Collaborative pursuit-evasion game of multi-UAVs based on Apollonius circle in the environment with obstacle

References

• De Souza, C., Newbury, R., Cosgun, A., Castillo, P., Vidolov, B., & Kulić, D. (2021). Decentralized multi-agent pursuit using deep reinforcement learning. IEEE Robotics and Automation Letters, 6(3), 4552–4559. https://doi.org/10.1109/LRA.2021.3068952
   Web of Science ®Google Scholar
• Du, W., Guo, T., Chen, J., Li, B., Zhu, G., & Cao, X. (2021). Cooperative pursuit of unauthorized UAVs in urban airspace via multi-agent reinforcement learning. Transportation Research Part C: Emerging Technologies, 128, 103122. https://doi.org/10.1016/j.trc.2021.103122
   Web of Science ®Google Scholar
• Gao, Y., Wu, Y., Cui, Z., Chen, H., & Yang, W. (2020). Robust design for turning and climbing angle-constrained UAV communication under malicious jamming. IEEE Communications Letters, 25(2), 584–588. https://doi.org/10.1109/LCOMM.2020.3028137
   Web of Science ®Google Scholar
• Isaacs, R. (1999). Differential games: A mathematical theory with applications to warfare and pursuit. Control and optimization. Courier Corporation.
   Google Scholar
• Islam, M., Protik, P., Das, S., & Boni, P. K. (2021). Mobile robot path planning with obstacle avoidance using chemical reaction optimization. Soft Computing, 25(8), 6283–6310. https://doi.org/10.1007/s00500-021-05615-6
   Web of Science ®Google Scholar
• Jiang, X., & Deng, Y. (2021). UAV track planning of electric tower pole inspection based on improved artificial potential field method. Journal of Applied Science and Engineering, 24(2), 123–132. https://doi.org/10.6180/jase.202104_24(2).0001
   Web of Science ®Google Scholar
• Konstantinidis, G., & Kehagias, A. (2019). Selfish cops and active robber: Multi-player pursuit evasion on graphs. Theoretical Computer Science, 780, 84–102. https://doi.org/10.1016/j.tcs.2019.02.025
   Web of Science ®Google Scholar
• Liang, L., Deng, F., Peng, Z., Li, X., & Zha, W. (2019). A differential game for cooperative target defense. Automatica, 102, 58–71. https://doi.org/10.1016/j.automatica.2018.12.034
   Web of Science ®Google Scholar
• Liang, X., Wang, H., & Luo, H. (2020). Collaborative pursuit-evasion strategy of UAV/UGV heterogeneous system in complex three-dimensional polygonal environment. Complexity, 2020. https://doi.org/10.1155/2020/7498740
   Web of Science ®Google Scholar
• Liang, X., Zhao, S., Chen, G., Meng, G., & Wang, Y. (2021). Design and development of ground station for UAV/UGV heterogeneous collaborative system. Ain Shams Engineering Journal, 12(4), 3879–3889. https://doi.org/10.1016/j.asej.2021.04.025
   Web of Science ®Google Scholar
• Ma, J., Lu, H., Xiao, J., Zeng, Z., & Zheng, Z. (2020). Multi-robot target encirclement control with collision avoidance via deep reinforcement learning. Journal of Intelligent & Robotic Systems, 99(2), 371–386. https://doi.org/10.1007/s10846-019-01106-x
   Web of Science ®Google Scholar
• Meir, P., Alexander, V. M., Eloy, G., David, C., & Dejan, M. (2020). Cooperative pursuit by multiple pursuers of a single evader. Journal of Aerospace Information Systems, 17(8), 371–389. https://doi.org/10.2514/1.I010739
   Web of Science ®Google Scholar
• Nath, S., & Ghose, D. (2022). A two-phase evasive strategy for a pursuit-evasion problem involving two non-holonomic agents with incomplete information. European Journal of Control, 68(2022), 100677. https://doi.org/10.1016/j.ejcon.2022.100677
   Google Scholar
• Noori, N., & Isler, V. (2014, September). The lion and man game on polyhedral surfaces with boundary. In 2014 IEEE/RSJ international conference on intelligent robots and systems (pp. 1769–1774). Chicago, IL: IEEE. https://doi.org/10.1109/IROS.2014.6942794
   Google Scholar
