基于深度学习的增强装配智能人机交互方法OACSTPCD

Aimed at the problem of low level of intelligence of the augmented assembly system and poor interaction between the system and the user,a deep learning-based intelligent parts picking perception method is proposed.The target detection algorithm was used to extract the category and position information of the hand and the parts to be assembled,and the region intersection determination method was used to obtain the region-of-interest.A classification network was built to identify the pickup status of parts in a single frame depth image.Based on this,a continuous frame status recognition method was proposed to eliminate errors of single frame recognition result.The experimental results show that:in the assembly process of aero-engine parts,the proposed method has a single-frame sensing speed within 0.031 s,and the accuracy of continuous frames can reach 100％,which meets the accuracy and speed requirements of on-site recognition.