Path planning using handcrafted waypoints is inefficient for a multi-task UAV operating in dynamic environments with potential risks such as bad weather, obstacles, or forbidden zones, among others. In this paper, we propose an automatic path planning method through natural language that instructs the UAV with compound commands about the tasks and the corresponding regions in a given map. First, we analyze the characteristics of the tasks and we model each task with a parameterized zone. Then, we use deep neural networks to segment the natural language commands into a sequence of labeled words, from which the semantics are extracted to select the waypoints and trajectory patterns accordingly. Finally, paths between the waypoints are generated using rapidly exploring random trees (RRT) or Dubins curves based on the task requirements. We demonstrate the effectiveness of the proposed method using a simulated quadrotor UAV that follows sequential commands in four typical tasks with potential risks.


    Access

    Download


    Export, share and cite



    Title :

    UAV Path Planning in Multi-Task Environments with Risks through Natural Language Understanding


    Contributors:
    Chang Wang (author) / Zhiwei Zhong (author) / Xiaojia Xiang (author) / Yi Zhu (author) / Lizhen Wu (author) / Dong Yin (author) / Jie Li (author)


    Publication date :

    2023




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Evolutionary Approaches to Path Planning Through Uncertain Environments

    Rathbun, D. / Capozzi, B. / American Institute of Aeronautics and Astronautics | British Library Conference Proceedings | 2002



    Multi UAV coverage path planning in urban environments

    Muñoz Mendi, Javier / Lopez Palomino, Blanca / Quevedo Vallejo, Fernando et al. | BASE | 2021

    Free access