基于两阶段的机器人动态多物品定位抓取方法

Robot Dynamic Object Positioning and Grasping Method based on Two Stages

为解决工厂流水线上不同种类动态物品的快速精准抓取问题,提出一种两阶段动态多物品定位抓取方法.第1阶段采用所提多尺度上下文感知的单分支融合语义分割网络获取目标物品的掩码区域:首先特征提取网络采用单分支结构,在保证提取丰富的空间信息和高层语义信息的同时,减小网络参数量;随后特征融合网络通过双边引导特征融合模块增强空间信息和语义信息的表达能力;最后设计特征增强网络,通过特征辅助收敛模块嵌入浅层和深层网络中,加快网络收敛速度.第2阶段采用基于轮廓点检测的快速位姿估计策略在掩码区域预测最佳抓取点位姿.在自建数据集上的测试及流水线平台抓取实验结果表明,所提方法能实时检测和预测物品抓取点位姿,精准完成物品抓取,其分割精度、预测时间和抓取成功率均优于对比方法.

A twostage dynamic multiobject positioning and grasping method is proposed to solve the problem of fast and accurate grasping of various types of dynamic objects on a factory assembly line.In the first stage,the proposed multiscale contextaware singlebranch fusion semantic segmentation network is used to obtain the mask area of the target object:first,the feature extraction network adopts a singlebranch structure,which reduces the number of network parameters while ensuring the extraction of rich spatial information and highlevel semantic information;subsequently,the feature fusion network improves the expression ability of spatial data and semantic information through the bilateral guided feature fusion module;finally,the feature enhancement network is designed,and the feature assisted convergence module is embedded in the shallow and deep networks to accelerate the convergence speed of the network.In the second stage,a quick pose estimation strategy based on contour point detection is applied to predict the optimum posture of the grasping point in the mask region.The test results on the selfbuilt dataset and the pipeline platform grab experiments demonstrate that the proposed method can detect and predict the position and posture of the object grab points in real time and accurately complete the object grab.Furthermore,its segmentation accuracy,prediction time,and grab success rate are better than the comparison method.