| Citation: | ZHANG Honghong, GAN Xusheng, XIN Jianlin, et al. Multi-aircraft conflict resolution algorithm based on cooperative game[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(5): 863-871. doi: 10.13700/j.bh.1001-5965.2020.0670(in Chinese)
|
In order to solve the problem of inequity of individual cost in conflict resolution of low-altitude UAV, a multi-aircraft conflict resolution algorithm based on the concept of "nucleolus solution" in cooperative game is proposed. According to the characteristics of low-altitude multi-aircraft conflict scenarios, based on the "nucleolus solution" concept, the UAV conflict resolution payment matrix is established. Combined with the advantages of artificial potential field method and ant colony optimization, a hybrid conflict resolution strategy based on artificial potential field-ant colony optimization (APF-ACO) is proposed. The simulation results show that the APF-ACO hybrid solution strategy has the best performance by integrating the three evaluation indexes of calculation time, feasibility and system efficiency. The solution strategy based on cooperative game "nucleolus solution" can improve individual fairness to a certain extent. At the same time, the priority UAV can be quickly planned to achieve the goal at the expense of a small amount of overall benefits.
| [1] |
SHAKHATREH H, SAWALMEH A, ALFUQAHA A, et al. Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges[J]. IEEE Access, 2019, 7: 48572-48634. doi: 10.1109/ACCESS.2019.2909530
|
| [2] |
TANG X M, JI X Q, LI T. Key technology in multi-UAV conflict detection and resolution strategy[J]. Transactions of Nanjing University of Aeronautics and Astronautics, 2020, 37(2): 175-186.
|
| [3] |
PEREZ-CARABAZA S, BESADA-PORTAS E, LOPEZ-OROZCO J A, et al. Ant colony optimization for multi-UAV minimum time search in uncertain domains[J]. Applied Soft Computing, 2018, 62(2): 789-806.
|
| [4] |
管祥民, 吕人力. 基于混合人工势场与蚁群算法的多飞行器冲突解脱方法[J]. 武汉理工大学学报(交通科学与工程版), 2020, 44(1): 28-33. doi: 10.3963/j.issn.2095-3844.2020.01.006
GUAN X M, LV R L. Conflict resolution method for multiple aircraft based on hybrid artificial potential field and ant colony algorithm[J]. Journal of Wuhan University of Technology (Transportation Science & Engineering), 2020, 44(1): 28-33(in Chinese). doi: 10.3963/j.issn.2095-3844.2020.01.006
|
| [5] |
KIM N, YOON Y. Cooperative sUAV collision avoidance based on satisficing theory[J]. International Journal of Aeronautical and Space Sciences, 2019, 20(4): 978-986. doi: 10.1007/s42405-019-00183-4
|
| [6] |
付其喜, 梁晓龙, 张佳强, 等. 双层优化的多无人机合作式冲突探测与解脱[J]. 哈尔滨工业大学学报, 2020, 52(4): 74-83. https://www.cnki.com.cn/Article/CJFDTOTAL-HEBX202004011.htm
FU Q X, LIANG X L, ZHANG J Q, et al. Cooperative conflict detection and resolution for multiple UAVs using two-layer optimization[J]. Journal of Harbin Institute of Technology, 2020, 52(4): 74-83(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HEBX202004011.htm
|
| [7] |
CAI J L, ZHANG N. Mixed integer nonlinear programming for aircraft conflict avoidance by applying velocity and altitude changes[J]. Arabian Journal for Science and Engineering, 2019, 44(10): 8893-8903. doi: 10.1007/s13369-019-03911-w
|
| [8] |
HERNÁNDEZ-ROMERO E, VALENZUELA A, RIVAS D. Probabilistic multi-aircraft conflict detection and resolution considering wind forecast uncertainty[J]. Aerospace Science and Technology, 2020, 105: 1-17.
|
| [9] |
钱晓鹏, 张洪海, 田宇, 等. 基于核仁解的低空无人机协作冲突解脱算法[J]. 武汉理工大学学报(交通科学与工程版), 2020, 44(4): 676-681. doi: 10.3963/j.issn.2095-3844.2020.04.017
QIAN X P, ZHANG H H, TIAN Y, et al. Cooperative conflict resolution algorithm for low-altitude drone based on nucleolus[J]. Journal of Wuhan University of Technology (Transportation Science & Engineering), 2020, 44(4): 676-681(in Chinese). doi: 10.3963/j.issn.2095-3844.2020.04.017
|
| [10] |
SCHMEIDLER D. The nucleolus of a characteristic function game[J]. SIAM Journal of Applies Mathematics, 1969, 17: 1163-1170. doi: 10.1137/0117107
|
| [11] |
ZHANG X, SUN H, XU G J, et al. On the core, nucleolus and bargaining sets of cooperative games with fuzzy payoffs[J]. Journal of Intelligent & Fuzzy Systems, 2019, 36(6): 6129-6142.
|
| [12] |
SZIKLAI B, FLEINER T, SOLYMOSI T. On the core and nucleolus of directed acyclic graph games[J]. Mathematical Programming, 2017, 163: 243-271. doi: 10.1007/s10107-016-1062-y
|
| [13] |
BERGSTRESSER K, YU P L. Domination structures and multicriteria problems in n-person games[J]. Theory and Decision, 1977, 8(1): 5-48. doi: 10.1007/BF00133085
|
| [14] |
YI Z W, LI L H, QU X, et al. Using artificial potential field theory for a cooperative control model in a connected and automated vehicles environment[J]. Transportation Research Record, 2020, 2674(9): 1005-1018. doi: 10.1177/0361198120933271
|
| [15] |
LI D F, PAN Z H, DENG H B. Two-dimensional obstacle avoidance control algorithm for snake-like robot in water based on immersed boundary-lattice Boltzmann method and improved artificial potential field method[J]. Transactions of the Institute of Measurement and Control, 2020, 42(10): 1840-1857. doi: 10.1177/0142331219897992
|
| [16] |
STODOLA P, MICHENKA K, NOHEL J, et al. Hybrid algorithm based on ant colony optimization and simulated annealing applied to the dynamic traveling salesman problem[J]. Entropy, 2020, 22(8): 1-28.
|
| [17] |
ADEBAYO K J, ADERIBIGBE F M, DELE-ROTIMI A O. On the development of a hybridized ant colony optimization (HACO) algorithm[J]. American Journal of Computational Mathematics, 2019, 9(4): 358-372.
|
Figures(10) / Tables(3)
Copyright © Journal of Beijing University of Aeronautics and Astronautics
Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@buaa.edu.cn
Supported by:
Beijing Renhe Information Technology Co., Ltd.
DownLoad:
