Task and Context Sensitive Gripper Design Learning Using Dynamic Grasp Simulation

Abstract

In this work, we present a generic approach to optimize the design of a parametrized robot gripper including both selected gripper mechanism parameters, and parameters of the finger geometry. We suggest six gripper quality indices that indicate different aspects of the performance of a gripper given a CAD model of an object and a task description. These quality indices are then used to learn task-specific finger designs based on dynamic simulation. We demonstrate our gripper optimization on a parallel finger type gripper described by twelve parameters. We furthermore present a parametrization of the grasping task and context, which is essential as an input to the computation of gripper performance. We exemplify important aspects of the indices by looking at their performance on subsets of the parameter space by discussing the decoupling of parameters and show optimization results for two use cases for different task contexts. We provide a qualitative evaluation of the obtained results based on existing design guidelines and our engineering experience. In addition, we show that with our method we achieve superior alignment properties compared to a naive approach with a cutout based on the “inverse of an object”. Furthermore, we provide an experimental evaluation of our proposed method by verifying the simulated grasp outcomes through a real-world experiment.

References

  1. 1.

    Nelder, J.A., Mead, R.: A simplex method for function minimization. The Computer Journal 7 (4), 308–313 (1965). [Online]. Available: http://comjnl.oxfordjournals.org/content/7/4/308.abstract

    MathSciNet  Article  MATH  Google Scholar 

  2. 2.

    Wolniakowski, A., Miatliuk, K., Krüger, N., Rytz, J.A.: Automatic evaluation of task-focused parallel jaw gripper design. In: International Conference on Simulation, Modeling, and Programming for Autonomous Robots (2014)

  3. 3.

    Wolniakowski, A., Jorgensen, J.A., Miatliuk, K., Petersen, H.G., Krüger, N.: Task and context sensitive optimization of gripper design using dynamic grasp simulation. In: 20th International Conference on Methods and Models in Automation and Robotics (2015)

  4. 4.

    Causey, G.C., Quinn, R.D.: Gripper design guidelines for modular manufacturing. Proceedings of the 1998 IEEE International Conference on Robotics and Automation, 1998, vol. 2, pp. 1453–1458. IEEE (1998)

  5. 5.

    Causey, G.: Guidelines for the design of robotic gripping systems. Assem. Autom. 23(1), 18–28 (2003)

    Article  Google Scholar 

  6. 6.

    Krenich, S.: Multicriteria design optimization of robot gripper mechanisms, vol. 117, pp. 207–218. Springer, Netherlands (2004)

    Google Scholar 

  7. 7.

    Boubekri, N., Chakraborty, P.: Robotic grasping: gripper designs, control methods and grasp configurations – a review of research. Integr. Manuf. Syst. 13(7), 520–531 (2002)

    Article  Google Scholar 

  8. 8.

    Blanes, C., Mellado, M., ortiz, C., Valera, A.: Review. technologies for robot grippers in pick and place operations for fresh fruits and vegetables. Span. J. Agric. Res. 9(4), 1130–1141 (2011)

    Article  Google Scholar 

  9. 9.

    Causey, G.C.: Elements of agility in manufacturing. Ph.D. dissertation Case Western Reserve University (1999)

  10. 10.

    Cuadrado, J., Naya, M.A., Ceccarelli, M., Carbone, G.: An optimum design procedure for two-finger grippers: a case of study. IFToMM Electronic Journal of Computational Kinematics 15403(1), 2002 (2002)

    Google Scholar 

  11. 11.

    Lanni, C., Ceccarelli, M.: An optimization problem algorithm for kinematic design of mechanisms for two-finger grippers. Open Mechanical Engineering Journal 3, 49–62 (2009)

    Article  Google Scholar 

  12. 12.

    Cuadrado, J., Naya, M.A., Ceccarelli, M., Carbone, G.: An optimum design procedure for two-finger grippers: a case of study. IFToMM Electronic Journal of Computational Kinematics 15403(1), 2002 (2002)

    Google Scholar 

  13. 13.

    Ceccarelli, M., Cuadrado, J., Dopico, D.: An optimum synthesis for gripping mechanisms by using natural coordinates. Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci. 216(6), 643–653 (2002)

    Article  Google Scholar 

  14. 14.

    Zhang, T.: Optimal design of self-aligning robot gripper jaws. Ph.D. dissertation. aAI3044755 (2001)

  15. 15.

    Zhang, T., cheung, L., Goldberg, K.: Shape tolerance for robot gripper jaws. In: IROS, pp. 1782–1787 (2001)

  16. 16.

    Zhang, M.T., Goldberg, K.: Designing robot grippers: optimal edge contacts for part alignment. Robotica 25(03), 341 (2006)

    Article  Google Scholar 

  17. 17.

    Ellekilde, L.-P., Petersen, H.G.: Design and test of object aligning grippers for industrial applications. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5165–5170. IEEE (2006)

  18. 18.

