Advertisement

An ASP-Based Framework for the Manipulation of Articulated Objects Using Dual-Arm Robots

  • Riccardo Bertolucci
  • Alessio Capitanelli
  • Carmine Dodaro
  • Nicola Leone
  • Marco MarateaEmail author
  • Fulvio Mastrogiovanni
  • Mauro Vallati
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11481)

Abstract

The manipulation of articulated objects is of primary importance in robotics, and is one of the most complex robotics tasks. Traditionally, this problem has been tackled by developing ad-hoc approaches, that lack of flexibility and portability.

In this paper we present a framework based on Answer Set Programming (ASP) for the automated manipulation of articulated objects in a robot architecture. In particular, ASP is employed for representing the configuration of the articulated object, for checking the consistency of the knowledge base, as well as for generating the sequence of manipulation actions. The framework is validated both in simulation and on the Baxter dual-arm manipulator, showing the applicability of the ASP methodology in this complex application scenario.

References

  1. 1.
    Alviano, M., Dodaro, C., Maratea, M.: An advanced answer set programming encoding for nurse scheduling. In: Esposito, F., Basili, R., Ferilli, S., Lisi, F.A. (eds.) (AI*IA 2017). LNCS, vol. 10640, pp. 468–482. Springer, Heidelberg (2017).  https://doi.org/10.1007/978-3-319-70169-1_35CrossRefGoogle Scholar
  2. 2.
    Amendola, G., Dodaro, C., Leone, N., Ricca, F.: On the application of answer set programming to the conference paper assignment problem. In: Adorni, G., Cagnoni, S., Gori, M., Maratea, M. (eds.) AI*IA 2016. LNCS (LNAI), vol. 10037, pp. 164–178. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-49130-1_13CrossRefGoogle Scholar
  3. 3.
    Andres, B., Rajaratnam, D., Sabuncu, O., Schaub, T.: Integrating ASP into ROS for reasoning in robots. In: Calimeri, F., Ianni, G., Truszczynski, M. (eds.) LPNMR 2015. LNCS (LNAI), vol. 9345, pp. 69–82. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-23264-5_7CrossRefzbMATHGoogle Scholar
  4. 4.
    Bodenhagen, L., et al.: An adaptable robot vision system performing manipulation actions with flexible objects. IEEE Trans. Autom. Sci. Eng. 11(3), 749–765 (2014)CrossRefGoogle Scholar
  5. 5.
    Brewka, G., Eiter, T., Truszczynski, M.: Answer set programming at a glance. Commun. ACM 54(12), 92–103 (2011)CrossRefGoogle Scholar
  6. 6.
    Calimeri, F., et al.: ASP-Core-2 Input Language Format (2013)Google Scholar
  7. 7.
    Capitanelli, A., Maratea, M., Mastrogiovanni, F., Vallati, M.: Automated planning techniques for robot manipulation tasks involving articulated objects. In: Esposito, F., Basili, R., Ferilli, S., Lisi, F. (eds.) AI*IA 2017. LNCS, pp. 483–497. Springer, Heidelberg (2017).  https://doi.org/10.1007/978-3-319-70169-1_36CrossRefGoogle Scholar
  8. 8.
    Capitanelli, A., Maratea, M., Mastrogiovanni, F., Vallati, M.: On the manipulation of articulated objects in human-robot cooperation scenarios. Robot. Auton. Syst. 109, 139–155 (2018)CrossRefGoogle Scholar
  9. 9.
    Di Rosa, E., Giunchiglia, E., Maratea, M.: Solving satisfiability problems with preferences. Constraints 15(4), 485–515 (2010)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Dimopoulos, Y., Gebser, M., Lühne, P., Romero, J., Schaub, T.: plasp 3: towards effective ASP planning. In: Balduccini, M., Janhunen, T. (eds.) LPNMR 2017. LNCS (LNAI), vol. 10377, pp. 286–300. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-61660-5_26CrossRefGoogle Scholar
  11. 11.
    Erdem, E., Patoglu, V.: Applications of ASP in robotics. Künstliche Intelligenz 32(2–3), 143–149 (2018)CrossRefGoogle Scholar
  12. 12.
    Erdem, E., Patoglu, V., Saribatur, Z.G.: Integrating hybrid diagnostic reasoning in plan execution monitoring for cognitive factories with multiple robots. In: Proceedings of ICRA, pp. 2007–2013. IEEE (2015)Google Scholar
  13. 13.
