Research on stealthy UAV path planning based on improved genetic algorithm

Abstract

An improved adaptive genetic algorithm is proposed for the problem of how to plan the assault path of stealth UAVs in a complex battlefield environment. Combined with radar network, terrain obstruction and other threats to simulate the real combat environment, the stealth UAV’s assault path is planned on a three-dimensional map, and in order to improve the real-time and flexibility of the path, the path needs to be re-planned with the addition of emergent obstacles. The improved adaptive genetic algorithm solves the disadvantages of the traditional genetic algorithm such as slow convergence speed and easy to fall into local extremes. The final simulation results show that the improved adaptive genetic algorithm not only converges faster than the standard genetic algorithm and adaptive genetic algorithm, but also the solution results are closer to the optimal solution and the planned paths are more reasonable.