Research on path planning of continuous control UAV based on improved PSO algorithm

The introduction of intelligent search algorithms provides an excellent solution to the global and local path planning problems of UAVs. The most classic and representative is Particle Swarm Optimization ( PSO ). However, when applying PSO algorithm to UAV path planning, it is easy to be constrained by factors such as environmental changes and fail to achieve the expected results. In order to solve the communication delay and communication reliability problems in wireless communication, a path planning study under successive control of UAVs is proposed. In this study, the defects of PSO algorithm and the problems in UAV path planning are improved. The improved algorithm introduces the adaptive weight into the update mechanism, and combines the Levy flight strategy to improve the global search strategy to reduce the impact of environmental changes on the algorithm update, so as to ensure the optimal solution. It can be seen from the simulation results that the improved PSO algorithm has achieved significant improvement in data update accuracy, local and overall search capabilities. Compared with genetic algorithm, grey wolf optimization algorithm and PSO algorithm, the improved PSO algorithm has obvious advantages such as fast planning speed and short planning path.