Towards an autonomous wildfire suppression system based on swarms of self-organising drones

    Student thesis: Doctoral ThesisDoctor of Philosophy

    Abstract

    This research aims at contributing in the development of a firefighting technology, which uses swarms of self-organising autonomous drones. Wildfires are indeed one of the most threatening cataclysms induced by climate change that endangers wildlife and human lives, and which devastates health, social, economic and environment spheres. Hence, the urge to find a new solution to tackle more efficiently and effectively critical wildfires by either extinguishing or containing them. The frontier of autonomous swarm robotics seems to provide good chances to succeed in this venture thanks to many strong points, for instance: the capability to work in hazardous areas, the robustness of the system with respect to loss of a few drones, and no need of highly trained pilots. The research presented in this thesis spans between various fields such as thermo-fluid-dynamics, swarm robotics, and Swarm-in-the-Loop (SwiL) simulation. More specifically the main research topics are wildfire propagation modelling, autonomous multi-agent robotics for firefighting application, stigmergic collision-avoidance systems for autonomous flight, and study and improvement of indoor positioning systems (IPSs). While the first three research subjects are clearly interlaced with each other, the last topic regarding IPS consists of some relevant theoretical and experimental work towards the development of a SwiL platform to test the fire suppression system and new self-organisation algorithms. The main contributions of the presented research are: a faster-than-real-time physics based propagation model, namely FireProM-F; self-organisation algorithms for swarm of autonomous firefighting drones; a stigmergy-based collision-avoidance algorithm for autonomous swarm; a study of the precision, accuracy, and failure of UWB-based IPSs; and the development of a debiasing filter for the improvement of the IPS’s accuracy.
    Date of AwardMar 2022
    Original languageEnglish
    Awarding Institution
    • Coventry University
    SupervisorMauro Innocente (Supervisor)

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