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 and Positioning Systems.

Self-Organising Swarms of Firefighting UAVs

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


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. 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
  2. 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
  3. 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
  4. 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.
  5. 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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