基于深度学习的激光对无人机传能跟瞄系统

Deep-Learning-Based Laser Power Transfer and Targeting System for Unmanned Aerial Vehicles

为实现地面激光为空中飞行无人机实时远程充电过程中的精确跟瞄,提出一种基于YOLOv5改进算法的跟瞄传能系统。其中,识别算法在YOLOv5基础上加入卷积注意力机制与小目标检测层,提升了地面摄像头对无人机上光伏电池目标的捕捉能力。跟踪瞄准过程采用质心追踪、自适应跟瞄算法调控地面转台对准空中目标,实现了地-空传能装置的精准快速对接。模型训练与实验测量结果表明,对距离激光发射端10 m、面积为4 cm×4 cm的光伏电池阵列,该系统的检测速率不低于80 frame/s,可实现对飞行速度低于0.5 m/s的无人机目标的精确跟瞄。该系统跟瞄速度快、精度高,且发射、接收装置简单,是一种便捷高效的无人机激光无线传能跟瞄系统。

A targeting and power transmission system based on an improved algorithm derived from YOLOv5 and that utilizes groundbased laser technology is proposed to achieve precise targeting and tracking during realtime remote charging of unmanned aerial vehicles(UAV).The recognition algorithm incorporates convolutional attention mechanisms and small object detection layers that enhance the ground camera’s ability to capture photovoltaic battery targets on the UAV.The tracking and targeting process utilizes centroid tracking and adaptive targeting algorithms to align the ground platform with the aerial target,enabling accurate and swift docking of the groundtoair power transmission device.Both model training and experimental measurements demonstrate that for a photovoltaic battery array with a distance of 10 m from the laser emission end and an area of 4 cm×4 cm,the detection rate is not fewer than 80 frames/s,enabling precise recognition and targeting of UAV targets with a flight speed of less than 0.5 m/s.Therefore,this system possesses the characteristics of highspeed and highprecision targeting as well as those of simple emitter and receiver devices,making it a convenient and efficient laser wireless power transfer and targeting system for UAV.