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Near Real-Time Robotic Grasping of Novel Objects in Cluttered Scenes

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Advances in Computer Vision (CVC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 944))

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Abstract

In this paper, we investigate the problem of grasping novel objects in unstructured environments. Object geometry, reachability, and force closure analysis are considered to address this problem. A framework is proposed for grasping unknown objects by localizing contact regions on the contours formed by a set of depth edges generated from a single view 2D depth image. Specifically, contact regions are determined based on edge geometric features derived from analysis of the depth map data. Finally, the performance of the approach is successfully validated by applying it to the scenes with both single and multiple objects, in both MATLAB simulation and experiments using a Kinect One sensor and a Baxter manipulator.

This study was funded in part by NIDILRR grant #H133G120275, and in part by NSF grant numbers IIS-1409823 and IIS-1527794. However, these contents do not necessarily represent the policy of the aforementioned funding agencies, and you should not assume endorsement by the Federal Government.

A. Jabalameli and N. Ettehadi are graduate students with the Electrical and Computer Engineering Dept. at the University of Central Florida (UCF), Orlando, FL 32816.

A. Behal is with ECE and NanoScience Technology Center at UCF, Orlando, FL.

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References

  1. Bohg, J., Morales, A., Asfour, T., Kragic, D.: Data-driven grasp synthesis–a survey. IEEE Trans. Robot. 30(2), 289–309 (2014)

    Article  Google Scholar 

  2. Sahbani, A., El-Khoury, S., Bidaud, P.: An overview of 3D object grasp synthesis algorithms. Robot. Auton. Syst. 60(3), 326–336 (2012)

    Article  Google Scholar 

  3. Nguyen, V.D.: Constructing force-closure grasps. In: Proceedings of the 1986 IEEE International Conference on Robotics and Automation, pp. 1368–1373 (1986)

    Google Scholar 

  4. Miller, A.T., Knoop, S., Christensen, H.I., Allen, P.K.: Automatic grasp planning using shape primitives. In: Proceedings of the IEEE International Conference Robotics and Automation, pp. 1824–1829 (2003)

    Google Scholar 

  5. Hübner, K., Kragic, D.: Selection of robot pre-grasps using box-based shape approximation. In: Proceedings of the IEEE/RSJ International Conference Intelligent Robots System, pp. 1765–1770 (2008)

    Google Scholar 

  6. Przybylski, M., Asfour, T., Dillmann, R.: Planning grasps for robotic hands using a novel object representation based on the medial axis transform. In: Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1781–1788, September 2011

    Google Scholar 

  7. Saxena, A., Driemeyer, J., Ng, A.Y.: Robotic grasping of novel objects using vision. Int. J. Robot. Res. 27(2), 157–173 (2008)

    Article  Google Scholar 

  8. ten Pas, A., Platt, R.: Using geometry to detect grasp poses in 3D point clouds. In: International Symposium on Robotics Research (2015)

    Google Scholar 

  9. Teng, Z., Xiao, J.: Surface-based detection and 6-DoF pose estimation of 3-D objects in cluttered scenes. IEEE Trans. Robot. 32(6), 1347–1361 (2016)

    Article  Google Scholar 

  10. Ückermann, A., Haschke, R., Ritter, H.: Realtime 3D segmentation for human-robot interaction. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, pp. 2136–2143 (2013)

    Google Scholar 

  11. Suzuki, T., Oka, T.: Grasping of unknown objects on a planar surface using a single depth image. In: 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Banff, AB, pp. 572–577 (2016)

    Google Scholar 

  12. Parkhurst, E.L., Rupp, M.A., Jabalameli, A., Behal, A., Smither, J.A.: Compensations for an assistive robotic interface. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 61(1), 1793–1793 (2017)

    Article  Google Scholar 

  13. Rahmatizadeh, R., Abolghasemi, P., Boloni, L., Jabalameli, A., Behal, A.: Trajectory adaptation of robot arms for head-pose dependent assistive tasks. In: FLAIRS Conference (2016)

    Google Scholar 

  14. Ettehadi, N., Behal, A.: Implementation of feeding task via learning from demonstration. In: 2018 Second IEEE International Conference on Robotic Computing (IRC), Laguna Hills, CA, pp. 274–277 (2018)

    Google Scholar 

  15. Stork, A.: Representation and learning for robotic grasping, caging, and planning. Ph.D. dissertation, Stockholm (2016)

    Google Scholar 

  16. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI–8(6), 679–698 (1986)

    Article  Google Scholar 

  17. Richtsfeld, A., Mörwald, T., Prankl, J., Zillich, M., Vincze, M.: Segmentation of unknown objects in indoor environments. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura, pp. 4791–4796 (2012)

    Google Scholar 

  18. Kovesi, P.D.: MATLAB and Octave functions for computer vision and image processing (2000). http://www.peterkovesi.com/matlabfns

  19. Balasubramanian, R., Xu, L., Brook, P.D., Smith, J.R., Matsuoka, Y.: Human-guided grasp measures improve grasp robustness on physical robot. In: 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, pp. 2294–2301 (2010)

    Google Scholar 

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Authors and Affiliations

  1. University of Central Florida, Orlando, FL, 32826, USA

    Amirhossein Jabalameli, Nabil Ettehadi & Aman Behal

Authors
  1. Amirhossein Jabalameli
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  2. Nabil Ettehadi
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  3. Aman Behal
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Corresponding author

Correspondence to Aman Behal .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Jabalameli, A., Ettehadi, N., Behal, A. (2020). Near Real-Time Robotic Grasping of Novel Objects in Cluttered Scenes. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-17798-0_28

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