REFERENCES

1. Garcia E, Casbeer DW, Von Moll A, Pachter M. Multiple pursuer multiple evader differential games. IEEE Trans Automat Contr 2020;66:2345-50.

2. Yu D, Chen CLP. Smooth transition in communication for swarm control with formation change. IEEE Trans Ind Inf 2020;16:6962-71.

3. Camci E, Kayacan E. Game of drones: UAV pursuit-evasion game with type-2 fuzzy logic controllers tuned by reinforcement learning. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE; 2016. pp. 618–25.

4. Vidal R, Rashid S, Sharp C, et al. Pursuit-evasion games with unmanned ground and aerial vehicles. In: Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation. IEEE; 2001. pp. 2948–55.

5. Turetsky V, Shima T. Target evasion from a missile performing multiple switches in guidance law. J Guid Control Dyn 2016;39:2364-73.

6. de Souza C, Newbury R, Cosgun A, Castillo P, Vidolov B, Kulić D. Decentralized multi-agent pursuit using deep reinforcement learning. IEEE Robot Autom Lett 2021;6:4552-59.

7. Lopez VG, Lewis FL, Wan Y, Sanchez EN, Fan L. Solutions for multiagent pursuit-evasion games on communication graphs: Finite-time capture and asymptotic behaviors. IEEE Trans Automat Contr 2019;65:1911-23.

8. Dong J, Zhang X, Jia X. Strategies of pursuit-evasion game based on improved potential field and differential game theory for mobile robots. In: 2012 Second International Conference on Instrumentation, Measurement, Computer, Communication and Control. IEEE; 2012. pp. 1452–56.

9. Sun Q, Chen Z, Qi N, Lin H. Pursuit and evasion conflict for three players based on differential game theory. In: 2017 29th Chinese Control And Decision Conference (CCDC). IEEE; 2017. pp. 4527–31.

10. Haslegrave J. An evasion game on a graph. Discrete Math 2014;314:1-5.

11. Kehagias A, Hollinger G, Singh S. A graph search algorithm for indoor pursuit/evasion. Math Comput Modell 2009;50:1305-17.

12. Janosov M, Virágh C, Vásárhelyi G, Vicsek T. Group chasing tactics: how to catch a faster prey. New J Phys 2017;19:053003.

13. Wang J, Li G, Liang L, Wang D, Wang C. A pursuit-evasion problem of multiple pursuers from the biological-inspired perspective. In: 2021 40th Chinese Control Conference (CCC). IEEE; 2021. pp. 1596–601.

14. Qu X, Gan W, Song D, Zhou L. Pursuit-evasion game strategy of USV based on deep reinforcement learning in complex multi-obstacle environment. Ocean Eng 2023;273:114016.

15. Bilgin AT, Kadioglu-Urtis E. An approach to multi-agent pursuit evasion games using reinforcement learning. In: 2015 International Conference on Advanced Robotics (ICAR). IEEE; 2015. pp. 164–69.

16. Wang Y, Dong L, Sun C. Cooperative control for multi-player pursuit-evasion games with reinforcement learning. Neurocomputing 2020;412:101-14.

17. Du W, Guo T, Chen J, Li B, Zhu G, Cao X. Cooperative pursuit of unauthorized UAVs in urban airspace via Multi-agent reinforcement learning. Trans Res Part C Emerg Technol 2021;128:103122.

18. Zhang Z, Wang X, Zhang Q, Hu T. Multi-robot cooperative pursuit via potential field-enhanced reinforcement learning. In: 2022 International Conference on Robotics and Automation (ICRA). IEEE; 2022. pp. 8808–14.

19. Hüttenrauch M, Šošić A, Neumann G. Deep reinforcement learning for swarm systems. J Mach Learn Res 2019;20:1-31. Available from: https://www.jmlr.org/papers/volume20/18-476/18-476.pdf.[Last accessed on 13 March 2024]

20. Liu S, Hu X, Dong K. Adaptive double fuzzy systems based Q-learning for pursuit-evasion game. IFAC-PapersOnLine 2022;55:251-56.

21. Zhang J, Zhang K, Zhang Y, Shi H, Tang L, Li M. Near-optimal interception strategy for orbital pursuit-evasion using deep reinforcement learning. Acta Astronaut 2022;198:9-25.

22. dos Santos RF, Ramachandran RK, Vieira MAM, Sukhatme GS. Parallel multi-speed Pursuit-Evasion Game algorithms. Robot Auton Syst 2023;163:104382.

23. Wang S, Wang B, Han Z, Lin Z. Local sensing based multi-agent pursuit-evasion with deep reinforcement learning. In: 2022 China Automation Congress (CAC). IEEE; 2022. pp. 6748–52.

24. Liu Z, Qiu C, Zhang Z. Soft-actor-attention-critic based on unknown agent action prediction for multi-agent collaborative confrontation. In: 2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE). IEEE; 2023. pp. 527-35.

25. Zhang M, Yan C, Dai W, Xiang X, Low KH. Tactical conflict resolution in urban airspace for unmanned aerial vehicles operations using attention-based deep reinforcement learning. Green Energy Intell Trans 2023;2:100107.

26. Peng Y, Tan G, Si H, Li J. DRL-GAT-SA: Deep reinforcement learning for autonomous driving planning based on graph attention networks and simplex architecture. J Syst Archit 2022;126:102505.

27. Kim Y, Singh T. Energy-time optimal control of wheeled mobile robots. J Franklin Inst 2022;359:5354-84.

28. Kathirgamanathan A, Mangina E, Finn DP. Development of a soft actor critic deep reinforcement learning approach for harnessing energy flexibility in a large office building. Energy AI 2021;5:100101.

Intelligence & Robotics
ISSN 2770-3541 (Online)
Follow Us

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/