Study on border patrol task planning of heterogeneous UAVs group based on swarm intelligence

Abstract

Heterogeneous UAVs performing patrol tasks are a new type of border patrol method with high flexibility, high patrol efficiency, and low operating cost In order to improve the ability of heterogeneous UAVs to perform border patrol task, by constructing a three-dimensional complex coordinated planning model, a multi-objective fitness function with the minimum patrol energy consumption and maximum patrol coverage of UAVs in a complex mountain environment is established. Design an improved shuffled frog leaping algorithm (ISFLA) based on spiral search mechanism to solve the problem of task planning in complex mountain environment. The proposed algorithm is verified by simulation experiments. The simulation results show that the ISFLA algorithm for solving the path problem in complex three-dimensional environment has significantly improved the solving efficiency, accuracy and global convergence compared with the particle swarm optimization (PSO), differential evolution (DE) and shuffled frog leaping algorithm (SFLA). The experiments show that the proposed algorithm also has excellent solving ability in solving complex planning problems.