Cooperative path planning for adversarial target is a challenging problem in competitive application scenarios such as military combats and sport games. In these scenarios, the goal and obstacle are dynamically moving to react against the path planning decisions just made, which makes conventional path planning methods not efficiency as before. The cooperative path planning of multi-robots is also a difficulty in this case. In this paper, a cooperative path planning method for adversarial target is proposed. The neural network is introduced to adaptively adjust the potential gain coefficients of the artificial potential field depends on the relative situation of adversarial target and cooperative partner. The neural network model is trained offline by approximately optimal samples. The method is evaluated in the 2 on 1 beyond-visual-range air combat path planning, which shows that our method significantly improves the win ratio by more than 30% comparing to the classic method.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Cooperative Path Planning for Adversarial Target based on Neural Network and Artificial Potential Field*


    Contributors:
    Zhang, Jing (author) / He, You (author) / Peng, Yingning (author) / Li, Gang (author)


    Publication date :

    2018-08-01


    Size :

    222624 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Cooperative Path Planning Algorithm Based on Artificial Potential Field Method

    Liu, Yuecheng / Yu, Guanfeng / Tian, Zhenpo et al. | Springer Verlag | 2023


    Artificial Potential Field-Based Path Planning for Cluttered Environments

    Diab, Mosab / Mohammadkarimi, Mostafa / Rajan, Raj Thilak | IEEE | 2023



    UAV Path Planning Based on Improved Artificial Potential Field Method

    Wang, Hai / Wang, Lei / Gao, Xiaohua et al. | Springer Verlag | 2023


    Path Planning Algorithm for Multiple UAVs Based on Artificial Potential Field

    Ren, Chongde / Chen, Jinchao / Du, Chenglie et al. | IEEE | 2023