Study on Vision-Based Dynamic Grasping Technology for Ammunition Components
In response to the issues of low detection accuracy and poor pose adaptability in the dynamic grasping process of robots in traditional military industry manufacturing,this paper proposes a dynamic grasping technology based on deep learning to efficiently grasp ammunition components on a moving conveyor belt.The YOLOv5 object detection network is employed to accurately and in real-time identify and locate ammunition components,while the Kalman filter algorithm is utilized for precise target tracking and position prediction.Additionally,an approaching grasp strategy is implemented to ensure stable and reliable grasping in dynamic environments.The feasibility of the proposed scheme is verified using the robot operating system(ROS)platform.The results indicate that this dynamic grasping solution meets practical requirements and provides a reference for similar grasping tasks.
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