A Secure and Efficient Multi-Object Grasping Detection Approach for Robotic Arms
This work addresses security and efficiency challenges in robotic arm grasping for industrial automation, but it appears incremental as it combines existing techniques like deep learning and edge-cloud collaboration.
The paper tackles the problem of secure and efficient multi-object grasping for robotic arms by proposing a deep learning and edge-cloud collaboration approach, achieving 92% accuracy on the OCID dataset, a 0.03% image compression ratio, and a structural difference value above 0.91.
Robotic arms are widely used in automatic industries. However, with wide applications of deep learning in robotic arms, there are new challenges such as the allocation of grasping computing power and the growing demand for security. In this work, we propose a robotic arm grasping approach based on deep learning and edge-cloud collaboration. This approach realizes the arbitrary grasp planning of the robot arm and considers the grasp efficiency and information security. In addition, the encoder and decoder trained by GAN enable the images to be encrypted while compressing, which ensures the security of privacy. The model achieves 92% accuracy on the OCID dataset, the image compression ratio reaches 0.03%, and the structural difference value is higher than 0.91.