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Cloud-Based Task Planning for Smart Robots

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Intelligent Autonomous Systems 14 (IAS 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 531))

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Abstract

This paper proposes an Open Semantic Framework for knowledge acquisition of cognitive robots performing manipulation tasks. It integrates a Cloud-based Engine, which extracts discriminative features from the objects and generates their manipulation actions, and an Ontology, where the Engine saves data for future accesses. The Engine offloads robots by transferring computation on the Cloud. The Ontology favors knowledge sharing among manipulator robots by defining a common manipulation vocabulary. It extends the work proposed by the IEEE RAS Ontology for Robotics and Automation Working Group by covering the manipulation task domain. During ontological data insertion, data duplication is avoided by providing a novel efficient interlinking algorithm. During their retrieval, visual data processing is optimized by using a cascade hashing algorithm that intelligently accesses data. No training is required for object recognition and manipulation because of the adoption of a human-robot cooperation. The framework is based on the open-source Robot Operating System.

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Notes

  1. 1.

    Google, Inc. “Google App Engine”. Online: https://cloud.google.com/appengine/ (2014).

  2. 2.

    Resource Description Format (RDF). Online: http://www.w3.org/RDF.

  3. 3.

    Web Ontology Language (OWL). Online: http://www.w3g/TR/owl-features.

  4. 4.

    World Wide Web Consortium (W3C). Online: http://www.w3c.com.

  5. 5.

    Protégé. Online: http://protege.stanford.edu/.

  6. 6.

    Simple Protocol and RDF Query Language (SPARQL). Online: http://www.w3.org/TR/sparql11-query.

  7. 7.

    Apache Jena Fuseki. Online: https://jena.apache.org/documentation/fuseki2/index.html.

  8. 8.

    Chris Sweeney, Theia Multiview Geometry Library: Tutorial & Reference. Online: http://theia-sfm.org.

  9. 9.

    MoveIt! Simple Grasps tool. Online: https://github.com/davetcoleman/moveit_simple_grasps.

  10. 10.

    CloudLab. Online: http://www.cloudlab.us.

  11. 11.

    Object Segmentation Database. Online: http://www.acin.tuwien.ac.at/forschung/v4r/software-tools/osd/.

  12. 12.

    Ioan A. Sucan and Sachin Chitta, “MoveIt!”, Online: http://moveit.ros.org.

References

  1. Goldfeder, C., Allen, P.K.: Data-driven grasping. Auton. Robot. 31(1), 1–20 (2011)

    Article  Google Scholar 

  2. Dang, H., Allen, P.K.: Learning grasp stability. In: Proceedings of International Conference on Robotics and Automation (ICRA), pp. 2392–2397. Saint Paul, MN (2012)

    Google Scholar 

  3. Glover, J., Rus, D., Roy, N.: Probabilistic models of object geometry for grasp planning. In: Proceedings of Robotics Science and Systems (RSS), Zurich, Switzerland (2008)

    Google Scholar 

  4. Quigley, M., Conley, K., Gerkey, B.P., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software (2009)

    Google Scholar 

  5. Mohanarajah, G., Hunziker, D., D’Andrea, R., Waibel, M.: Rapyuta: a cloud robotics platform. IEEE Trans. Autom. Sci. Eng. 12(2), 481–493 (2015)

    Article  Google Scholar 

  6. Cheng, J., Leng, C., Wu, J., Cui, H., Lu, H.: Fast and accurate image matching with cascade hashing for 3d reconstruction. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)

    Google Scholar 

  7. Fan, Z.: Concise Pattern Learning for RDF Data Sets Interlinking. PhD thesis, University of Grenoble (2014)

    Google Scholar 

  8. Kaufman, L., Rousseeuw, P.J.: Statistical Data Analysis Based on the L1-Norm and Related Methods, chapter Clustering by means of Medoids. North-Holland, pp. 405–416 (1987)

    Google Scholar 

  9. Dubois, V., Quafafou, M.: Concept learning with approximation: rough version spaces. In: Rough Sets and Current Trends in Computing: Proceedings of the Third International Conference (RSCTC), Malvern, PA, USA, pp. 239–246 (2002)

    Google Scholar 

  10. Goldfeder, C., Ciocarlie, M., Allen, P.: The Columbia grasp dataset. In: Proceedings of International Conference on Robotics Automation (ICRA), Kobe, Japan, pp. 1710–1716 (2009)

    Google Scholar 

  11. Kasper, A., Xue, Z., Dillman, R.: The KIT object models database: an object model database for object recognition, localization and manipulation in service robotics. Int. J. Robot. Res. (IJRR) 31(8), 927–934 (2012)

    Article  Google Scholar 

  12. Ciocarlie, M., Hsiao, K., Jones, E., Chitta, S., Rusu, R., Sucan, I.: Towards reliable grasping and manipulation in household environements. In: Proceedings of International Symposium on Experimental Robotics, Delhi, India, pp. 1–12 (2010)

    Google Scholar 

  13. Tenorth, M., Beetz, M.: KnowRob: a knowledge processing infrastructure for cognition-enabled robots. Int. J. Robot. Res. (IJRR) 23(5), 566–590 (2013)

    Article  Google Scholar 

  14. Waibel, M., Beetz, M., Civera, J., D’Andrea, R., Elfring, J., Galvez-Lopez, D., Haussermann, K., Janssen, R., Montiel, J.M.M., Perzylo, A., Schiessle, B., Tenorth, M., Zweigle, O., van de Molengraft, R.: RoboEarth—a world wide web for robots. IEEE Robot. Autom. Mag. 18(2), 69–82 (2011)

