Unmanned Aerial Vehicle (UAV) swarm, also named drone swarm, has been the study object of many types of research due to its potential to improve applications such as monitoring, surveillance, and search missions. With several drones flying simultaneously, the challenge is to increase their level of automation and intelligence while avoiding collision, reducing communication level with these entities, and improving strategical organization to accomplish a specific task. In this sense, we propose a solution to coordinate a UAV swarm using bivariate potential fields with autonomous and distributed intelligence among drones for a cooperative target search application. Results have shown an improvement in the swarm effectiveness by reducing the number of UAVs blocked at local minima by using distributed decision-making methods, proving to be an effective approach to solve this frequent problem in potential fields.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Autonomous and Collective Intelligence for UAV Swarm in Target Search Scenario




    Publication date :

    2021-10-11


    Size :

    4004789 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Autonomous Car With Swarm Intelligence

    Anand, Y / Ajithkumar, Rahul | IEEE | 2019


    WORST PERCEPTION SCENARIO SEARCH FOR AUTONOMOUS DRIVING

    Xu, Liheng / Zhang, Chi / Liu, Yuehu et al. | British Library Conference Proceedings | 2020


    Worst Perception Scenario Search for Autonomous Driving

    Xu, Liheng / Zhang, Chi / Liu, Yuehu et al. | IEEE | 2020


    Autonomous flight control method for UAVs using Swarm Intelligence

    RYOO CHANGKYUNG / KIMJEONGHUN / PARK KUK KWON et al. | European Patent Office | 2022

    Free access

    Highly Flexible Swarm Intelligence Algorithm for Resilient Autonomous Systems

    Clifford, J. / Towhidnejad, M. / Neighbors, J. et al. | IEEE | 2018