Research on Clustering Data-Driven Intelligent Perception Algorithm for Micro Mobile Robots

To address the challenges faced by micro flapping-wing flying robots in visual navigation—specifically, the large volume of visual information and the difficulty in transforming it into usable intelligent visual data—this paper proposes a clustering-based data-driven approach for directional and image perception. The aim is to enable intelligent visual navigation for flapping-wing robots. The proposed method performs clustering analysis on gyroscope data from the flapping-wing robot to extract directional features. Simultaneously, it applies clustering techniques to visual images captured by the robot to identify intelligent features such as edges. This approach enables the robot to acquire multiple optimized perceptual data types, thereby enhancing the behavior control system. Through the use of clustering analysis, the method not only improves the effectiveness of visual navigation but also extracts features related to visual targets and environmental information, providing technical support for visual target tracking. The experimental platform consists of a flapping-wing robot equipped with an onboard camera, and the proposed clustering-driven visual image perception approach has been experimentally validated. Experimental results demonstrate the high feasibility and effectiveness of the method in practical applications. The main contributions of this study lie in two aspects: (1) a clustering-driven visual image perception method for flapping-wing robots, and (2) a clustering-based approach for identifying posture and behavioral patterns of flapping-wing flying robots.