信息与控制2026，Vol.55Issue(4)：714-726,13.DOI:10.13976/j.cnki.xk.2025.2973
融合卷积神经网络和Transformer的轻量化抓取检测算法
Lightweight Grasp Detection Algorithm via CNN-Transformer Fusion
摘要
Abstract
To address real-time grasping tasks in unstructured and cluttered environments,we propose a position-aware lightweight grasp detection network,PAG-Net.The algorithm embeds PEG(positional encoding generator)into MBConv(mobile inverted bottleneck block convolution)to explicitly inject spatial position information,and further improves the network's computational efficiency by introducing an improved LowFormer(a low-resolution transformer)for feature enhancement.The network simultaneously predicts grasp quality,angle,and gripper width in a pixel-wise generation manner,effectively improving the accuracy and speed of grasp detection.Experimental results show that PAG-Net achieves an accuracy of 98.8%on the Cornell dataset and 96.1%on the Jacquard dataset.In PyBullet simulation tests,PAG-Net achieves a 94%grasping success rate in cluttered environments,proving the robustness of the proposed network in complex scenes.关键词
机器人抓取/深度学习/机械臂Key words
robot grasping/deep learning/robotic arm分类
信息技术与安全科学引用本文复制引用
胡平,李浩,张靖..融合卷积神经网络和Transformer的轻量化抓取检测算法[J].信息与控制,2026,55(4):714-726,13.基金项目
国家自然科学基金项目(62302011) （62302011）
安徽省重点研究与开发计划项目(20250718KTRD0020) （20250718KTRD0020）
安徽省教育厅高校科学研究重点项目(2024AH050132) （2024AH050132）
