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Geometrically Finding Best Grasping Points on Single Novel 3D Point Cloud

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Informatics in Control, Automation and Robotics (ICINCO 2017)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 495))

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Abstract

The task of robotic grasping brings together several challenges. Among them, we focus on the calculus of where gripper plates should be placed over an object’s surface in order to grasp it. To do this, we have developed a method based on visual information. The main goal is to geometrically analyse a single 3D point cloud, where the object is present, to find the best pair of contact points so a gripper can perform a stable grasp of the object. Our proposal is to find these points near a perpendicular cutting plane to the object’s main axis through its centroid. We have found that this method shows promising experimental results fast and accurate enough to be used on real service robots.

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References

  1. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981). https://doi.org/10.1145/358669.358692. http://portal.acm.org/citation.cfm?doid=358669.358692

  2. Gil, P., Mezouar, Y., Vincze, M., Corrales, J.A.: Editorial: robotic perception of the sight and touch to interact with environments. J. Sens. 2016(Article ID 1751205), 1–2 (2016). https://doi.org/10.1155/2016/1751205

  3. Jain, S., Argall, B.: Grasp detection for assistive robotic manipulation. In: Proceedings - IEEE International Conference on Robotics and Automation 2016-June, 2015–2021 (2016). https://doi.org/10.1109/ICRA.2016.7487348

  4. Jiang, Y., Moseson, S., Saxena, A.: Efficient grasping from RGBD images: Learning using a new rectangle representation. In: Proceedings - IEEE International Conference on Robotics and Automation, pp. 3304–3311 (2011). https://doi.org/10.1109/ICRA.2011.5980145

  5. Lenz, I., Lee, H., Saxena, A.: Deep learning for detecting robotic grasps. Int. J. Robot. Res. 34(4–5), 705–724 (2013). https://doi.org/10.1177/0278364914549607. http://arxiv.org/abs/1301.3592

  6. Miller, A.T., Allen, P.K.: GraspIt!. IEEE Robot. Autom. Mag. 11(4), 110–122 (2004). https://doi.org/10.1109/MRA.2004.1371616. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1371616

  7. ten Pas, A., Gualtieri, M., Saenko, K., Platt, R.: Grasp pose detection in point clouds. arXiv preprint (2017). https://doi.org/10.1177/ToBeAssigned

  8. ten Pas, A., Platt, R.: Using geometry to detect grasps poses in 3D point clouds. Int. Symp. Robot. Res. (2015). http://arxiv.org/abs/1501.03100

  9. ten Pas, A., Platt, R.: Localizing handle-like grasp affordances in 3D point clouds. Springer Tracts Adv. Robot. 109, 623–638 (2016). https://doi.org/10.1007/978-3-319-23778-7_41

    Article  Google Scholar 

  10. Pauly, M., Gross, M., Kobbelt, L.: Efficient simplification of point-sampled surfaces. In: 13th IEEE Visualization Conference (Section 4), pp. 163–170 (2002). https://doi.org/10.1109/VISUAL.2002.1183771

  11. Redmon, J., Angelova, A.: Real-time grasp detection using convolutional neural networks. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 1316–1322 (2015). https://doi.org/10.1109/ICRA.2015.7139361. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7139361

  12. Richtsfeld, M., Vincze, M.: Grasping of unknown objects from a table top. In: Workshop on Vision in Action: Efficient Strategies for Cognitive Agents in Complex Environments (2008). http://hal.inria.fr/inria-00325794/en/

  13. Rusu, R.B.: Semantic 3D object maps for everyday manipulation in human living environments. KI - Kunstliche Intelligenz 24, 345–348 (2010). https://doi.org/10.1007/s13218-010-0059-6

    Article  Google Scholar 

  14. Rusu, R.B., Cousins, S.: 3D is here: Point cloud library (PCL). In: Proceedings - IEEE International Conference on Robotics and Automation (2011). https://doi.org/10.1109/ICRA.2011.5980567

  15. Townsend, W.: The BarrettHand grasper-programmably flexible part handling and assembly. Int. J. Ind. Robot. 27(3), 181–188 (2000). https://doi.org/10.1108/01439910010371597

    Article  Google Scholar 

  16. Trottier, L., Giguère, P., Chaib-draa, B.: Dictionary learning for robotic grasp recognition and detection. 1–19 (2016). http://arxiv.org/abs/1606.00538

  17. Vahrenkamp, N., Westkamp, L., Yamanobe, N., Aksoy, E.E., Asfour, T.: Part-based grasp planning for familiar objects. In: 2016 IEEE-RAS International Conference on Humanoid Robots (Humanoids 2016) (2016). https://doi.org/10.1109/HUMANOIDS.2016.7803382

  18. Varley, J., Weisz, J., Weiss, J., Allen, P.: Generating multi-fingered robotic grasps via deep learning. In: IEEE International Conference on Intelligent Robots and Systems 2015-December, pp. 4415–4420 (2015). https://doi.org/10.1109/IROS.2015.7354004

  19. Wang, Z., Li, Z., Wang, B., Liu, H.: Robot grasp detection using multimodal deep convolutional neural networks. Adv. Mech. Eng. 8(9), 1–12 (2016). https://doi.org/10.1177/1687814016668077. http://ade.sagepub.com/lookup/doi/10.1177/1687814016668077

  20. Zapata-Impata, B.S., Mateo, C.M., Gil, P., Pomares, J.: Using geometry to detect grasping points on 3D unknown point cloud. In: 14th International Conference on Informatics in Control, Automation and Robotics, vol. 2, pp. 154–161 (2017). https://doi.org/10.5220/0006470701540161

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Acknowledgements

This work was funded by the Spanish Government Ministry of Economy, Industry and Competitiveness through the project DPI2015-68087-R and the predoctoral grant BES-2016-078290.

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Authors and Affiliations

  1. Dept. of Physics, System Engineering and Signal Theory, University of Alicante, 03690, Alicante, Spain

    Brayan S. Zapata-Impata, Pablo Gil & Jorge Pomares

Authors
  1. Brayan S. Zapata-Impata
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  2. Pablo Gil
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  3. Jorge Pomares
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Corresponding author

Correspondence to Brayan S. Zapata-Impata .

Editor information

Editors and Affiliations

  1. Ford Research and Advanced Engineering, Dearborn, MI, USA

    Oleg Gusikhin

  2. University of Paris-EST Créteil (UPEC), Vitry sur Seine, France

    Kurosh Madani

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Zapata-Impata, B.S., Gil, P., Pomares, J. (2020). Geometrically Finding Best Grasping Points on Single Novel 3D Point Cloud. In: Gusikhin, O., Madani, K. (eds) Informatics in Control, Automation and Robotics . ICINCO 2017. Lecture Notes in Electrical Engineering, vol 495. Springer, Cham. https://doi.org/10.1007/978-3-030-11292-9_25

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