基于RGB-D感知与点云碰撞检测的禽蛋采收方法OA

In free-range farming,poultry egg harvesting robots face problems,including insufficient recognition accuracy and task interruption due to collisions during grasping.An improved model was developed based on the original YOLO v8n-OBB framework.A MobileNet-style lightweight architecture was introduced,and a direction-aware enhanced SimAM attention mechanism was proposed.Meanwhile,a refined rotation angle regression strategy based on distribution focal loss was adopted to further boost the localization accuracy of poultry egg bounding boxes and orientation regression precision.Compared with the original YOLO v8n-OBB model,the improved model increased the precision from 97%to 98.5%and the mAP from 98.9%to 99.1%.Meanwhile,the orientation inference accuracy of poultry eggs rised from 86.3%to 91.2%,and the model parameter volume was reduced by 13%.It can well adapt to the hardware resource constraints of edge computing devices.Following poultry egg recognition and localization,environmental point clouds were generated via a depth camera.A differentiated strategy was then employed to generate candidate angle sequences for the three rotational degrees of freedom of the robotic manipulator.Collision detection was conducted between the tool point clouds under candidate poses and the environmental point cloud to determine the final effective collision-free suction pose.After point cloud processing,the system maintained stable point cloud collision detection accuracy,and the average collision detection time for a single poultry egg was shortened to 0.23 s.In practical harvesting scenarios,the system achieved a poultry egg recognition success rate of 99%and an overall harvesting success rate of 78%.The proposed method was suitable for rapid poultry egg recognition and localization for egg harvesting robots,effectively solving the technical challenges of deploying visual perception systems on performance-limited devices and avoiding collisions in real-world scenarios.It constructed a high-performance and robust model for the whole poultry egg harvesting process,providing theoretical support and practical reference for the further development of intelligent automated agricultural production systems.