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                    基于微分博弈的追逃問題最優策略設計

                    劉坤 鄭曉帥 林業茗 韓樂 夏元清

                    劉坤,  鄭曉帥,  林業茗,  韓樂,  夏元清.  基于微分博弈的追逃問題最優策略設計.  自動化學報,  2021,  47(8): 1840?1854 doi: 10.16383/j.aas.c200979
                    引用本文: 劉坤,  鄭曉帥,  林業茗,  韓樂,  夏元清.  基于微分博弈的追逃問題最優策略設計.  自動化學報,  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問題最優策略設計

                    doi: 10.16383/j.aas.c200979
                    基金項目: 國家自然科學基金(61873034), 北京自然科學基金(4182057), 北京市智能物流系統協同創新中心開放課題(BILSCIC-2019KF-13)資助
                    詳細信息
                      作者簡介:

                      劉坤:北京理工大學自動化學院研究員. 主要研究方向為網絡化控制理論與應用, 復雜網絡安全. E-mail: kunliubit@bit.edu.cn

                      鄭曉帥:北京理工大學自動化學院碩士研究生. 主要研究方向為對抗學習, 追逃博弈. E-mail: xiaoshuaizheng@bit.edu.cn

                      林業茗:北京理工大學自動化學院博士研究生. 主要研究方向為分布式優化, 分布式學習. 本文通信作者. E-mail: yeminglin@bit.edu.cn

                      韓樂:北京理工大學自動化學院碩士研究生. 主要研究方向為機器學習, 微分博弈, 追逃博弈, 分布式學習, 編隊控制, 魯棒控制, 多智能體系統協同控制與決策. E-mail: lehan@bit.edu.cn

                      夏元清:北京理工大學自動化學院教授. 主要研究方向為云控制, 云數據中心優化調度管理, 智能交通, 模型預測控制, 自抗擾控制, 魯棒控制, 復雜網絡控制與安全, 網絡化控制理論與應用, 飛行器控制和空天地一體化網絡協同控制. 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:

                      本文設計了基于線性二次型微分博弈的多個攻擊者、多個防御者和單個目標的追逃問題最優策略. 首先, 針對攻防雙方保持聚合狀態的情形, 基于攻擊方內部、防御方內部以及雙方之間的通信拓撲, 分別給出了目標沿固定軌跡運動和目標采取逃跑時攻防雙方的最優策略. 其次, 針對攻防雙方保持分散狀態的情形, 利用二分圖最大匹配算法分配相應的防御者與攻擊者, 將多攻擊者、多防御者追逃問題轉化為多組兩人零和微分博弈, 并求解出了攻防雙方的最優策略. 最后, 數值仿真驗證了所提策略的有效性.

                    • 圖  1  防御者通信拓撲

                      Fig.  1  The communication topology of defendes

                      圖  3  防御者與攻擊者之間的通信拓撲

                      Fig.  3  The communication topology between defendes and attackers

                      圖  2  攻擊者通信拓撲

                      Fig.  2  The communication topology of attackers

                      圖  4  攻擊者勝利時目標、攻擊者、防御者的運動軌跡

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

                      圖  5  防御者勝利時權重系數調整目標、攻擊者、防御者的運動軌跡

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

                      圖  6  防御者勝利時權重系數調整目標、攻擊者、防御者的成本函數

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

                      圖  7  $ m = 3$, $ l = 5$時目標、攻擊者、防御者的運動軌跡

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

                      圖  8  $m = 5,\;l = 3$時目標、攻擊者、防御者的運動軌跡

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

                      圖  9  目標采取逃跑行動時目標、攻擊者、防御者的運動軌跡

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

                      圖  10  防御者、攻擊者分散狀態下攻擊者、防御者的運動軌跡

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

                      360彩票
                    • [1] 杜永浩, 邢立寧, 蔡昭權. 無人飛行器集群智能調度技術綜述. 自動化學報, 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, 王小剛, 單永志, 趙亞麗, 崔乃剛. 基于改進粒子群算法的飛行器協同軌跡規劃. 自動化學報, 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. 云控制系統及其面臨的挑戰. 自動化學報, 2016, 42(01): 1-12.

                      Xia Yuan-Qing. Cloud control systems and their challenges. Acta Automatica Sinica, 2016, 42(1): 1-12.
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                    出版歷程
                    • 收稿日期:  2020-11-25
                    • 錄用日期:  2021-03-02
                    • 網絡出版日期:  2021-05-10
                    • 刊出日期:  2021-08-20

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