The limited time duration and spatial instability of TSTs increase the difficulty of mission planning for unmanned aerial vehicle (UAV) formations, which in turn affects the task execution abilities of UAVs. Aiming at this problem, a multi-UAV dynamic mission planning algorithm based on the time window mechanism is proposed under a distributed formation structure. The proposed algorithm comprehensively considers a variety of complex constraints involved in actual application scenarios and constructs a UAV formation mission planning model with minimum task execution cost and path planning cost as the optimization goal. In this article, particle swarm optimization algorithm is improved by introducing a specific particle coding method and a competitive co-evolutionary population updating strategy, to enhance the solution speed and accuracy of the mission planning model. In this way, the optimal task allocation strategy and task execution path are obtained to meet the mission requirements, and to achieve the multi-UAV cooperative reconnaissance, strike and evaluation of multiple TSTs. Furthermore, the effectiveness of the proposed algorithm is validated via simulations.
Dynamic Mission Planning Algorithm for UAV Formation in Battlefield Environment
IEEE Transactions on Aerospace and Electronic Systems ; 59 , 4 ; 3750-3765
01.08.2023
9377206 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Optimization Algorithm of Battlefield Medicinal Materials Distribution Route Planning
British Library Conference Proceedings | 2020
|The Dirty Battlefield Environment
British Library Conference Proceedings | 1997
|Aerial Warfare and the Future Battlefield - Mission Management
British Library Conference Proceedings | 2000
|