The purpose of this study is to develop a design for maximum area drone coverage in a post-disaster flood situation. When it comes to covering a disaster-region for monitoring and detection of the extent of damage and losses, a suitable and technically balanced approach is vital to achieving the best solution while covering the maximum affected area. Therefore, a mathematical optimisation model is proposed to effectively capture maximum images of the impacted region. The particle swarm optimisation (PSO) algorithm is used to solve the optimisation problem. Modern relief missions heavily rely on drones, specifically in the case of flooding, to capture the damage due to the disaster and to create roadmaps to help impacted people. This system has convincing results for inertia, exploration, exploitation, velocity, and determining the height of the drones to enhance the response to a disaster. The proposed approach indicates that when maintaining the flight height of the drone above 120 m, the coverage can be enhanced by approximately 34% compared with a flight height of 100 m.


    Access

    Download


    Export, share and cite



    Title :

    Disaster Region Coverage Using Drones: Maximum Area Coverage and Minimum Resource Utilisation


    Contributors:


    Publication date :

    2022




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Disaster Area Coverage Optimisation Using Reinforcement Learning

    Gruffeille, Ciaran / Perrusquia, Adolfo / Tsourdos, Antonios et al. | IEEE | 2024



    Multi-Camera Networks for Coverage Control of Drones

    Sunan Huang / Rodney Swee Huat Teo / William Wai Lun Leong | DOAJ | 2022

    Free access

    Robust Maximum Coverage Facility Location Problem with Drones Considering Uncertainties in Battery Availability and Consumption

    Chauhan, Darshan R. / Unnikrishnan, Avinash / Figliozzi, Miguel et al. | Transportation Research Record | 2020


    DISASTER EVACUATION USING SWARM DRONES

    KIM WON TAE / LEE SEUNGGI / KIM KWAN HYUCK et al. | European Patent Office | 2021

    Free access