TY - GEN
T1 - Visual tracking and grasping of moving objects and its application to an industrial robot
AU - Zhao, Zhou
AU - Huang, Panfeng
AU - Chen, Lu
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - In industrial robots, the use of vision sensors is indispensable, which can enhance the robot's industrial intelligence and better help the industrial robot accomplish tasks. But achieving the accuracy and time efficiency together still is a challenge in industrial tasks. In this paper, a new method is presented to track and grasp the moving object. First, the high-resolution depth and RGB sensing are acquired by Kinect v2. Next, a tracking algorithm of improved spatio-temporal context is applied to tracking the moving object, and the gripper position in the base coordinate system is calculated. To accomplish grasping tasks, a linear prediction method is applied to predict the trajectory of the moving object in three dimension space, and the distance between the moving object and the gripper are constantly decreased by a simple grasping strategy. Finally, the tracking system based on the industrial robot is set up in our laboratory. The effectiveness of the proposed method is verified.
AB - In industrial robots, the use of vision sensors is indispensable, which can enhance the robot's industrial intelligence and better help the industrial robot accomplish tasks. But achieving the accuracy and time efficiency together still is a challenge in industrial tasks. In this paper, a new method is presented to track and grasp the moving object. First, the high-resolution depth and RGB sensing are acquired by Kinect v2. Next, a tracking algorithm of improved spatio-temporal context is applied to tracking the moving object, and the gripper position in the base coordinate system is calculated. To accomplish grasping tasks, a linear prediction method is applied to predict the trajectory of the moving object in three dimension space, and the distance between the moving object and the gripper are constantly decreased by a simple grasping strategy. Finally, the tracking system based on the industrial robot is set up in our laboratory. The effectiveness of the proposed method is verified.
KW - industrial robotic applications
KW - kalman filter
KW - kinect v2
KW - Visual tracking
UR - https://www.scopus.com/pages/publications/85050595530
U2 - 10.1109/RCAR.2017.8311921
DO - 10.1109/RCAR.2017.8311921
M3 - 会议稿件
AN - SCOPUS:85050595530
T3 - 2017 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2017
SP - 555
EP - 560
BT - 2017 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2017
Y2 - 14 July 2017 through 18 July 2017
ER -