In recent years, enhancing intelligence in the development of Unmanned Aerial Vehicles (UAVs) with a decrease in cost for the application of swarm fleets has attracted a variety of interest. Specifically, for urban applications such as public transportation, logistics mobilization, rescue operations, etc., there is a need for a fleet of UAVs to plan and coordinate intelligently without human intervention. A multi-agent reinforcement learning framework that can learn and make policies for such systems is desperately needed. This paper proposes an AI-based Bio-inspired Decentralized Multi-Agent Reinforcement Learning (B-DMARL) framework as a multi-agent actor-critic model for executing an assigned job in an increasingly dynamic environment. The B-DMARL is a distributed control architecture with two levels. For group coordination and collision avoidance, low-level control is developed using an AI-based bio-inspired steering behavior algorithm. Using a Proximal Policy Optimization (PPO) based reinforcement learning method, the agent is trained as a high-level control to correctly execute tasks in more dynamic environments. In a virtual simulation environment, the proposed B-DMARL framework is applied to persistent surveillance tasks that require the cooperation and collaboration of UAVs. Simulation results demonstrate that the proposed methods have an improved learning rate and reward signal.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Deep Multi Agent Reinforcement Learning Based Decentralized Swarm UAV Control Framework for Persistent Surveillance


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Lee, Sangchul (Herausgeber:in) / Han, Cheolheui (Herausgeber:in) / Choi, Jeong-Yeol (Herausgeber:in) / Kim, Seungkeun (Herausgeber:in) / Kim, Jeong Ho (Herausgeber:in) / Kaliappan, Vishnu Kumar (Autor:in) / Nguyen, Tuan Anh (Autor:in) / Jeon, Sang Woo (Autor:in) / Lee, Jae-Woo (Autor:in) / Min, Dugki (Autor:in)

    Kongress:

    Asia-Pacific International Symposium on Aerospace Technology ; 2021 ; Korea (Republic of) November 15, 2021 - November 17, 2021



    Erscheinungsdatum :

    30.09.2022


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    A Multi-agent Deep Reinforcement Learning Framework for UAV Swarm

    Zeng, Fanyu / Yang, Haigen / Zhao, Qian et al. | Springer Verlag | 2024


    Multi-Agent Deep Reinforcement Learning for Decentralized Cooperative Traffic Signal Control

    Zhao, Yang / Hu, Jian-Ming / Gao, Ming-Yang et al. | ASCE | 2020




    Supporting UAVs Swarm Missions by Multi-Agent Reinforcement Learning

    Fusco, P. / Porcelli, L. / Palmieri, F. et al. | IEEE | 2025