Research Article
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Development of a Data Clustering System for 2DOF Robotic Ball Balancer Using Laser Scanning RangeFinder

Year 2021, Volume: 25 Issue: 1, 72 - 82, 01.02.2021
https://doi.org/10.16984/saufenbilder.726455

Abstract

In this study, a new perspective for developing laser scanner rangefinder based data clustering system for a 2DOF robotic ball balancer was proposed. The study focused on detecting an object (i.e., ball) on the tilt-table robotic platform using the sensor fusion and data clustering systems proposed. Clustering system was modeled by following the principles of hierarchical clustering method. The developed system involving the clustering and sensor fusion algorithms was embedded in Matlab-Simulink environment to be able to run in real-time applications. The system was tested using an experimental platform including a 2DOF robotic ball balancer equipped with high resolution encoders and a laser scanner rangefinder. In the experiments, the goal was to detect the ball and its position not only on the flat but also on the tilted platform. A camera was also attached to the top of the experimental setup and used to monitor the location of the ball on the platform. By this way the results obtained using the proposed system could be verified for accuracy, performance and repeatability issues.

Supporting Institution

Zonguldak Bulent Ecevit University

Project Number

2013-77654622-03

Thanks

The author thanks to the infrastructure project of the Mechanical Engineering Department of Zonguldak Bulent Ecevit University (Zonguldak, Turkey) numbered 2013-77654622-03 for providing the equipment of 2DOF robotic ball balancer and laser scanner rangefinder used in this research.

References

  • [1] N. Wouw, M. N. Heuvel, H. Nijmeijer, J. A. Rooij, “Performance of an automatic ball balancer with dry friction”, International Journal of Bifurcation and Chaos, 15(1), 65-82, 2005.
  • [2] K. Green, A. R. Champneys, M. I. Friswelland, A. M. Munoz, “Investigation of a multi-ball, automatic dynamic balancing mechanism for eccentric rotors”, Phil. Trans. R. Soc. A, 366, 705–728, 2008.
  • [3] J. Cui, H. Zha, H. Zhao, R. Shibasaki, “Laser-based detection and tracking of multiple people in crowds”, Computer Vision and Image Understanding, 106, 300–312, 2007.
  • [4] C. Feng, Y. Taguchi, V. R. Kamat, “Fast plane extraction in organized point clouds using agglomerative hierarchical clustering”, In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, 6218-6225, 2014.
  • [5] A. Lipnickas, K. Rimkus, S. Sinkevicius, “Design of 3D scene scanner for slat surface detection”, Issues and Challenges in Artificial Intelligence, Switzerland: Springer International Publishing, 2014.
  • [6] S. Kim, T. Hinckley, D. Briggs, “Classifying individual tree genera using stepwise cluster analysis based on height and intensity metrics derived from airborne laser scanner data”, Remote Sensing of Environment, 115, 3329–3342, 2011.
  • [7] T. Sasaki, H. Tamura, H. Hashimoto, F. Inoue, “Position estimation based on the target shape information using laser range finders for intelligent space”, In: Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Montreal, Canada, 605-610, 2010.
  • [8] P. Nunez, P. Drews, A. Bandera, R. Rocha, M. Campos, J. Dias, “Change detection in 3D environments based on Gaussian mixture model and robust structural matching for autonomous robotic applications”, In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, 2633-2638, 2010.
  • [9] T. Tsuji, H. Zha, T. Hasegawa, R. Kur, “Hierarchical face cluster partitioning of polygonal surfaces and high-speed rendering”, Systems and Computers in Japan, 38(8), 1205–1215, 2007.
  • [10] K. O. Arras, N. Tomatis, “Improving robustness and precision in mobile robot localization by using laser range finding and monocular vision”, In: Proceedings of the Third European Workshop on Advanced Mobile Robots (Eurobot99), Zurich, 177-185, 1999.
  • [11] A. Azim, O. Aycard, “Detection, classification and tracking of moving objects in a 3D environment”, In: Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Alcala de Henares, 802-807, 2012.
  • [12] Q. Chen, P. Gong, D. Baldocchi, G. Xie, “Filtering airborne laser scanning data with morphological methods”, Photogrammetric Engineering & Remote Sensing, 73(2), 175–185, 2007.
  • [13] C. Fraley, A. E. Raftery, “How many clusters? which clustering method? answers via model-based cluster analysis”, Technical Report No. 329, Department of Statistics University of Washington, 1998.
  • [14] J. Han, M. Kamber, “Data mining: concepts and techniques”, USA: Morgan Kaufmann Publishers, 2001.
  • [15] L. Rokach, O. Maimon, “Clustering methods: data mining and knowledge discovery handbook, USA: Springer, 2005.
  • [16] P. Sneath, R. Sokal, “Numerical taxonomy”, San Francisco, CA, USA: W.H. Freeman Co., 1973.
  • [17] 2-DOF ball balancer Workbook and User Manual, Quanser Inc., 2013.
Year 2021, Volume: 25 Issue: 1, 72 - 82, 01.02.2021
https://doi.org/10.16984/saufenbilder.726455

