Open AccessResearch on Adaptive Grasping of Robotic Manipulator with Depth Visual Perception
Author(s) -
Wenjiang Liang,
Honglin Zhu,
Haisen Zeng,
Yongchuan Xiong,
Chengya Lu,
Luoqian Emu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1924/1/012024
Subject(s) - grasp , computer vision , artificial intelligence , computer science , object (grammar) , robot , task (project management) , process (computing) , rgb color model , perception , manipulator (device) , robot manipulator , engineering , systems engineering , neuroscience , biology , programming language , operating system
Aiming at the problem of poor grasping performance and low repetitive teaching efficiency when traditional machine vision-based robots are facing the grasping tasks of objects with unknown heights, an adaptive grasping method of robotic manipulator based on depth visual perception feedback control is proposed. The RGB-D image is acquired through the depth camera, and the embedded computing device is used to process the RGB-D image based on ROS(Robot Operating System) to obtain the object positioning information. Finally, the coordinate information of the object is used to control the joint motion of the robot to perform the grasping task. The experimental results show that the method is robust and requires a small amount of data, and it can grasp the target object with unknown height without teaching.