Distributed task assignment algorithm is the key algorithm to solve multi-agent task assignment problem. In this paper, heterogeneous UAVs cooperative search and combat task is taken as the background. First, a multi-UAV task assignment model is established by comprehensively considering the constraints of UAV load resources, task time window and task type. Aiming at the obstacle threat in the task scenario, the UAV motion model is considered to improve the mobility and obstacle avoidance ability of the UAV in the assignment model. Second, UAV maneuver trajectory and path in the Consensus-Based Bundle Algorithm (CBBA) are improved by integrating dynamic window algorithm (DWA). Finally, the proposed algorithm is verfied which can improve the ability of multi-UAV task assignment in complex environment through numerical simulation.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Heterogeneous Multi-UAV Mission Planning Considering Obstacle Avoidance and UAV Motion Model


    Contributors:
    Shang, Shu (author) / Qi, Guoqing (author) / Sheng, Andong (author) / Li, Yinya (author)


    Publication date :

    2023-09-15


    Size :

    2319434 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Collaborative Reconnaissance Mission Planning Considering Task Overlap and Obstacle Avoidance

    Tao, Cancan / Zha, Li / She, Wanqiang et al. | Springer Verlag | 2025


    A considering lane information and obstacle-avoidance motion planning approach

    Yun-xiao Shan / Bi-jun Li / Xiaomin Guo et al. | IEEE | 2014


    Bridge crane path planning method considering obstacle avoidance

    WANG XINWEI / LIU JIE / DONG XIANZHOU et al. | European Patent Office | 2020

    Free access

    Obstacle avoidance trajectory planning strategy considering network communication constraints

    Luan, Zhongkai / Zheng, Shuangquan / Zhou, Guan et al. | SAGE Publications | 2024