A Secure and Efficient Multi-Object Grasping Detection Approach for Robotic Arms

TL;DR

This paper presents a deep learning-based multi-object grasping detection method for robotic arms that enhances security through image encryption and achieves high accuracy and compression efficiency via edge-cloud collaboration.

Contribution

It introduces a novel grasping approach combining deep learning with GAN-based image encryption and edge-cloud collaboration for improved security and efficiency.

Findings

Achieves 92% accuracy on OCID dataset.

Image compression ratio of 0.03%.

Structural difference value exceeds 0.91.

Abstract

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.