摘要
Abstract
The difficulty of service robot is that the object shape is irregular,the object pose is random and the background en-vironment is complex.To solve this problem,a robot grasping method based on convolutional neural network is proposed.In this method,the depth map information is used as input,and the grasping quality,grasping direction and grasping angle are mapped in-to a heat map using lightweight convolutional neural network.The candidate grasping boxes are generated according to the peak val-ues in the mass heat map,and the optimal grasping boxes are selected.In order to verify the effectiveness of the research method in this paper,the training is conducted based on the Cornell capture data set,and the IntelRealSenseD415i depth camera and UR5 ma-nipulator are used to build the experimental platform,and the random objects are captured in the real scene.The comparison test shows that the accuracy and detection speed are improved on Cornell data set,reaching 88.2%and 21.0 ms,respectively.For ob-jects outside the data set,the success rate of grasping reaches 86%.To sum up,this method can generate grasping frames for multi-ple objects quickly and accurately,and meet the needs of grasping tasks.关键词
卷积神经网络/抓取框检测/平面拟合/机器人控制Key words
convolutional neural network/grasping box detection/plane fitting/robot control分类
信息技术与安全科学
