机电工程技术2024，Vol.53Issue(6)：74-78,118,6.DOI:10.3969/j.issn.1009-9492.2024.00071
融合BlendMask的机械手臂抓取位姿估计模型
A Robotic Arm Grasping Pose Estimation Model Based on BlendMask
摘要
Abstract
In the robot grasping environment,both speed and accuracy are of vital importance.An end-to-end grasping pose estimation model called Grasp PCA Network(GPNet)is presented.Basing on BlendMask,GPNet combines instance segmentation and grasping pose estimation to handle real-time grasping pose estimation in complex scenes.To get grasping pose,GPNet extends BlendMask by adding the grasping center and grasping direction estimation branch;Secondly,GPNet combines the Hough voting idea to improve the accuracy and robustness of the 2D grasping center estimation;To eliminate interference on predicting grasping direction caused by round object,the method based on geometric information called Ellipse Filtering is used.Finally,by making use of a new loss function to train GPNet,the grasping pose result using the depth is got.The grasping model is evaluated on industrial Internet platform of the China Academy of Information and Communications Technology and the laboratory of the Ministry of Industry and Information Technology.Pick nine kinds of object as grasping target,under the complex scene of average six kinds of objects with obstacles,the average speed of grasping pose estimation by the GPNet reaches 0.057 s,and the average grasping accuracy reaches 90.2%.关键词
抓取位姿估计/BlendMask/端到端/椭圆筛选/霍夫投票Key words
grasping pose estimation/BlendMask/end-to-end/ellipse filtering/Hough voting分类
信息技术与安全科学引用本文复制引用
刘家东,费博文,万子豪,胡建华..融合BlendMask的机械手臂抓取位姿估计模型[J].机电工程技术,2024,53(6):74-78,118,6.基金项目
中科院科技服务网络计划(STS)(STS-HP-202202) （STS）
