2.793

2018影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于微分博弈的追逃问题最优策略设计

刘坤 郑晓帅 林业茗 韩乐 夏元清

刘坤,  郑晓帅,  林业茗,  韩乐,  夏元清.  基于微分博弈的追逃问题最优策略设计.  自动化学报,  2021,  47(8): 1840−1854 doi: 10.16383/j.aas.c200979
引用本文: 刘坤,  郑晓帅,  林业茗,  韩乐,  夏元清.  基于微分博弈的追逃问题最优策略设计.  自动化学报,  2021,  47(8): 1840−1854 doi: 10.16383/j.aas.c200979
Liu Kun,  Zheng Xiao-Shuai,  Lin Ye-Ming,  Han Le,  Xia Yuan-Qing.  Design of optimal strategies for the pursuit-evasion problem based on differential game.  Acta Automatica Sinica,  2021,  47(8): 1840−1854 doi: 10.16383/j.aas.c200979
Citation: Liu Kun,  Zheng Xiao-Shuai,  Lin Ye-Ming,  Han Le,  Xia Yuan-Qing.  Design of optimal strategies for the pursuit-evasion problem based on differential game.  Acta Automatica Sinica,  2021,  47(8): 1840−1854 doi: 10.16383/j.aas.c200979

基于微分博弈的追逃问题最优策略设计

doi: 10.16383/j.aas.c200979
基金项目: 国家自然科学基金(61873034), 北京自然科学基金(4182057), 北京市智能物流系统协同创新中心开放课题(BILSCIC-2019KF-13)资助
详细信息
    作者简介:

    刘坤:北京理工大学自动化学院研究员. 主要研究方向为网络化控制理论与应用, 复杂网络安全. E-mail: kunliubit@bit.edu.cn

    郑晓帅:北京理工大学自动化学院硕士研究生. 主要研究方向为对抗学习, 追逃博弈. E-mail: xiaoshuaizheng@bit.edu.cn

    林业茗:北京理工大学自动化学院博士研究生. 主要研究方向为分布式优化, 分布式学习. 本文通信作者. E-mail: yeminglin@bit.edu.cn

    韩乐:北京理工大学自动化学院硕士研究生. 主要研究方向为机器学习, 微分博弈, 追逃博弈, 分布式学习, 编队控制, 鲁棒控制, 多智能体系统协同控制与决策. E-mail: lehan@bit.edu.cn

    夏元清:北京理工大学自动化学院教授. 主要研究方向为云控制, 云数据中心优化调度管理, 智能交通, 模型预测控制, 自抗扰控制, 鲁棒控制, 复杂网络控制与安全, 网络化控制理论与应用, 飞行器控制和空天地一体化网络协同控制. E-mail: xia_yuanqing@bit.edu.cn

Design of Optimal Strategies for the Pursuit-evasion Problem Based on Differential Game

Funds: Supported by National Natural Science Foundation of China (61873034), Beijing Natural Science Foundation (4182057), and the Open Subject of Beijing Intelligent Logistics System Collaborative Innovation Center (BILSCIC-2019KF-13)
More Information
    Author Bio:

    LIU Kun Professor at the School of Automation, Beijing Institute of Technology. His research interest covers theory and applications of networked control, and security of complex networked systems

    ZHENG Xiao-Shuai Master student at the School of Automation, Beijing Institute of Technology. His research interest covers adversarial learning and pursuit-evasion problem

    LIN Ye-Ming Ph. D. candidate at the School of Automation, Beijing Institute of Technology. His research interest covers distributed optimization and distributed learning. Corresponding author of this paper

    HAN Le Master student at the School of Automation, Beijing Institute of Technology. Her research interest covers machine learning, differential game, pursuit-evasion problem, distributed learning, formation control problem, robust control, cooperative control and decision of multi-agent system

    XIA Yuan-Qing Professor at the School of Automation, Beijing Institute of Technology. His research interest covers cloud control, cloud data center optimization scheduling and management, intelligent transportation, model predictive control, active disturbance rejection control, robust control, control and security of complex networked systems, theory and applications of networked control, flight control and networked cooperative control for integration of space, air and earth

  • 摘要:

    本文设计了基于线性二次型微分博弈的多个攻击者、多个防御者和单个目标的追逃问题最优策略. 首先, 针对攻防双方保持聚合状态的情形, 基于攻击方内部、防御方内部以及双方之间的通信拓扑, 分别给出了目标沿固定轨迹运动和目标采取逃跑时攻防双方的最优策略. 其次, 针对攻防双方保持分散状态的情形, 利用二分图最大匹配算法分配相应的防御者与攻击者, 将多攻击者、多防御者追逃问题转化为多组两人零和微分博弈, 并求解出了攻防双方的最优策略. 最后, 数值仿真验证了所提策略的有效性.

