This paper looks into the problem of precise autonomous landing of an Unmanned Aerial Vehicle (UAV) which is considered to be a difficult problem as one has to generate appropriate landing trajectories in presence of dynamic constraints, such as, sudden changes in wind velocities and directions, downwash effects, change in payload etc. The problem is further compounded due to uncertainties arising from inaccurate model information and noisy sensor readings. The problem is partially solved by proposing a Reinforcement Learning (RL) based controller that uses Least Square Policy Iteration (LSPI) to learn the optimal control policies required for generating these trajectories. The efficacy of the approach is demonstrated through both simulation and real-world experiments with actual Parrot AR drone 2.0. According to our study, this is the first time such experimental results have been presented using RL based controller for drone landing, making it a novel contribution in this field.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Reinforcement Learning Approach for Autonomous Control and Landing of a Quadrotor


    Beteiligte:
    Vankadari, Madhu Babu (Autor:in) / Das, Kaushik (Autor:in) / Shinde, Chinmay (Autor:in) / Kumar, Swagat (Autor:in)


    Erscheinungsdatum :

    01.06.2018


    Format / Umfang :

    2157949 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Autonomous Quadrotor Landing using Deep Reinforcement Learning

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

    Freier Zugriff

    A Deep Reinforcement Learning-Based Approach for Autonomous Quadrotor Tracking Control

    Deng, Lan / Wang, Jiaqing / Jiang, Shuang et al. | Springer Verlag | 2024


    A Reinforcement Learning Approach to Control of a Quadrotor Biplane Tailsitter for Adaptive Landing Maneuvers

    Kim, J. / McIntosh, K. / Shrestha, E. et al. | British Library Conference Proceedings | 2023


    Autonomous Landing of a Quadrotor on an UGV

    Fu, Mengyin / Zhang, Kuan / Yi, Yang et al. | British Library Conference Proceedings | 2016


    Visual Environment Assessment for Safe Autonomous Quadrotor Landing

    Secchiero, Mattia / Bobbili, Nishanth / Zhou, Yang et al. | IEEE | 2024