Unmanned Aerial Vehicle (UAV) is increasingly becoming an important tool used for a variety of tasks. In addition, Reinforcement Learning (RL) is a popular research topic. In this paper, these two fields are combined together and we apply the reinforcement learning into the UAV field, promote the application of reinforcement learning in our real life. We design a reinforcement learning framework named ROS-RL, this framework is based on the physical simulation platform Gazebo and it can address the problem of UAV motion in continuous action space. We can connect our algorithms into this framework through ROS and train the agent to control the drone to complete some tasks. We realize the autonomous landing task of UAV using three different reinforcement learning algorithms in this framework. The experiment results show the effectiveness of algorithm in controlling UAV which flights in a simulation environment close to the real world.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Platform


    Contributors:


    Publication date :

    2022-12-01


    Size :

    556713 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Autonomous Quadrotor Landing using Deep Reinforcement Learning

    Polvara, Riccardo / Patacchiola, Massimiliano / Sharma, Sanjay et al. | ArXiv | 2017

    Free access

    IMAGE-BASED DEEP REINFORCEMENT LEARNING FOR AUTONOMOUS LUNAR LANDING

    Scorsoglio, Andrea / Furfaro, Roberto / Linares, Richard et al. | TIBKAT | 2020


    Deep Reinforcement Learning with Corrective Feedback for Autonomous UAV Landing on a Mobile Platform

    Lizhen Wu / Chang Wang / Pengpeng Zhang et al. | DOAJ | 2022

    Free access

    Image-based Deep Reinforcement Learning for Autonomous Lunar Landing

    Scorsoglio, Andrea / Furfaro, Roberto / Linares, Richard et al. | AIAA | 2020


    Image-Based Deep Reinforcement Meta-Learning for Autonomous Lunar Landing

    Scorsoglio, Andrea / D’Ambrosio, Andrea / Ghilardi, Luca et al. | AIAA | 2022