A Fixed-Wing UAV Swarm Coverage Search Strategy Based on Improved Weighted Mean of Vectors (IINFO) Algorithm

Unmanned aerial vehicle (UAV) swarm coverage path planning is computationally intensive and difficult to converge. For the problem of rapid acquisition of unknown sea information by UAV swarms, this paper proposes a fast coverage search strategy for fixed-wing UAV swarms based on improved weighted mean of vectors (IINFO) algorithm. First, a two-dimensional mission space model is established based on the mission environment and the flight characteristics of fixed-wing UAVs, and the trajectory planning problem is transformed into a multi-dimensional function optimization problem. Then, the mission area is divided to each fixed-wing UAV by IINFO algorithm. Finally, within the assigned mission area, this paper uses the planning method with the minimum number of turns to obtain the UAV trajectory. This paper selects a swarm of fixedwing UAVs carrying synthetic aperture radar (SAR) as a research object, comparing the improved beluga whale optimization (IBWO) algorithm, the improved brain storm optimization (IBSO) algorithm and the IINFO algorithm, the simulation results show that the trajectory search time planned by the IINFO Algorithm is the shortest, which verifies the effectiveness of the proposed method.