Drones are being increasingly used in delivery services, often in coordination with delivery trucks through networked communication links. When a drone loses network communication with the truck in dynamic environments with uncertainties (e.g., obstacles and traffic congestion), navigation adaptation and re-routing are necessary to ensure safety. In this paper, we present a novel Drone Trajectory Planning (DTP) model based on Q-Learning, designed to adapt drone delivery missions in the presence of intermittent network connectivity. Our model leverages state representations including the drone’s position, proximity to congestion zones, its general direction and truck/drone network status to adapt to uncertainties. We conduct simulations within a realistic grid environment to evaluate the performance of our DTP model. The results demonstrate DTP model’s robust performance in optimizing path decisions and ensuring timely deliveries, even when communication between the truck and drone is lost due to uncertainties, achieving approximately 23% better rewards compared to the state-of-the-art A* path-finding algorithm.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Q-Learning-Based Dynamic Drone Trajectory Planning in Uncertain Environments




    Publication date :

    2025-02-17


    Size :

    1936492 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Aircraft trajectory planning based on threat sources in uncertain environments

    Ji, Wanfeng / Li, Cheng / Zhang, Yaoqing | IEEE | 2024



    Robust trajectory planning for unmanned aerial vehicles in uncertain environments

    Luders, Brandon (Brandon Douglas) | DSpace@MIT | 2008

    Free access

    Grid-Based Stochastic Model Predictive Control for Trajectory Planning in Uncertain Environments

    Brudigam, Tim / Luzio, Fulvio Di / Pallottino, Lucia et al. | IEEE | 2020