Refine Search

New Search

Results: 2

(searched for: doi:10.1109/cgncc.2016.7828772)
Save to Scifeed
Page of 1
Articles per Page
by
Show export options
  Select all
, Keqin Chen, Jiongming Su, Hongfu Liu, Wanpeng Zhang
Wireless Communications and Mobile Computing, Volume 2021, pp 1-13; https://doi.org/10.1155/2021/4312592

Abstract:
Unmanned aerial vehicles (UAVs) are gradually used in logistics transportation. They are forbidden to fly in some airspace. To ensure the safety of UAVs, reasonable path planning and design is one of the key factors. Aiming at the problem of how to improve the success rate of unmanned aerial vehicle (UAV) maneuver penetration, a method of UAV penetration path planning and design is proposed. Ant colony algorithm has strong path planning ability in biological swarm intelligence algorithm. Based on the modeling of UAV planning and threat factors, improved ant colony algorithm is used for UAV penetration path planning and design. It is proposed that the path with the best pheromone content is used as the planning path. Some principles are given for using ant colony algorithm in UAV penetration path planning. By introducing heuristic information into the improved ant colony algorithm, the convergence is completed faster under the same number of iteratives. Compared with classical methods, the total steps reduced by 56% with 50 ant numbers and 200 iterations. 62% fewer steps to complete the first iteration. It is found that the optimal trajectory planned by the improved ant colony algorithm is smoother and the shortest path satisfying the constraints.
, Xiaoguang Gao, , Yiwei Zhai, Qianglong Wang
Published: 12 January 2021
Chinese Journal of Aeronautics, Volume 34, pp 187-204; https://doi.org/10.1016/j.cja.2020.12.027

The publisher has not yet granted permission to display this abstract.
Page of 1
Articles per Page
by
Show export options
  Select all
Back to Top Top