A Robot Collision Avoidance Method Using Kinect and Global Vision
Abstract: This paper
introduces a robot collision avoidance method using Kinect and global vision to
improve the industrial robot’s security. Global vision is installed above the
robot, and a combination of the background-difference method and the Otsu
algorithm are used. Human skeleton detection is then introduced to detect the
location information of the human body. The collided objects are classified
into nonhuman and human obstacle which is further categorized into the human
head and non-head areas such as the arm. The Kalman filter is used to predict
the human gesture. The human joints danger index is used to evaluate the risk
level of the human on the basis of human body joints and robot’s motion information.
Finally, a motion control strategy is adopted in view of obstacle categories
and the human joint danger index. Results show that the proposed method can
effectively improve robot’s security in real time.
Keywords: robot safety, human
body skeleton detection, Kalman filter, global vision, security control
Author: Haibin Wu, Jianfeng
Huang, Xiaoning Yang, Jinhua Ye, Sumei He
Journal Code: jptkomputergg170102