    Article  Google Scholar 

  15. Di Marco, D., Koch, A., Zweigle, O., Haussermann, K., Schiessle, B., Levi, P., Galvez-Lopez, D., Riazuelo, L., Civera, J., Montiel, J.M.M., Tenorth,M., Perzylo, A., Waibel, M., Van de Molengraft, R.: Creating and using RoboEarth object models. IEEE Int. Conf. Robot. Autom. (ICRA) 3549–3550 (2012)

    Google Scholar 

  16. Prestes, E., Fiorini, S.R., Carbonera, J.: Core Ontology for Robotics and Automation. Standardized Knowledge Representation and Ontologies for Robotics and Automation. Workshop on the 18th, pp. 7–9. Illinois, USA, Chicago (2014)

    Google Scholar 

  17. Schlenoff, C., Prestes, E., Madhavan, R., Goncalves, P., Li, H., Balakirsky, S., Kramer, T., Miguelanez, E.: An IEEE standard ontology for robotics and automation. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1337–1342. Vilamoura, Algarve, Portugal (2012)

    Google Scholar 

  18. Schlenoff, C.I.: An overview of the IEEE ontology for robotics and automation (ORA) standardization effort. In: Workshop on the 18th, Standardized Knowledge Representation and Ontologies for Robotics and Automation, pp. 1–2. Illinois, USA, Chicago (2014)

    Google Scholar 

  19. Balakirsky, S., Kootbally, Z., T. RKramer, A. Pietromartire, C. Schlenoff, Gupta, S.: Knowledge driven robotics for kitting applications. Robot. Auton. Syst. 61(11), 1205–1214 (2013)

    Google Scholar 

  20. Fiorini, S.R., Carbonera, J.L., Gonçalves, P., Jorge, V.A., Rey, V.F., Haidegger, T., Abel, M., Redfield, S.A., Balakirsky, S., Ragavan, V., Li, H., Schlenoff, C., Prestes, E.: Extensions to the core ontology for robotics and automation. Robot. Comput.-Integr. Manuf. 33(C), 3–11 (2015)

    Google Scholar 

  21. Cutkosky, M.R.: On grasp choice, grasp models, and the design of hands for manufacturing tasks. IEEE Trans. Robot. Automa. 5(3), 269–279 (1989)

    Google Scholar 

  22. Beksi, W.J., Spruth, J., Papanikolopoulos, N.: Core: a cloud-based object recognition engine for robotics. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4512–4517 (2015)

    Google Scholar 

  23. Rusu, R.B., Cousins, S.: 3D is here: Point Cloud Library (PCL). In: IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, May 9–13 (2011)

    Google Scholar 

  24. Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the International Conference on Computer Vision-Volume 2–Volume 2, ICCV ’99, pp. 1150–1157 (1999)

    Google Scholar 

  25. Tombari, F., Salti, S., Di Stefano, L.: Unique signatures of histograms for local surface description. In: 11th European Conference on Computer Vision (ECCV), Hersonissos, Greece, September 5–11 (2010)

    Google Scholar 

  26. Bradski, G.: Dr. Dobb’s Journal of Software Tools

    Google Scholar 

  27. Miller, A.T., Allen, P.K.: Graspit! a versatile simulator for robotic grasping. IEEE Robot. Autom. Mag. 11(4), 110–122 (2004). Dec

    Article  Google Scholar 

  28. Koenig, N., Howard, A.: Design and use paradigms for gazebo, an open-source multi-robot simulator. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS 2004), pp. 2149–2154 (2004)

    Google Scholar 

  29. Mörwald, T., Vincze, M.: Object Modelling for Cognitive Robotics. PhD Thesis, Vienna University of Technology (2013)

    Google Scholar 

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Authors and Affiliations

  1. Intelligent Autonomous Systems Lab (IAS-Lab) Department of Information Engineering (DEI), University of Padova, Via Gradenigo 6/B, 35131, Padova, Italy

    Elisa Tosello, Alejandro Gatto Castro & Enrico Pagello

  2. Big Data Team Department of Big Data and IT Technology, China Mobile Research Institute (CMRI), Innovation Building, No. 32, Xuanwumen West Avenue, 100053, Beijing, Xicheng District, People’s Republic of China

    Zhengjie Fan

Authors
  1. Elisa Tosello
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  2. Zhengjie Fan
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  3. Alejandro Gatto Castro
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  4. Enrico Pagello
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Corresponding authors

Correspondence to Elisa Tosello or Enrico Pagello .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University , Shanghai, China

    Weidong Chen

  2. Osaka University , Osaka, Japan

    Koh Hosoda

  3. University of Padua , Padua, Italy

    Emanuele Menegatti

  4. Osaka University , Osaka, Japan

    Masahiro Shimizu

  5. Shanghai Jiao Tong University , Shanghai, China

    Hesheng Wang

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Tosello, E., Fan, Z., Castro, A.G., Pagello, E. (2017). Cloud-Based Task Planning for Smart Robots. In: Chen, W., Hosoda, K., Menegatti, E., Shimizu, M., Wang, H. (eds) Intelligent Autonomous Systems 14. IAS 2016. Advances in Intelligent Systems and Computing, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-319-48036-7_21

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  • DOI: https://doi.org/10.1007/978-3-319-48036-7_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48035-0

  • Online ISBN: 978-3-319-48036-7

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