Page 60 - Read Online

Wang et al. Intell Robot 2023;3(4):538-64 I http://dx.doi.org/10.20517/ir.2023.30 Page 27 of 27

9. Zhong Y, Yao P, Sun Y, Yang J. Method of multi-UAVs cooperative search for Markov moving targets. In: 2017 29th Chinese Control
And Decision Conference (CCDC). IEEE; 2017. pp. 6783–89. DOI
10. Hu X, Liu Y, Wang G. Optimal search for moving targets with sensing capabilities using multiple UAVs. J Syst Eng Electron 2017;28:526–
35. DOI
11. Yue W, Xi Y, Guan X. A new searching approach using improved multi-ant colony scheme for multi-UAVs in unknown environments.
Ieee Access 2019;7:161094-102. DOI
12. Li Z, Xu P, Chang X, et al. When object detection meets knowledge distillation: A survey. IEEE Trans Pattern Anal Mach Intell
2023;45:10555-79. DOI
13. Li M, Huang PY, Chang X, et al. Video pivoting unsupervised multi-modal machine translation. IEEE Trans Pattern Anal Mach Intell
2023;45:3918-32. DOI
14. Shorakaei H, Vahdani M, Imani B, Gholami A. Optimal cooperative path planning of unmanned aerial vehicles by a parallel genetic
algorithm. Robotica 2016;34:823–36. DOI
15. Alanezi MA, Bouchekara HR, Apalara TAA, et al. Dynamic target search using multi-UAVs based on motion-encoded genetic algorithm
with multiple parents. IEEE Access 2022;10:77922–39. DOI
16. Luo R, Zheng H, Guo J. Solving the multi-functional heterogeneous UAV cooperative mission planning problem using multi-swarm fruit
fly optimization algorithm. Sensors 2020;20:5026. DOI
17. Ma C, Zhu X, Liu S, Gui J, Yao W. A multi-UAV cooperative searching method based on differential evolution. In: 2022 34th Chinese
Control and Decision Conference (CCDC). IEEE; 2022. pp. 5643–48. DOI
18. Zhikuo C, Chen W, Yuxing Z. A cooperative approach to multi-UAVs search for mobile targets based on pigeon-inspired optimization.
In: 2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC). IEEE; 2018. pp. 1–8. DOI
19. Fang Z, Wang J, Ren Y, et al. Age of information in energy harvesting aided massive multiple access networks. IEEE J Select Areas
Commun 2022;40:1441–56. DOI
20. Zhang Y, Wang J, Zhang L, et al. Reliable transmission for NOMA systems with randomly deployed receivers. Ieee T Commun
2022;71:1179–92. DOI
21. Zhou Z, Liu D, Bao W, et al. Multi-UAV cooperative target search method based on nash equilibrium distributed model predictive control.
In: Proceedings of the 4th International Symposium on Application of Materials Science and Energy Materials; 2020. pp. 631–41. DOI
22. Yao P, Wei X. Multi-UAV information fusion and cooperative trajectory optimization in target search. IEEE Syst J 2021;16:4325–33. DOI
23. Wei W, Wang J, Fang Z, et al. 3U: Joint design of UAV-USV-UUV networks for cooperative target hunting. IEEE T Veh Technol
2022;72:4085–90. DOI
24. Jia Z, Wenjun X, Qing G. Research on Multi-UAV collaborative search in dynamic environment. In: MATEC Web of Conferences. vol.
173. EDP Sciences; 2018. p. 02002. DOI
25. Kamrani F, Ayani R. Using on-line simulation for adaptive path planning of UAVs. In: 11th IEEE International Symposium on Distributed
Simulation and Real-Time Applications (DS-RT’07). IEEE; 2007. pp. 167–74. DOI
26. de Alcantara Andrade FA, Reinier Hovenburg A, Netto de Lima L, et al. Autonomous unmanned aerial vehicles in search and rescue
missions using real-time cooperative model predictive control. Sensors 2019;19:4067. DOI
27. Grefenstette JJ. Genetic algorithms and machine learning. In: Proceedings of the sixth annual conference on Computational learning
theory; 1993. pp. 3–4. DOI
28. Das S, Suganthan PN. Differential evolution: a survey of the state-of-the-art. IEEE T Evolut Comput 2010;15:4–31. DOI
29. Gao Wf, Liu Sy, Huang Ll. A novel artificial bee colony algorithm based on modified search equation and orthogonal learning. IEEE T
Cybernetics 2013;43:1011–24. DOI