Dedicated to meeting the growing demand for multi-agent collaboration in complex scenarios, this paper introduces a parameter-sharing off-policy multi-agent path planning and the following approach. Current multi-agent path planning predominantly relies on grid-based maps, whereas our proposed approach utilizes laser scan data as input, providing a closer simulation of real-world applications. In this approach, the unmanned aerial vehicle (UAV) uses the soft actor–critic (SAC) algorithm as a planner and trains its policy to converge. This policy enables end-to-end processing of laser scan data, guiding the UAV to avoid obstacles and reach the goal. At the same time, the planner incorporates paths generated by a sampling-based method as following points. The following points are continuously updated as the UAV progresses. Multi-UAV path planning tasks are facilitated, and policy convergence is accelerated through sharing experiences among agents. To address the challenge of UAVs that are initially stationary and overly cautious near the goal, a reward function is designed to encourage UAV movement. Additionally, a multi-UAV simulation environment is established to simulate real-world UAV scenarios to support training and validation of the proposed approach. The simulation results highlight the effectiveness of the presented approach in both the training process and task performance. The presented algorithm achieves an 80% success rate to guarantee that three UAVs reach the goal points.


    Access

    Download


    Export, share and cite



    Title :

    Multi-UAV Path Planning and Following Based on Multi-Agent Reinforcement Learning


    Contributors:
    Xiaoru Zhao (author) / Rennong Yang (author) / Liangsheng Zhong (author) / Zhiwei Hou (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Deep Reinforcement Learning for Image-Based Multi-Agent Coverage Path Planning

    Xu, Meng / She, Yechao / Jin, Yang et al. | IEEE | 2023




    Flight path planning method based on multi-agent reinforcement learning in opposite support scene

    QU CHONGXIAO / JIN HUIQUAN / JIAO WENMING et al. | European Patent Office | 2022

    Free access