Deep-Learning-Based Laser Power Transfer and Targeting System for Unmanned Aerial Vehicles
A targeting and power transmission system based on an improved algorithm derived from YOLOv5 and that utilizes ground-based laser technology is proposed to achieve precise targeting and tracking during real-time 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 ground-to-air 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 high-speed and high-precision 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.
machine visionwireless power transferrecognition and targetingYOLOv5 algorithmsmall object detection
NETL
NSTL
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