  • 图  1  防御者通信拓扑

    Fig.  1  The communication topology of defendes

    图  3  防御者与攻击者之间的通信拓扑

    Fig.  3  The communication topology between defendes and attackers

    图  2  攻击者通信拓扑

    Fig.  2  The communication topology of attackers

    图  4  攻击者胜利时目标、攻击者、防御者的运动轨迹

    Fig.  4  Trajectories of the target, attackers and defenders when attackers win

    图  5  防御者胜利时权重系数调整目标、攻击者、防御者的运动轨迹

    Fig.  5  Trajectories of the target, attackers and defenders with different weight coefficients when defendes win

    图  6  防御者胜利时权重系数调整目标、攻击者、防御者的成本函数

    Fig.  6  Cost functions of the target, attackers and defenders with different weight coefficients when defendes win

    图  7  $ m = 3$, $ l = 5$时目标、攻击者、防御者的运动轨迹

    Fig.  7  Trajectories of the target, attackers and defenders with $ m = 3$, $ l = 5$

    图  8  $m = 5,\;l = 3$时目标、攻击者、防御者的运动轨迹

    Fig.  8  Trajectories of the target, attackers and defenders with $m = 5,\;l = 3$

    图  9  目标采取逃跑行动时目标、攻击者、防御者的运动轨迹

    Fig.  9  Trajectories of the target, attackers and defenders when the target adopts an escape strategy

    图  10  防御者、攻击者分散状态下攻击者、防御者的运动轨迹

    Fig.  10  Trajectories of attackers and defenders when defenders and attackers stay distributed

  • [1] 杜永浩, 邢立宁, 蔡昭权. 无人飞行器集群智能调度技术综述. 自动化学报, 2020, 46(2): 222-241.

    DU Yong-Hao, XING Li-Ning, CAI Zhao-Quan. Survey on intelligent scheduling technologies for unmanned flying craft clusters. Acta Automatica Sinica, 2020, 46(2): 222-241.
    [2] 周宏宇, 王小刚, 单永志, 赵亚丽, 崔乃刚. 基于改进粒子群算法的飞行器协同轨迹规划. 自动化学报, DOI: 10.16383/j.aas.c190865

