全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
Machines  2013 

Six-Degrees-of-Freedom (6-DOF) Work Object Positional Calibration Using a Robot-Held Proximity Sensor

DOI: 10.3390/machines1020063

Keywords: positional calibration, industrial robot, proximity sensor, manufacturing automation, electric machine assembly

Full-Text   Cite this paper   Add to My Lib

Abstract:

Industrial automation has been recognized as a fundamental key to build and keep manufacturing industries in developed countries. In most automation tasks, knowing the exact position of the objects to handle is essential. This is often done using a positional calibration system, such as a camera-based vision system. In this article, an alternative six-degrees-of-freedom work object positional calibration method using a robot-held proximity sensor, is presented. A general trigonometry-based measurement and calculation procedure, which, step-by-step, adjusts a work object coordinate system to the actual work object position, is explained. For suitable robot tasks and work object geometries, the benefits with the presented method include its robustness, large work area and low investment cost. Some drawbacks can be longer cycle time and its limited capacity to handle unsorted and complicated objects. To validate the presented method, it was implemented in an experimental robot setup. In this robot cell, it was used to calibrate the position of a stator section work object, which is used in the Uppsala University Wave Energy Converter generator. Hereby the function of the positional calibration procedure was validated. Sufficient positioning accuracy for the stator winding task was achieved and theoretically validated based on the experiments.

References

[1]  Bogue, R. Europe fights back with advanced manufacturing and assembly technologies. Assem. Autom. 2012, 32, 312–317, doi:10.1108/01445151211262366.
[2]  Reed, W.D. Self-Bonding Wire in Automated Motor Assembly. In Proceedings of the Electrical Insulation Conference and Electrical Manufacturing & Coil Winding Technology Conference, Indianapolis, IN, USA, 23–25 September 2003; pp. 579–581.
[3]  Alkkiom?ki, O.; Kyrki, V.; Liu, Y.; Handroos, H. Multi-Modal Force/Vision Sensor Fusion in 6-DOF Pose Tracking. In Proceedings of the International Conference on Advanced Robotics, Munich, Germany, 22–26 June 2009; pp. 1–8.
[4]  Chen, S.; Li, Y.; Ming, K.N. Active vision in robotic systems: A survey of recent developments. Int. J. Robot Res. 2012, 30, 1343–1377, doi:10.1177/0278364911410755.
[5]  Kubota, T.; Aiyama, Y. Calibration of Relative Position between Manipulator and Work by Point-To-Face Touching Method. In Proceedings of the 2009 IEEE International Symposium on Assembly and Manufacturing, Suwon, Korea, 17–20 November 2009; pp. 286–290.
[6]  Lin, S.T.; Huang, A.K. Hierarchical fuzzy force control for industrial robots. IEEE Trans. Ind. Electron. 1998, 45, 646–653, doi:10.1109/41.704894.
[7]  Liu, W.; Chen, T.; Wang, P.; Qiao, H. Pose estimation for 3D workpiece grasping in industrial environment based on evolutionary algorithm. J. Intell. Robot Syst. 2012, 68, 293–306, doi:10.1007/s10846-012-9686-5.
[8]  Maykol, P.A.; Rocha, L.F.; Paulo, M.A. Object recognition using laser range finder and machine learning techniques. Robot Cim-Int. Manuf. 2013, 29, 12–22.
[9]  Greggio, N.; Bernardino, A.; Laschi, C.; Santos-Victor, J.; Dario, P. Real-time 3D stereo tracking and localizing of spherical objects with the iCub robotic platform. J. Intell. Robot Syst. 2011, 63, 417–446, doi:10.1007/s10846-010-9527-3.
[10]  Ramisa, A.; Aldavert, D.; Vasudevan, S.; Toledo, R.; Lopez de Mantaras, R. Evaluation of three vision based object perception methods for a mobile robot. J. Intell. Robot Syst. 2012, 68, 185–208, doi:10.1007/s10846-012-9675-8.
[11]  Boochs, F.; Schutze, R.; Simon, C.; Marzani, F. Increasing the Accuracy of Untaught Robot Positions by Means of a Multi-Camera System. In Proceedings of the 2010 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Zurich, Switzerland, 15–17 September 2010; pp. 1–9.
[12]  Nubiola, A.; Bonev, I.A. Absolute calibration of an ABB IRB 1600 robot using a laser tracker. Robot Cim-Int. Manuf. 2013, 29, 236–245, doi:10.1016/j.rcim.2012.06.004.
[13]  Akita, H.; Nakahara, Y.; Miyake, N.; Oikawa, T. New Core Structure and Manufacturing Method for High Efficiency of Permanent Magnet Motors. In Proceedings of the 38th Industry Applications Conference, Salt Lake City, UT, USA, 12–16 October 2003; Volume 1, pp. 367–372.
[14]  Franke, J.; Dobroschke, A.; Tremel, J. Innovative Processes and Systems for the Automated Manufacturing, Assembly and Test of Magnetic Components for Electric Motors. In Proceedings of the 2011 1st International Electric Drives Production Conference (EDPC), Nuremberg, Germany, 28–29 September 2011; pp. 228–234.
[15]  Kirkhoff, J. A new level of automation in fraction HP (kW) electric motor manufacturing. Assem. Autom. 2004, 24, 159–161, doi:10.1108/01445150410529937.
[16]  Danielsson, O.; Eriksson, M.; Leijon, M. Study of a longitudinal flux permanent magnet linear generator for wave energy converters. Int. J. Energ. Res. 2006, 30, 1130–1145, doi:10.1002/er.1209.
[17]  Lejerskog, E.; Gravr?kmo, H.; Savin, A.; Str?mstedt, E.; Tyrberg, S.; Haikonen, K.; Krishna, R.; Bostr?m, C.; Rahm, M.; Ekstr?m, R.; et al. Lysekil research site, Sweden: Status update. In Proceedings of the 9th European Wave and Tidal Energy Conference, Southampton, UK, 5–9 September; 2011.
[18]  Hultman, E.; Leijon, M. Utilizing cable winding and industrial robots to facilitate the manufacturing of electric machines. Robot Cim-Int. Manuf. 2013, 29, 246–256, doi:10.1016/j.rcim.2012.06.005.

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133