Optical Tracking System for Multi-UAV Clustering

This paper proposes an optical tracking system to realize the clustering of multiple unmanned aerial vehicles (UAVs). The system uses a laser spot as a visual recognition target to realize cluster flight operations in a complex environment. To reduce the interference of sunlight in the optical visual navigation and obtain the optimal combination of laser receiving devices, we selected lasers in a variety of spectrum bands and filter films with different light transmittances to conduct a comprehensive test. By performing a fluid simulation of the UAVs with the related modules mounted in different positions, the optimal installation position was selected to enhance the effect of the UAV wind field. An online learning tracking framework based on the multi-feature extraction of convolutional networks was developed to realize robust long-term visual tracking. In the tracking process, the tracking algorithm could train the detector online according to the status of the target and adaptively initialize the target. Finally, to verify the practical application performance of the clustering system, a group of UAVs was tested in flight. We verified that the proposed method and device exhibit a high feasibility and reliability.