Research on intelligent algorithm for autonomous landing of quadrotor UAV
Chaohang Xue, Jin Liying, Kelian Hu, Xindong Hu, Yujie Shen · 2023
This paper takes the autonomous landing of four-rotor UAV as the specific research object. Aiming at the problem of identifying the feature points of landing signs and improving the accuracy of autonomous landing, an improved multi-objective soft subspace clustering algorithm is proposed. The anti-redundant mutation operator and forward comparison operation are designed, it is proposed to improve the population diversity and convergence speed of the algorithm. The iNSGA-II is used as the base algorithm, and the repair operator and local search operator are designed to reflect the clustering characteristics. An autonomous landing control model based on visual recognition is established. A large number of position feature points are clustered and analyzed to decouple the highly nonlinear mapping relationship in the feature space, and then the redundant information between the dimensions is stripped to achieve the maximum stability of the sampling points, the importance sequence of all features and the accurate classification.