Heuristic position allocation methods for forming multiple UAV formations. (February 2023)

• Record Type: Journal Article
• Authors: Wu, Yu; Xu, Shuting; Dai, Wei; Lin, Liyang
• Is Part Of: Engineering applications of artificial intelligence. Volume 118(2023)
• Publication Date: 2023-02
• Subjects: UAV formation -- Position allocation -- Consensus-based trajectory planning -- Hybrid genetic and simulated annealing algorithm -- Minimal cost increase strategy; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105654
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: It is a common action that the unmanned aerial vehicles (UAVs) change the formation during the flight to realize different goals and address an emergency. In this process, the UAVs should determine their positions in the new formation to avoid the collision and reduce the flight time. In this paper, the position allocation problem for multiple UAV formations is studied, and the problem is solved considering the different requirements on offline and online cases. In the offline case, the trajectories of UAVs when forming new formations are calculated by the consensus-based trajectory planning (CBTP) method, in which the transient process is introduced to avoid the collision among UAVs. Then a hybrid genetic and simulated annealing (HGSA) algorithm, which utilizes the framework of genetic algorithm (GA) and the operators in simulated annealing (SA) algorithm, is proposed to obtain the optimal position allocation scheme. The CBTP method is treated as a part of the HGSA algorithm to calculate the optimization index for a specific position allocation scheme. In the online case, a two-step minimal cost increase strategy (MCIS)-based method is developed and the UAVs in each new formation and the allocated position of UAV in the new formation are determined successively. Simulation results demonstrate that the CBTP method can generate the safe trajectories for the UAVs when forming new formations, and the optimal position allocation scheme can be obtained by the HGSA algorithm. The HGSA algorithm performs better than other similar algorithms especially in the complicated situation. The MCIS-based method can output the partially optimized position allocation scheme at short notice, but the real trajectories of UAVs are not considered in the online case to reduce the computation time. Highlights: The consensus-based trajectory planning (CBTP) method is proposed to calculate the trajectories of UAVs in the formation. A hybrid genetic and simulate annealing (HGSA) algorithm is developed to allocate UAVs' positions in the new formations. A two-step minimum cost increase strategy (MCIS) algorithm is designed to deal with the online position allocation issue.