    Erdem, E., Patoglu, V., Saribatur, Z.G., Schüller, P., Uras, T.: Finding optimal plans for multiple teams of robots through a mediator: a logic-based approach. Theory Pract. Log. Program. 13(4–5), 831–846 (2013)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Gebser, M., et al.: Ricochet robots: a transverse ASP benchmark. In: Cabalar, P., Son, T.C. (eds.) LPNMR 2013. LNCS (LNAI), vol. 8148, pp. 348–360. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40564-8_35CrossRefGoogle Scholar
  15. 15.
    Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T., Wanko, P.: Theory solving made easy with clingo 5. In: Proceedings of the Technical Communications of the International Conference on Logic Programming (ICLP), pp. 2:1–2:15. Schloss Dagstuhl (2016)Google Scholar
  16. 16.
    Gebser, M., Maratea, M., Ricca, F.: The sixth answer set programming competition. J. Artif. Intell. Res. 60, 41–95 (2017)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Proceedings of the International Conference on Logic Programming (ICLP), pp. 1070–1080. MIT Press (1988)Google Scholar
  18. 18.
    Giunchiglia, E., Maratea, M.: Solving optimization problems with DLL. In: Brewka, G., Coradeschi, S., Perini, A., Traverso, P. (eds.) Proceedings of the 17th European Conference on Artificial Intelligence (ECAI 2006). Frontiers in Artificial Intelligence and Applications, vol. 141, pp. 377–381. IOS Press (2006)Google Scholar
  19. 19.
    Harnad, S.: The symbol grounding problem. Physica D 42, 335–346 (1990)CrossRefGoogle Scholar
  20. 20.
    Heyer, C.: Human-robot interaction and future industrial robotics applications. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4749–4754. IEEE (2010)Google Scholar
  21. 21.
    Howey, R., Long, D., Fox, M.: VAL: automatic plan validation, continuous effects and mixed initiative planning using PDDL. In: Proceedings of the IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp. 294–301. IEEE Computer Society (2004)Google Scholar
  22. 22.
    Kautz, H.A., Selman, B.: Planning as satisfiability. In: Proceedings of the European Conference on Artificial Intelligence (ECAI), pp. 359–363 (1992)Google Scholar
  23. 23.
    Khandelwal, P., Zhang, S., Sinapov, J., Leonetti, M., Thomason, J., Yang, F., Gori, I., Svetlik, M., Khante, P., Lifschitz, V., Aggarwal, J.K., Mooney, R.J., Stone, P.: Bwibots: a platform for bridging the gap between AI and human-robot interaction research. Int. J. Robot. Res. 36(5–7), 635–659 (2017)CrossRefGoogle Scholar
  24. 24.
    Krüger, J., Lien, T.K., Verl, A.: Cooperation of human and machines in assembly lines. CIRP Ann. 58(2), 628–646 (2009)CrossRefGoogle Scholar
  25. 25.
    Lee, J., Lifschitz, V., Yang, F.: Action language BC: preliminary report. In: Rossi, F. (ed.) Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013), pp. 983–989. IJCAI/AAAI (2013)Google Scholar
  26. 26.
    Lifschitz, V.: Answer set programming and plan generation. Artif. Intell. J. 138(1–2), 39–54 (2002)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Nair, A., et al.: Combining self-supervised learning and imitation for vision-based rope manipulation. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 2146–2153. IEEE (2017)Google Scholar
  28. 28.
    Niemelä, I.: Logic programs with stable model semantics as a constraint programming paradigm. AMAI 25(3–4), 241–273 (1999)MathSciNetzbMATHGoogle Scholar
  29. 29.
    Schäpers, B., Niemueller, T., Lakemeyer, G., Gebser, M., Schaub, T.: ASP-based time-bounded planning for logistics robots. In: Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), pp. 509–517. AAAI Press (2018)Google Scholar
  30. 30.
    Schulman, J., Ho, J., Lee, C., Abbeel, P.: Learning from demonstrations through the use of non-rigid registration. In: Inaba, M., Corke, P. (eds.) Robotics Research. STAR, vol. 114, pp. 339–354. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-28872-7_20CrossRefGoogle Scholar
  31. 31.
    Wakamatsu, H., Arai, E., Hirai, S.: Knotting/unknotting manipulation of deformable linear objects. Int. J. Robot. Res. 25(4), 371–395 (2006)CrossRefGoogle Scholar
  32. 32.
    Yamakawa, Y., Namiki, A., Ishikawa, M.: Dynamic high-speed knotting of a rope by a manipulator. IJARS 10, 1–12 (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.DeMaCSUniversity of CalabriaRendeItaly
  2. 2.DIBRISUniversity of GenovaGenovaItaly
  3. 3.University of HuddersfieldHuddersfieldUK

Personalised recommendations