    Zhou Hong-Yu, Wang Xiao-Gang, Shan Yong-Zhi, Zhao Ya-Li, Cui Nai-Gang. Synergistic path planning for multiple vehicles based on an improved particle swarm optimization method. Acta Automatica Sinica, DOI: 10.16383/j.aas.c190865
    [3] Azam M A, Ragi S. Decentralized formation shape control of UAV swarm using dynamic programming. In: Proceedings of Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX. California, USA, 2020. 11423: 114230I
    [4] Zhou Z, Zhang W, Ding J, Huang, H, Stipanovic D M, Tomlin C J. Cooperative pursuit with voronoi partitions. Automatica, 2016, 72: 64-72. doi: 10.1016/j.automatica.2016.05.007
    [5] De Simone D, Scianca N, Ferrari P, Lanari L, Oriolo G. MPC-based humanoid pursuit-evasion in the presence of obstacles. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. 2017. 5245−5250
    [6] Isaacs R. Differential Games: A Mathematical Theory With Applications to Warfare and Pursuit, Control and Optimization. Courier Corporation, 1999.
    [7] Fang B, Pan Q, Hong B, Lei D, Zhong Q B, Zhang Z. Research on high speed evader vs. multi lower speed pursuers in multi pursuit-evasion games. Information Technology Journal, 2012, 11(8): 989-997. doi: 10.3923/itj.2012.989.997
    [8] Lin W, Qu Z, Simaan M A. Nash strategies for pursuit-evasion differential games involving limited observations. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(2): 1347-1356. doi: 10.1109/TAES.2014.130569
    [9] Pachter M, Garcia E, Casbeer D W. Differential game of guarding a target. Journal of Guidance, Control, and Dynamics, 2017, 40(11): 2991-2998. doi: 10.2514/1.G002652
    [10] Venkatesan R H, Sinha N K. The target guarding problem revisited: Some interesting revelations. In: Proceedings of IFAC World Congress. Cape Town, South Africa, 2014. 1556−1561
    [11] Li D, Cruz J B. Defending an asset: A linear quadratic game approach. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(2): 1026-1044. doi: 10.1109/TAES.2011.5751240
    [12] Garcia E, Casbeer D W, Pachter M. Design and analysis of state-feedback optimal strategies for the differential game of active defense. IEEE Transactions on Automatic Control, 2018, 64(2): 553-568.
    [13] Liang L, Deng F, Peng Z, Li X, Zha W. A differential game for cooperative target defense. Automatica, 2019, 102: 58-71. doi: 10.1016/j.automatica.2018.12.034
    [14] Casbeer D W, Garcia E, Pachter M. The target differential game with two defenders. Journal of Intelligent & Robotic Systems, 2018, 89(1-2): 87-106.
    [15] Chen M, Zhou Z, Tomlin C J. Multiplayer reach-avoid games via low dimensional solutions and maximum matching. In: Proceedings of American Control Conference. Portland, USA, 2014. 1444−1449
    [16] Coon M, Panagou D. Control strategies for multiplayer target-attacker-defender differential games with double integrator dynamics. In: Proceedings of IEEE Conference on Decision and Control. Melbourne, Australia, 2017. 1496−1502
    [17] Chipade V S, Panagou D. Multiplayer target-attacker-defender differential game: pairing allocations and control strategies for guaranteed intercept. In: Proceedings of AIAA Scitech 2019 Forum. California, USA, 2019. 658−678
    [18] Yan R, Shi Z, Zhong Y. Task assignment for multiplayer reach-avoid games in convex domains via analytical barriers. IEEE Transactions on Robotics, 2019, 36(1): 107-124.
    [19] Garcia E, Casbeer D W, Von Moll A, Pachter M. Multiple Pursuer Multiple Evader Differential Games. IEEE Transactions on Automatic Control, arxiv: 1911. 03806
    [20] Sin E, Arcak M, Packard A, Philbrick D, Seiler P. Optimal assignment of collaborating agents in multi-body asset-guarding games. In: Proceedings of the 2020 American Control Conference (ACC). Denver, Colorado, USA, 2020. 858−864
    [21] Li D X, Cruz J B. Graph-Based Strategies for Multi-Player Pursuit Evasion Games. In: Proceedings of IEEE Conference on Decision and Control. New Orleans, LA, USA, 2007. 4063−4068
    [22] Mejia V G L, Lewis F L, Wan Y, Sanchez E N, Fan L. Solutions for multiagent pursuit-evasion games on communication graphs: Finite-time capture and asymptotic behaviors. IEEE Transactions on Automatic Control, 2019, 65(5): 1911-1923.
    [23] Engwerda J. LQ dynamic optimization and differential games. John Wiley & Sons, 2005.
    [24] Kuhn H. The Hungarian method for the assignment problem. Naval Research Logistics Quarterly, 1955, 2(1-2): 83-97. doi: 10.1002/nav.3800020109
    [25] Amato F, Pironti A. A note on singular zero-sum linear quadratic differential games. In: Proceedings of IEEE Conference on Decision and Control. Lake Buena Vista, USA, 1994. 1533−1535
    [26] 夏元清. 云控制系统及其面临的挑战. 自动化学报, 2016, 42(01): 1-12.

    Xia Yuan-Qing. Cloud control systems and their challenges. Acta Automatica Sinica, 2016, 42(1): 1-12.
  • 加载中
图(10)
计量
  • 文章访问数:  2087
  • HTML全文浏览量:  498
  • PDF下载量:  203
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-11-25
  • 录用日期:  2021-03-02
  • 网络出版日期:  2021-05-10
  • 刊出日期:  2021-08-20

目录

    /

    返回文章
    返回