Abstract

Project Number

2013-77654622-03

References

  • [1] N. Wouw, M. N. Heuvel, H. Nijmeijer, J. A. Rooij, “Performance of an automatic ball balancer with dry friction”, International Journal of Bifurcation and Chaos, 15(1), 65-82, 2005.
  • [2] K. Green, A. R. Champneys, M. I. Friswelland, A. M. Munoz, “Investigation of a multi-ball, automatic dynamic balancing mechanism for eccentric rotors”, Phil. Trans. R. Soc. A, 366, 705–728, 2008.
  • [3] J. Cui, H. Zha, H. Zhao, R. Shibasaki, “Laser-based detection and tracking of multiple people in crowds”, Computer Vision and Image Understanding, 106, 300–312, 2007.
  • [4] C. Feng, Y. Taguchi, V. R. Kamat, “Fast plane extraction in organized point clouds using agglomerative hierarchical clustering”, In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, 6218-6225, 2014.
  • [5] A. Lipnickas, K. Rimkus, S. Sinkevicius, “Design of 3D scene scanner for slat surface detection”, Issues and Challenges in Artificial Intelligence, Switzerland: Springer International Publishing, 2014.
  • [6] S. Kim, T. Hinckley, D. Briggs, “Classifying individual tree genera using stepwise cluster analysis based on height and intensity metrics derived from airborne laser scanner data”, Remote Sensing of Environment, 115, 3329–3342, 2011.
  • [7] T. Sasaki, H. Tamura, H. Hashimoto, F. Inoue, “Position estimation based on the target shape information using laser range finders for intelligent space”, In: Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Montreal, Canada, 605-610, 2010.
  • [8] P. Nunez, P. Drews, A. Bandera, R. Rocha, M. Campos, J. Dias, “Change detection in 3D environments based on Gaussian mixture model and robust structural matching for autonomous robotic applications”, In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, 2633-2638, 2010.
  • [9] T. Tsuji, H. Zha, T. Hasegawa, R. Kur, “Hierarchical face cluster partitioning of polygonal surfaces and high-speed rendering”, Systems and Computers in Japan, 38(8), 1205–1215, 2007.
  • [10] K. O. Arras, N. Tomatis, “Improving robustness and precision in mobile robot localization by using laser range finding and monocular vision”, In: Proceedings of the Third European Workshop on Advanced Mobile Robots (Eurobot99), Zurich, 177-185, 1999.
  • [11] A. Azim, O. Aycard, “Detection, classification and tracking of moving objects in a 3D environment”, In: Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Alcala de Henares, 802-807, 2012.
  • [12] Q. Chen, P. Gong, D. Baldocchi, G. Xie, “Filtering airborne laser scanning data with morphological methods”, Photogrammetric Engineering & Remote Sensing, 73(2), 175–185, 2007.
  • [13] C. Fraley, A. E. Raftery, “How many clusters? which clustering method? answers via model-based cluster analysis”, Technical Report No. 329, Department of Statistics University of Washington, 1998.
  • [14] J. Han, M. Kamber, “Data mining: concepts and techniques”, USA: Morgan Kaufmann Publishers, 2001.
  • [15] L. Rokach, O. Maimon, “Clustering methods: data mining and knowledge discovery handbook, USA: Springer, 2005.
  • [16] P. Sneath, R. Sokal, “Numerical taxonomy”, San Francisco, CA, USA: W.H. Freeman Co., 1973.
  • [17] 2-DOF ball balancer Workbook and User Manual, Quanser Inc., 2013.
There are 17 citations in total.

Details

Primary Language English
Subjects Mechanical Engineering
Journal Section Research Articles
Authors

Gokhan Bayar 0000-0002-6344-3621

Project Number 2013-77654622-03
Publication Date February 1, 2021
Submission Date April 24, 2020
Acceptance Date November 4, 2020
Published in Issue Year 2021 Volume: 25 Issue: 1

Cite

APA Bayar, G. (2021). Development of a Data Clustering System for 2DOF Robotic Ball Balancer Using Laser Scanning RangeFinder. Sakarya University Journal of Science, 25(1), 72-82. https://doi.org/10.16984/saufenbilder.726455
AMA Bayar G. Development of a Data Clustering System for 2DOF Robotic Ball Balancer Using Laser Scanning RangeFinder. SAUJS. February 2021;25(1):72-82. doi:10.16984/saufenbilder.726455
Chicago Bayar, Gokhan. “Development of a Data Clustering System for 2DOF Robotic Ball Balancer Using Laser Scanning RangeFinder”. Sakarya University Journal of Science 25, no. 1 (February 2021): 72-82. https://doi.org/10.16984/saufenbilder.726455.
EndNote Bayar G (February 1, 2021) Development of a Data Clustering System for 2DOF Robotic Ball Balancer Using Laser Scanning RangeFinder. Sakarya University Journal of Science 25 1 72–82.
IEEE G. Bayar, “Development of a Data Clustering System for 2DOF Robotic Ball Balancer Using Laser Scanning RangeFinder”, SAUJS, vol. 25, no. 1, pp. 72–82, 2021, doi: 10.16984/saufenbilder.726455.
ISNAD Bayar, Gokhan. “Development of a Data Clustering System for 2DOF Robotic Ball Balancer Using Laser Scanning RangeFinder”. Sakarya University Journal of Science 25/1 (February 2021), 72-82. https://doi.org/10.16984/saufenbilder.726455.
JAMA Bayar G. Development of a Data Clustering System for 2DOF Robotic Ball Balancer Using Laser Scanning RangeFinder. SAUJS. 2021;25:72–82.
MLA Bayar, Gokhan. “Development of a Data Clustering System for 2DOF Robotic Ball Balancer Using Laser Scanning RangeFinder”. Sakarya University Journal of Science, vol. 25, no. 1, 2021, pp. 72-82, doi:10.16984/saufenbilder.726455.
Vancouver Bayar G. Development of a Data Clustering System for 2DOF Robotic Ball Balancer Using Laser Scanning RangeFinder. SAUJS. 2021;25(1):72-8.