Research on Deep Learning-Based Lightweight Object Grasping Algorithm for Robots

In order to enhance the efficiency and accuracy of robots in automated production lines and address issues such as inaccurate positioning and limited real-time capabilities in robot-controlled grasping, a deep learning-based lightweight algorithm for robot object grasping is proposed. This algorithm optimizes the lightweight network GG-CNN2 as the base model. Firstly, the depth of the backbone network is increased, and transpose convolutions are replaced with dilated convolutions to enhance the network’s feature extraction for grasping detection. Secondly, the ASPP module is introduced to obtain a wider receptive field and multi-scale feature information. Furthermore, the shallow feature maps are merged with the deep feature maps to incorporate more semantic and detailed information from the images. Experimental results demonstrate that the algorithm achieves an accuracy of 81.27% on the Cornell dataset. Compared to the original GG-CNN2 network, the accuracy has improved by 11.68%, achieving a balance between speed and accuracy. Finally, grasping verification is conducted on the Panda robot arm, with an average success rate of 89.62%, which validates the superiority of the algorithm and showcases the theoretical and practical value of this research.