Research on learning behavior control algorithm for micro flapping-wing aerial robot based on clustering data-driven

In order to solve the problem of poor adaptability and generalization ability of traditional control algorithms for micro flapping-wing aerial robots, and the limited application of tradi-tional control algorithms, the use of task-oriented and job-object-oriented data training and data-driven for aerial robots is utilized to achieve navigation control of micro flapping-wing aerial robots based on machine vision. This paper proposes a learning behavior control al-gorithm for micro flapping-wing aerial robots based on cluster data-driven. The data from the gyroscope of the flapping-wing machine is processed using cluster analysis to obtain the directional characteristic behavior function of the flapping-wing machine. And utilize cluster analysis to process the visual images of the flapping-wing aerial robot, in order to obtain the image area response behavior function of the aerial robot. This enhances the generalization ability of the behavior control function of the flapping-wing aerial robot with perception fea-tures. While ensuring the flapping-wing aerial robot’s vision-based navigation, the features of the flapping-wing aerial robot’s visual target and environment are extracted through clus-ter analysis, providing technical support for the behavior control and target tracking of the flapping-wing aerial robot based on dynamic vision. An experimental verification system was built using a flapping-wing aerial robot system and an onboard camera. Experimental verification was conducted on the learning behavior control algorithm for flapping-wing aer-ial robots based on cluster data-driven. Four verification experiments were conducted to demonstrate the effectiveness of the learning behavior control algorithm.