关键词: 深度学习, 机械臂, 最小尺寸点模型, 目标检测, 位姿估计, 抓取方法, 分类

Abstract: Aiming at the problems of low recognition rate,poor robustness and long running time of traditional robotic arm visual recognition algorithms,a grab method for object detection and pose estimation based on minimum size points model  was proposed.On the basis of the improved YOLO algorithm,the dataset was established with the proposed grab method to train the data set.Through a single RGB image,the object could be directly detected and the 6D pose information could be estimated simultaneously.On this basis,the path planning algorithm was used to capture the object.Simulation experiment proved that the method could accurately classify objects and perform pose estimation.The gripping experiment was carried out on Co602a manipulator,and the results showed the effectiveness of the method.

Key words: deep learning, robotic arm, minimum size points model, object dectection, pose estimation, grab method, classification