Optimization of Energy Consumption of UAV Cluster Based on K-Means Improved Particle Swarm Algorithm

In this paper, a path planning method using k-means improved particle swarm algorithm, i.e., K-means-PSO algorithm, is proposed to solve the high energy consumption problem in UAV cluster path planning. By establishing the energy consumption model of multi-UAV flights and the optimised energy consumption function model, the clustering algorithm is used to classify them into advantageous particle swarms and disadvantageous particle swarms based on the fitness values, and the ant colony pheromone mechanism and the reverse update mechanism are used to perform the position and speed updates, respectively. Experimental results show that the improved algorithm is able to minimise the energy consumption of UAV clusters, achieving an energy value of 998.34. This study provides an effective solution for UAV cluster path planning.