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grasp detection with Hierarchical Multi-Scale Feature Fusion and Inverted Shuffle Residual

Tsinghua Science and Technology 2024年第1期29卷 244-256页

作者： Wenjie Geng Zhiqiang Cao Peiyu Guan Fengshui Jing Min Tan Junzhi Yu State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Department of Advanced Manufacturing and Robotics College of EngineeringPeking UniversityBeijing 100871China

grasp detection plays a critical role for robot *** pixel-wise grasp detection networks with encoder-decoder structure receive much attention due to good accuracy and ***,they usually transmit the high-level feature in the encoder to the decoder,and low-level features are *** is noted that low-level features contain abundant detail information,and how to fully exploit low-level features remains ***,the channel information in high-level feature is also not well ***,the performance of grasp detection is *** solve these problems,we propose a grasp detection network with hierarchical multi-scale feature fusion and inverted shuffle *** low-level and high-level features in the encoder are firstly fused by the designed skip connections with attention module,and the fused information is then propagated to corresponding layers of the decoder for in-depth feature *** a hierarchical fusion guarantees the quality of grasp ***,an inverted shuffle residual module is created,where the high-level feature from encoder is split in channel and the resultant split features are processed in their respective *** such differentiation processing,more high-dimensional channel information is kept,which enhances the representation ability of the ***,an information enhancement module is added before the encoder to reinforce input *** proposed method attains 98.9%and 97.8%in image-wise and object-wise accuracy on the Cornell grasping dataset,respectively,and the experimental results verify the effectiveness of the method.

关键词： grasp detection hierarchical multi-scale feature fusion skip connections with attention inverted shuffle residual

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Robotic grasp detection Using Light-weight CNN Model

Robotic Grasp Detection Using Light-weight CNN Model

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第32届中国控制与决策会议

作者： Yang Jiang Xulong Li Minghao Yu Zhongyu Bai Faculty of Robot Science and Engineering Northeastern University

Manipulating objects is the premise of many robotic applications,such as assembly,sort and *** programming is widely used in industrial robot,it is necessary to improve the intelligent level of *** this paper,we propose an accurate,real-time,light-weight convolutional neural network SqueezeNet-RCM to predict a grasp configuration from a RGB and aligned depth image of graspable *** of using sliding window or regional proposal network to generate potential bounding box,we use a way of end-to-end to train and test our *** the standard Cornell grasping Dataset,our model achieves accuracy of 90.1% and 88.6% on image-wise split and objectwise split respectively,speed at 73 frames per second(fps) on GPU inferencing,which could meet the requirement of ***,our model size is 2.9 MB,which is able to fit the memory to limited environment such as FPGA.

关键词： grasp detection Robotic grasping SqueezeNet-RCM SqueezeNet CNN

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A Multi-Scale grasp Detector Based on Fully Matching Model

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Computer Modeling in Engineering & Sciences 2022年第11期133卷 281-301页

作者： Xinheng Yuan Hao Yu Houlin Zhang Li Zheng Erbao Dong Heng’an Wu School of Engineering Science University of Science and Technology of ChinaHefei230027China

Robotic grasping is an essential problem at both the household and industrial levels,and unstructured objects have always been difficult for ***-plate grippers and algorithms,focusing on partial information of objects,are one of the widely used ***,most works predict single-size grasp rectangles for fixed cameras and gripper *** this paper,a multi-scale grasp detector is proposed to predict grasp rectangles with different sizes on RGB-D or RGB images in real-time for hand-eye cameras and various parallel-plate *** detector extracts feature maps of multiple scales and conducts predictions on each scale *** guarantee independence between scales and efficiency,fully matching model and background classifier are applied in the *** on analysis of the Cornell grasp Dataset,the fully matching model canmatch all labeled grasp ***,background classification,along with angle classification and box regression,functions as hard negative mining and background *** detector is trained and tested on the augmented dataset,which includes images of 320×320 pixels and grasp rectangles ranging from 20 tomore than 320 *** performs up to 98.87% accuracy on image-wise dataset and 97.83% on object-wise split dataset at a speed of more than 22 frames per *** addition,the detector,which is trained on a single-object dataset,can predict grasps on multiple objects.

关键词： grasp detection deep convolutional neural network anchor box parallel-plate gripper

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基于GR-CNN 改进的抓取检测算法及仿真

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建模与仿真 2023年第6期12卷 5196-5206页

作者：娄晓恒毛鹏军牛鑫宇段云涛河南科技大学机电工程学院，河南洛阳

随着计算机视觉的发展，抓取检测在机器人视觉领域取得了广泛应用，以往的抓取检测方法存在对抓取相关特征学习不够重视、检测精度低、泛化能力差等问题。GR-CNN算法解决了之前这些检测算法存在的一些问题，但是该算法预测的抓取框所处... 详细信息

随着计算机视觉的发展，抓取检测在机器人视觉领域取得了广泛应用，以往的抓取检测方法存在对抓取相关特征学习不够重视、检测精度低、泛化能力差等问题。GR-CNN算法解决了之前这些检测算法存在的一些问题，但是该算法预测的抓取框所处位置和角度有时会存在不准确的情况。针对这一问题，对该算法进行改进优化。通过加入CBAM注意力机制模块使网络主要集中于学习可抓取的信息，从而提高抓取预测的准确性。并在ROS环境下对改进后的算法进行验证，通过Gazebo与MoveIt!联合仿真，使用UR5机械臂对桌面上随机放置的四类物体进行抓取放置任务，平均抓取成功率达到86%，实验结果表明改进后的算法抓取框适应性更强，对规则物体与形状结构复杂的物体都可以有效抓取。

关键词：抓取检测注意力机制模块 ROS Gazebo MoveIt! grasp detection Attention Mechanism Module ROS Gazebo MoveIt!

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