ROS-Industrial based robotic cell for Industry 4.0: Eye-in-hand stereo camera and visual servoing for flexible, fast, and accurate picking and hooking in the line

被引:33
作者
D'Avella, Salvatore [1 ]
Avizzano, Carlo Alberto [1 ]
Tripiccho, Paolo [1 ]
机构
[1] Scuola Super Sant Anna, Dept Excellence Robot & AI, Pisa, Italy
关键词
Flexible manufacturing; Visual servoing; Machine learning; ROS-Industrial; Industry; 4; 0; RECOGNITION;
D O I
10.1016/j.rcim.2022.102453
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The fourth industrial revolution envisages the use of modern smart technologies to automate traditional manufacturing and industrial practices. However, industrial robots execute mostly pre-programmed jobs and are not able to face challenging tasks in unstructured environments. Industry 4.0 pushes for flexibility on target changes and autonomy. In line with the new principles of Industry 4.0, the proposed work describes an autonomous industrial cell that employs several smart technologies for loading jewelry pieces from a conveyor belt to a hooking frame built on purpose. The cell involves an industrial robot, a custom gripper, pneumatic and electric actuators with the aim of moving and opening the frame hooks, and a custom vision pipeline for detecting the feature of interest during the picking and hooking phases. The implemented pipeline makes use of a stereo camera pair mounted under the robot gripper and two fixed monocular cameras. The method employs HOG feature descriptors and machine learning algorithms for the detection. The software architecture is a component-based designed architecture that uses ROS as the underlying framework and ROS-Industrial packages to control the robot. The robot is controlled with position-based commands to reach intermediate positions in the workspace and with velocity command to implement a visual servoing control scheme that runs at 30 Hz and adjusts the robot position with the feedback of the vision during picking and hooking. The proposed visual servoing approach, thanks to the design of the stereo camera and choice of the optics, is able to perceive the features until the final movement phase, differently from most of the visual servoing employed in the literature that, due to the use of RGB-D camera or other vision apparatus, use an open control loop at a standoff distance. The presented work reaches an accuracy of 95% with a cycle time under 8 s.
引用
收藏
页数:12
相关论文
共 38 条
[1]  
[Anonymous], 2016, CoRR
[2]  
[Anonymous], 2017, Conference on robot learning
[3]   Reflective workpiece detection and localization for flexible robotic cells [J].
Astanin, Sergey ;
Antonelli, Dario ;
Chiabert, Paolo ;
Alletto, Chiara .
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2017, 44 :190-198
[4]   Safety assurance mechanisms of collaborative robotic systems in manufacturing [J].
Bi, Z. M. ;
Luo, Chaomin ;
Miao, Zhonghua ;
Zhang, Bing ;
Zhang, W. J. ;
Wang, Lihui .
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2021, 67
[5]   SUPPORT-VECTOR NETWORKS [J].
CORTES, C ;
VAPNIK, V .
MACHINE LEARNING, 1995, 20 (03) :273-297
[6]   Supervised stowing as enabling technology for the integration of impaired operators in the industry [J].
D'Avella, Salvatore ;
Tripicchio, Paolo .
30TH INTERNATIONAL CONFERENCE ON FLEXIBLE AUTOMATION AND INTELLIGENT MANUFACTURING (FAIM2021), 2020, 51 :171-178
[7]   A study on picking objects in cluttered environments: Exploiting depth features for a custom low-cost universal jamming gripper [J].
D'Avella, Salvatore ;
Tripicchio, Paolo ;
Avizzano, Carlo Alberto .
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2020, 63
[8]   Histograms of oriented gradients for human detection [J].
Dalal, N ;
Triggs, B .
2005 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOL 1, PROCEEDINGS, 2005, :886-893
[9]   In-hand recognition and manipulation of elastic objects using a servo-tactile control strategy [J].
Delgado, A. ;
Jara, C. A. ;
Torres, F. .
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2017, 48 :102-112
[10]  
Ekvall S, 2005, IEEE INT CONF ROBOT, P748