A Robot Collision Avoidance Method Using Kinect and Global Vision
Haibin Wu, Jianfeng Huang, Xiaoning Yang, Jinhua Ye, Sumei He
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.
robot safety; human body skeleton detection; kalman filter; global vision; security control