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Multi‐target detection and grasping control for humanoid robot NAO
Author(s) -
Zhang Lei,
Zhang Huayan,
Yang Hanting,
Bian GuiBin,
Wu Wanqing
Publication year - 2019
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.3031
Subject(s) - grasp , humanoid robot , artificial intelligence , computer vision , computer science , robot , trajectory , object (grammar) , robot control , mobile robot , physics , astronomy , programming language
Summary Graspirng objects is an important capability for humanoid robots. Due to complexity of environmental and diversity of objects, it is difficult for the robot to accurately recognize and grasp multiple objects. In response to this problem, we propose a robotic grasping method that uses the deep learning method You Only Look Once v3 for multi‐target detection and the auxiliary signs to obtain target location. The method can control the movement of the robot and plan the grasping trajectory based on visual feedback information. It is verified by experiments that this method can make the humanoid robot NAO grasp the object effectively, and the success rate of grasping can reach 80% in the experimental environment.