Abstract Obstacle avoidance and path planning of unmanned aerial vehicles (UAVs) is an essential and challenging task, especially in the unknown environment with dynamic obstacles. To address this problem, a method of UAV path planning based on Deep Q-Learning is proposed. The experience replay mechanism is introduced in the deep reinforcement learning (DRL) process, and a value network is established to calculate the optimal value for the action of the UAV. The optimal flight policy of the UAV is determined through the $$\epsilon $$ -greed algorithm. The experimental results show that the UAV with well-trained model can avoid the obstacles in motion perfectly, and the cruise time is reduced by half compared with the untrained UAV.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Unmanned Aerial Vehicles Path Planning Based on Deep Reinforcement Learning


    Contributors:


    Publication date :

    2019-11-07


    Size :

    8 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Cooperative path planning of unmanned aerial vehicles

    Tsourdos, Antonios / White, Brian / Shanmugavel, Madhavan | TIBKAT | 2011


    Cooperative path planning of unmanned aerial vehicles

    Tsourdos, Antonios / White, Brian / Shanmugavel, Madhavan | TIBKAT | 2011


    Cooperative path planning of unmanned aerial vehicles

    Tsourdos, Antonios ;White, Brian ;Shanmugavel, Madhavan | SLUB | 2011



    Deep Reinforcement Learning for Mapless Navigation of Unmanned Aerial Vehicles

    Grando, Ricardo B. / de Jesus, Junior C. / Drews-Jr, Paulo L. J. | IEEE | 2020