Open Access
MATEC Web Conf.
Volume 355, 2022
2021 International Conference on Physics, Computing and Mathematical (ICPCM2021)
Article Number 03002
Number of page(s) 7
Section Computing Methods and Computer Application
Published online 12 January 2022
  1. Wang, Q., Liu, M., Ren, J., et al. (2019) Overview of common algorithms for UAV path planning. Journal of Jilin University (Information Science Edition), 37: 58-67. [Google Scholar]
  2. Cekmez, U., Ozsiginan, M., Sahingoz, O. K. (2016) Multi-colony ant optimization for UAV path planning with obstacle avoidance, In: 2016 International Conference on Unmanned Aircraft Systems. Arlington. pp. 47-52. [CrossRef] [Google Scholar]
  3. Hu, T. (2019) Research on 3D surveillance trajectory planning for small UAV based on bio-inspired algorithms. Dissertation of Chongqing University of Posts and Telecommunications, 41-50. [Google Scholar]
  4. Li, X., Wei, R., Zhang, Q., et al. (2017) A path planning for UAVs in urban building areas based on cellular ant colony algorithm. Journal of Air Force Engineering University (Natural Science Edition), 18: 19-23. [Google Scholar]
  5. Xing, D., Zhen, Z., Zhou, C. et al. (2019) Cooperative search of UAV swarm based on ant colony optimization with artificial potential field. Transactions of Nanjing University of Aeronautics and Astronautics, 36: 912-918. [Google Scholar]
  6. Li, X., Ma, R., Zhang, S., et al. (2020) Improved design of ant colony algorithm and its application in path planning. Acta Aeronautica et Astronautica Sinica, 41: 213-219. [Google Scholar]
  7. Liu, R., Yang, F., Zhang, H. (2018) Path planning for UAV based on improved chaotic ant colony algorithm. Command Information System and Technology, 9: 41-48. [Google Scholar]
  8. Xia, G., Han, Z., Zhao, B., et al. (2019) Unmanned surface vessel path planning based on quantum ant colony algorithm. Journal of Harbin Engineering University, 40: 1263-1268. [Google Scholar]
  9. Jiao, Y. (2019) Research on path planning of UAV based on improved ant colony algorithm. Ship Electronic Engineering, 39: 41-45. [Google Scholar]
  10. Wei, J., Wang, J., Wang, J., et al. (2020) 3D path planning based on improved ant colony algorithm. Computer Engineering and Applications, 56: 217-223. [Google Scholar]
  11. Gao, Y., Chen, X., Zhou, S., et al. (2016) Planning based on improved ant colony algorithm for multiple batches collaborative three-dimensional track. Journal of Northwestern Polytechnical University, 34: 41-46. [Google Scholar]
  12. Jie, D., Tang, X., Chen, J., et al. (2020) Research on conflict resolution technology of multi-UAV based on improved ant colony algorithm. Journal of Wuhan University of Technology (Transportation Science & Engineering), 44: 141-147. [Google Scholar]
  13. Tang, X., Ji, X., Li, T. (2020) Key technology in multi-UAV conflict detection and resolution strategy. Transactions of Nanjing University of Aeronautics and Astronautics, 37: 175-186. [Google Scholar]
  14. Wu, X., Jia, Y., Zhang, J., et al. (2018) Simulation study of UAV conflict resolution based on an improved ant colony algorithm. Journal of Hebei University of Science and Technology, 39: 166-175. [Google Scholar]
  15. Ouyang, Z., Guo, Q. (2018) Penetration route planning of UAV based on improved ant colony algorithm. Modern Defense Technology, 46: 74-78. [Google Scholar]
  16. Zhao, H., Zhou, H., Wang, S. (2021) Quantum particle swarm optimization algorithm of three-dimensional path planning of unmanned aerial vehicle. Aerospace Control, 39: 40-45. [Google Scholar]

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