No Clamp Robotic Assembly with Use of Point Cloud Data from Low-Cost Triangulation Scanner

The paper shows the clamp-less assembly idea as a very important one in the modern assembly. Assembly equipment such as clamps represent a significant group of industrial equipment in manufacturing plants whose number can be effectively reduced. The article presents the concept of using industrial robot equipped with a triangulation scanner in the assembly process in order to minimize the number of clamps that hold the units in a particular position in space. It also shows how the system searches for objects in the point cloud based on multi-step processing algorithm proposed in this work, then picks them up, transports and positions them in the right assembly locations with the use of industrial robot manipulator. The accuracy of the positioning of parts was also examined as well as the impact of the number of iterations of the algorithm searching the models in the point cloud on the accuracy of determining the position of the objects. The tests show that presented system is suitable for assembly of various items as plastic packaging and palletizing of products. Such kind of system is the basis for modern, fully flexible assembly systems.

[1]  Siddhartha S. Srinivasa,et al.  The MOPED framework: Object recognition and pose estimation for manipulation , 2011, Int. J. Robotics Res..

[2]  Krzysztof Okarma,et al.  The 3D scanning system for the machine vision based positioning of workpieces on the CNC machine tools , 2012, 2012 17th International Conference on Methods & Models in Automation & Robotics (MMAR).

[3]  B. Gurumoorthy,et al.  AIWIN—a fast and jigless inspection technique for machined parts , 2012 .

[4]  Jonathan Salvatore De Mattia,et al.  On the tribological behavior of retrieved hip femoral heads affected by metallic debris. A comparative investigation by stylus and optical profilometer for a new roughness measurement protocol , 2016 .

[5]  Radu Bogdan Rusu,et al.  3D is here: Point Cloud Library (PCL) , 2011, 2011 IEEE International Conference on Robotics and Automation.

[6]  Sebastian Zug,et al.  Are laser scanners replaceable by Kinect sensors in robotic applications? , 2012, 2012 IEEE International Symposium on Robotic and Sensors Environments Proceedings.

[7]  Stanislaw Legutko,et al.  Investigations Into Surface Integrity in the Turning Process of Duplex Stainless Steel , 2014 .

[8]  Stanislaw Legutko,et al.  EXPERIMENTAL ANALYSIS BY MEASUREMENT OF SURFACE ROUGHNESS VARIATIONS IN TURNING PROCESS OF DUPLEX STAINLESS STEEL , 2014 .

[9]  Richard A. Wysk,et al.  Automatic part localization in a CNC machine coordinate system by means of 3D scans , 2015 .

[10]  François Blais.  Review of 20 years of range sensor development , 2004, J. Electronic Imaging.

[11]  Seokbae Son,et al.  Automated laser scanning system for reverse engineering and inspection , 2002 .

[12]  Darius Burschka,et al.  An Efficient RANSAC for 3D Object Recognition in Noisy and Occluded Scenes , 2010, ACCV.

[13]  Sören Larsson,et al.  Motion control and data capturing for laser scanning with an industrial robot , 2006, Robotics Auton. Syst..

[14]  G. Królczyk,et al.  Experimental studies of the cutting force and surface morphology of explosively clad Ti–steel plates , 2016 .

[15]  Jae-Bok Song,et al.  Force control based jigless assembly strategy of a unit box using dual-arm and friction , 2013, IEEE ISR 2013.

[16]  Li Qi,et al.  Laser cutting of irregular shape object based on stereo vision laser galvanometric scanning system , 2015 .

[17]  Stanislaw Legutko,et al.  Metrological changes in surface morphology of high-strength steels in manufacturing processes , 2016 .

[18]  Henrik Gordon Petersen,et al.  Pose estimation using local structure-specific shape and appearance context , 2013, 2013 IEEE International Conference on Robotics and Automation.

[20]  Dariusz Mika,et al.  DIAGNOSTICS OF WORKPIECE SURFACE CONDITION BASED ON CUTTING TOOL VIBRATIONS DURING MACHINING , 2015 .

[21]  Stanislaw Legutko,et al.  Modelling of assembly sequences using hypergraph and directed graph , 2014 .

[22]  Julian D Booker,et al.  Designing for assembly quality: strategies, guidelines and techniques , 2005 .

[23]  Ales Leonardis,et al.  Incremental PCA for on-line visual learning and recognition , 2002, Object recognition supported by user interaction for service robots.

[24]  Reinhard Klein,et al.  Efficient RANSAC for Point‐Cloud Shape Detection , 2007, Comput. Graph. Forum.

[25]  Darius Burschka,et al.  Rigid 3D geometry matching for grasping of known objects in cluttered scenes , 2012, Int. J. Robotics Res..

[26]  Geoffrey Boothroyd,et al.  Product design for manufacture and assembly , 1994, Comput. Aided Des..

[27]  Pedro Neto,et al.  A low-cost laser scanning solution for flexible robotic cells: spray coating , 2012, ArXiv.