In recent years, the rapid advancements in Machine Learning have led to substantial research in control systems for autonomous aerial vehicles. Particularly, Reinforcement Learning (RL) has attracted a lot of attention for the design and development of such control algorithms. This paper examines the control issues of autonomous flight and how these are addressed using RL approaches. The objective is to investigate how RL algorithms like Deep Deterministic Policy Gradient may be applied particularly for control actions in an unmanned aerial vehicle (UAV). This learning paradigm acts as a mechanism that continuously generates policies for tasks such as attitude and position control, which converges into an optimized trajectory. As an outlook, the application of Continual Reinforcement Learning is proposed. This is a novel RL methodology that holds the potential to advance the control system of a UAV operating in dynamic, unknown environments with the ability to reapply learnt behavior and flexibly adapt to new situations.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep Reinforcement Learning for Trajectory Generation and Optimisation of UAVs


    Contributors:


    Publication date :

    2023-06-07


    Size :

    1070492 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





    Ground-sensitive trajectory generation for UAVs

    ROY NICK / TAKAYAMA LEILA / FLECK MATHIAS SAMUEL et al. | European Patent Office | 2016

    Free access

    A new deep reinforcement learning architecture for autonomous UAVs

    Muñoz Ferran, Guillem | BASE | 2018

    Free access

    Deep Reinforcement Learning Attitude Control of Fixed-Wing UAVs

    Zhen, Yan / Hao, Mingrui / Sun, Wendi | IEEE | 2020