoparticles with Tunable Sizes by Hydrothermal Method. In Materials Science Forum, 1053, 176–181, Trans Tech Publications Ltd.
    DOI: 10.4028/p-0so8ha
  4. P. Grasso, & M.S. Innocente (2022). Stigmergy-based collision-avoidance algorithm for autonomous firefighting drone swarms. Accepted for publication in: Proceedings of fifth International Conference on Computational Vision and Bio Inspired Computing. Advances in Intelligent Systems and Computing, Springer-Nature.
    DOI: 10.1007/978-981-16-9573-5_19
  5. P. Grasso, & M.S. Innocente (2020). Physics-based model of wildfire propagation towards faster-than-real-time simulations. Computers and Mathematics with Applications, 80, 790–808, Elsevier.
    DOI: 10.1016/j.camwa.2020.05.009
  6. M.S. Innocente, & P. Grasso (2019). Self-organising swarms of firefighting drones: Harnessing the power of collective intelligence in decentralised multi-robot systems. Journal of Computational Science, 34, 80–101, Elsevier.
    DOI: 10.1016/j.jocs.2019.04.009
  7. P. Grasso, & M.S. Innocente (2018). A two-dimensional reaction-advection-diffusion model of the spread of fire in wildlands. In Advances in Forest Fire Research 2018 (pp. 334–342). Imprensa da Universidade de Coimbra.
    DOI: 10.14195/978-989-26-16-506_36
  8. M.S. Innocente, & P. Grasso (2018). Swarm of autonomous drones self-organised to fight the spread of wildfires. In Proceedings of the GEOSAFE Workshop on Robust Solutions for Fire Fighting (Vol. 2146), L’Aquila, Italy, 2018. CEUR.
  9. M.S. Innocente, & P. Grasso (2018). Proof-of-Concept Swarm of Self-Organising Drones Aimed at Fighting Wildfires. In Proceedings of the 2017 UK-RAS Conference: ‘Robots Working for and among Us’, pp. 102–105, Bristol, UK, 2017.
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