Application of improved grey wolf model in collaborative trajectory optimization of unmanned aerial vehicle swarm

• Published in: Scientific reports Vol. 14; no. 1; pp. 17321 - 18
• Main Authors: Chen, Jiguang, Chen, Yu, Nie, Rong, Liu, Li, Liu, Jianqiang, Qin, Yuxin
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 27-07-2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705/117; 639/705/258; Algorithms; Collaboration; Deep reinforcement learning; Grey wolf algorithm; Humanities and Social Sciences; Intelligence; multidisciplinary; Optimization algorithms; Science; Science (multidisciplinary); Swarm intelligence optimization; Track planning; Unmanned aerial vehicle; Unmanned aerial vehicles; Grey wolf algorithm; Deep reinforcement learning; Swarm intelligence optimization; Unmanned aerial vehicle; Track planning
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-65383-9

With the development of science and technology and economy, UAV is used more and more widely. However, the existing UAV trajectory planning methods have the limitations of high cost and low intelligence. In view of this, grey Wolf algorithm is being used to achieve collaborative trajectory optimization of UAV groups. However, it is found that the Grey Wolf optimization algorithm (GWO) has the problem of weak cooperation. In this study, based on the traditional GWO pheromone factor is introduced to improve it.. Aiming at the problem of unstable performance of swarm intelligence optimization algorithm under dynamic threat, deep reinforcement learning is used to optimize the model. An unmanned aerial vehicle swarm trajectory planning model was constructed based on the improved grey wolf algorithm. Through experimental analysis, the optimal fitness value of the improved grey wolf algorithm was lower than 0.43 of the grey wolf algorithm. Compared with other algorithms, the fitness value of this algorithm is significantly reduced and the stability is higher. In complex scenarios, the improved grey wolf algorithm had a trajectory length of 70.51 km and a planning time of 5.92 s, which was clearly superior to other algorithms. The path length planned by the research and design model was 58.476 km, which was significantly smaller than the other three models. The planning time was 5.33 s and the number of path extension points was 46. The indicator values of the Unmanned Aerial Vehicle swarm trajectory planning model designed by this research were all smaller than the other three models. By analyzing the results, the model can achieve low-cost trajectory optimization, providing more reasonable technical support for unmanned aerial vehicle mission execution.