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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:


    Publication date :

    2018-06-01


    Size :

    2157949 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

    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