A multi- unmanned aerial vehicle dynamic task assignment method based on bionic algorithms. (April 2022)

• Record Type: Journal Article
• Authors: Shi, Jiaqi; Tan, Li; Lian, Xiaofeng; Xu, Tianying; Zhang, Hongtao; Zhang, Yu
• Is Part Of: Computers & electrical engineering. Volume 99(2022)
• Publication Date: 2022-04
• Subjects: Task allocation -- Bionic algorithms -- Calculation time -- Travel time -- Traveled distance; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.107820
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Shortens the algorithm's calculation time and the UAV's travel time and distance by improving the task allocation method Dynamic allocation of the UAVs based on mission points and number of the UAVs. Use the comparison mechanism to compare the path calculated by the bionic algorithm and the distance between two points. Abstract: Because of the long calculation time in task allocation algorithms and the long travelled distance of the Unmanned Aerial Vehicle (UAV) during task allocation, a multi-UAV task allocation method based on three bionic algorithms, which are the ant colony algorithm, bat algorithm, and gray wolf algorithm was proposed in this paper. The Multi-UAV Dynamic Task Assignment Method Based on Bionic Algorithms (TABA) method dynamically allocates UAVs based on the number of task points and includes a comparison mechanism. The results of experiments demonstrate that, among the three algorithms, the proposed method has 30% improvement to the conventional methods, and the UAV travel time is about one-third that of the original algorithm on average, thereby revealing the effectively reduced algorithm complexity. Moreover, the algorithm can reduce the travel time of UAVs.