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.

翻译：机械臂在自动化工业中应用广泛。然而，随着深度学习在机械臂领域的深入应用，出现了抓取算力分配与日益增长的安全性需求等新挑战。本文提出一种基于深度学习与边缘-云端协同的机械臂抓取方法，该方法实现了机械臂任意抓取规划，兼顾了抓取效率与信息安全。此外，通过GAN训练的编码器与解码器能够在压缩图像的同时实现加密，保障了隐私安全。该模型在OCID数据集上达到92%的准确率，图像压缩比降至0.03%，结构差异值高于0.91。