Simulation investigation of autonomous route planning for unmanned aerial vehicles based on an improved genetic algorithm

With the rapid development of robotics technology and the increasing maturity of flight control technology, unmanned aerial vehicles are being widely used in an increasing number of fields. Path planning is an important component of unmanned aerial vehicle autonomous flight, and planning high-quality paths is the key to ensuring that unmanned aerial vehicles can safely and quickly reach their destination while performing tasks. This paper aims to simulate the autonomous route planning of unmanned aerial vehicles based on an improved genetic algorithm. In this paper, an evaluation of the use of a genetic algorithm for path planning and improvements to the adaptive genetic algorithm are proposed. In this study, a simulation experiment involving unmanned aerial vehicle autonomous route planning based on an improved genetic algorithm is performed. The experimental results in this paper show that through the comparative analysis of the two evolutionary algorithms, the improved genetic algorithm cost 22.91 s, and the traditional genetic algorithm cost 40.22 s. The consumption time increases by 43.04%. The results showed that the path graph obtained by this method performs better than that of the unoptimized genetic algorithm. Based on the above results, this paper proposes a multi-UAV route planning method based on a genetic algorithm. According to the experimental results, the improved genetic algorithm based on artificial intelligence is efficient and feasible for autonomous UAV route planning. For enterprises, secure and efficient data security and privacy management are crucial. This requires companies to use artificial intelligence technology when developing security products and services. The experimental results indicate that unmanned aerial vehicles based on improved genetic algorithms have played an important role in civil aviation. An increasing number of drones are being used for actual flight missions, which is expected to increase the convenience of unmanned aerial vehicles. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.