• Qi, J., Yang, H., & Sun, H. (2020). MOD-RRT*: A sampling-based algorithm for robot path planning in dynamic environment. IEEE Transactions on Industrial Electronics, 68(8), 7244–7251. https://doi.org/10.1109/TIE.2020.2998740
   Web of Science ®Google Scholar
• Sani, M., Robu, B., & Hably, A. (2020, September). Pursuit-evasion game for nonholonomic mobile robots with obstacle avoidance using NMPC. In 2020 28th Mediterranean conference on control and automation (MED) (pp. 978–983). Saint-Raphaël: IEEE. https://doi.org/10.1109/MED48518.2020.9182862
   Google Scholar
• Šćepanović, J. R., Karač, A., Jakšić, Z. M., Budinski-Petković, L., & Vrhovac, S. B. (2019). Group chase and escape in the presence of obstacles. Physica A: Statistical Mechanics and its Applications, 525(2019), 450–465. https://doi.org/10.1016/j.physa.2019.03.017
   Google Scholar
• Souidi, M. E. H., Siam, A., & Pei, Z. (2019). Multi-agent pursuit coalition formation based on a limited overlapping of the dynamic groups. Journal of Intelligent & Fuzzy Systems, 36(6), 5617–5629. https://doi.org/10.3233/JIFS-181471
   Web of Science ®Google Scholar
• Sun, W., Tsiotras, P., Lolla, T., Subramani, D. N., & Lermusiaux, P. F. (2017). Multiple-pursuer/one-evader pursuit–evasion game in dynamic flowfields. Journal of Guidance, Control, and Dynamics, 40(7), 1627–1637. https://doi.org/10.2514/1.G002125
   Web of Science ®Google Scholar
• Sun, Z., Sun, H., Li, P., & Zou, J. (2022). Cooperative strategy for pursuit-evasion problem with collision avoidance. Ocean Engineering, 266(2022), 112742. https://doi.org/10.1016/j.oceaneng.2022.112742
   Google Scholar
• Yan, F., Jiang, J., Di, K., Jiang, Y., & Hao, Z. (2019). Multi agent pursuit-evasion problem with the pursuers moving at uncertain speeds. Journal of Intelligent & Robotic Systems, 95(1), 119–135. https://doi.org/10.1007/s10846-018-0841-5
   Web of Science ®Google Scholar
• Yao, P., & Qi, S. (2019). Obstacle-avoiding path planning for multiple autonomous underwater vehicles with simultaneous arrival. Science China Technological Sciences, 62(1), 121–132. https://doi.org/10.1007/s11431-017-9198-6
   Web of Science ®Google Scholar
• Zhang, R., Zong, Q., Zhang, X., Dou, L., & Tian, B. (2022). Game of Drones: Multi-UAV pursuit-evasion game with online motion planning by deep reinforcement learning. IEEE Transactions on Neural Networks and Learning Systems, 1–10. https://doi.org/10.1109/TNNLS.2022.3146976
   Web of Science ®Google Scholar
• Zhang, Z., Smereka, J. M., Lee, J., Zhou, L., Sung, Y., & Tokekar, P. (2021). Game tree search for minimizing detectability and maximizing visibility. Autonomous Robots, 45(2), 283–297. https://doi.org/10.1007/s10514-020-09963-4
   Web of Science ®Google Scholar
• Zheng, Z., Yang, S. H., Zheng, Y. J., Liu, X. X., Chen, J., & Su, D. (2020). Obstacle avoidance path planning algorithm for multi-rotor UAVs. Transactions of the Chinese Society of Agricultural Engineering, 36(23), 59–69. https://doi.org/10.11975/j.issn.1002-6819.2020.23.007
   Google Scholar
• Zhou, Z., Shewchuk, J. R., Stipanović, D., Huang, H., & Tomlin, C. J. (2020). Smarter lions: Efficient cooperative pursuit in general bounded arenas. SIAM Journal on Control and Optimization, 58(2), 1229–1256. https://doi.org/10.1137/17M1152589
   Web of Science ®Google Scholar
• Zhu, Q., Wang, K., Shao, Z., & Biegler, L. T. (2020). Receding horizon optimization method for solving the cops and robbers problems in a complex environment with obstacles. Journal of Intelligent & Robotic Systems, 100(1), 83–112. https://doi.org/10.1007/s10846-020-01188-y
   Web of Science ®Google Scholar
• Zou, R., & Bhattacharya, S. (2019). On optimal pursuit trajectories for visibility-based target-tracking game. IEEE Transactions on Robotics, 35(2), 449–465. https://doi.org/10.1109/TRO.2018.2882747
   Web of Science ®Google Scholar