    Datta, R., Deb, K.: Optimizing and deciphering design principles of robot gripper configurations using an evolutionary multi-objective optimization method (2011)

  19. 19.

    Kolluru, R., Valavanis, K., Smith, S., Tsourveloudis, N.: Design fundamentals of a reconfigurable robotic gripper system. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 30(2), 181–187 (2000)

    Article  Google Scholar 

  20. 20.

    Song, D., Ek, C., Huebner, K., Kragic, D.: Multivariate discretization for bayesian network structure learning in robot grasping. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 1944–1950 (2011)

  21. 21.

    Nikandrova, E., Kyrki, V.: Category-based task specific grasping. Robot. Auton. Syst. 70, 25–35 (2015)

    Article  Google Scholar 

  22. 22.

    Ciocarlie, M., Allen, P.: Data-driven optimization for underactuated robotic hands. In: 2010 IEEEInternational Conference on Robotics and Automation (ICRA), pp. 1292–1299. IEEE (2010)

  23. 23.

    Vassilis, M., Kostas, S., Stavros, P., Vassileios, S., Argiris, D., Nikos, A., et al.: Application of soft computing techniques in the design of robot grippers. Guidelines for a Decision Support Method Adapted to NPD Processes (2007)

  24. 24.

    Zhang, X., Nelson, C.A.: Multiple-criteria kinematic optimization for the design of spherical serial mechanisms using genetic algorithms. J. Mech. Des. 133(1), 011 005–011 005 (2011)

    Article  Google Scholar 

  25. 25.

    Moulianitis, V.C., Aspragathos, N.A., Dentsoras, A.J.: A model for concept evaluation in design—-an application to mechatronics design of robot grippers. Mechatronics 14(6), 599–622 (2004)

    Article  Google Scholar 

  26. 26.

    Kraft, D., Ellekilde, L.-P., Jorgensen, J.A.: Automatic grasp generation and improvement for industrial bin-picking. In: Röhrbein, F., Veiga, G., Natale, C. (eds.) Gearing Up and Accelerating Cross-fertilization between Academic and Industrial Robotics Research in Europe:, ser. Springer Tracts in Advanced Robotics, vol. 94, pp. 155–176. Springer International Publishing, Cham (2014)

  27. 27.

    Krenich, S.: Optimal design of robot gripper mechanism using force and displacement transmission ratio. Applied Mechanics and Materials 613, 117–125 (2014)

    Article  Google Scholar 

  28. 28.

    Schunk: Schunk egrip. [Online]. Available: http://www.schunk-produkte.com/en/tools/3d-designtool-egrip.html (2015)

  29. 29.

    Rytz, J.A., Ellekilde, L.-P., Kraft, D., Petersen, H.G., Krüger, N.: On transferability of grasp-affordances in data-driven grasping. In: Proceedings of the RAAD 2013 22nd International Workshop on Robotics in Alpe-Adria-Danube Region (2013)

  30. 30.

    Jorgensen, J., Ellekilde, L., Petersen, H.: Robworksim - an open simulator for sensor based grasping. In: Proceedings of Joint 41st International Symposium on Robotics (ISR 2010) and the 6th German Conference on Robotics (ROBOTIK 2010), pp. 1–8, Munich (2010)

  31. 31.

    Ferrari, C., Canny, J.: Planning optimal grasps. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 2290–2295 (1992)

  32. 32.

    Zheng, Y., Qian, W.-H.: Improving grasp quality evaluation. Robot. Auton. Syst. 57, 665–673 (2009). [Online]. Available: http://www.sciencedirect.com/science/article/pii/S092188900800208X

  33. 33.

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

    Article  Google Scholar 

  34. 34.

    Kraft, D., Ellekilde, L.-P., Jorgensen, J.: Automatic grasp generation and improvement for industrial bin-picking, vol. 94, pp. 155–176. Springer International Publishing, Cham (2014)

  35. 35.

    Jorgensen, J.A., Rukavishnikova, N., Krüger, N., Petersen, H.G.: Spatial constraint identification of parts in se3 for action optimization. In: IEEE International Conference on Industrial Technology (ICIT) 03 (2015)

  36. 36.

    Wolniakowski, A., Gams, A., Kiforenko, L., Kramberger, A., Chrysostomou, D., Madsen, O., Miatliuk, K., Petersen, H.G., Hagelskjaer, F., Buch, A.G., Ude, A., Krüger, N.: Compensating pose uncertainties through appropriate gripper finger cutouts. submitted (2016)

  37. 37.

    Wolniakowski, A., Kramberger, A., Gams, A., Chrysostomou, D., Hagelskjaer, F., Thulesen, T.N., Kiforenko, L., Buch, A.G., Bodenhagen, L., Petersen, H.G., Madsen, O., Ude, A., Krüger, N.: Optimizing grippers for compensating pose uncertainties by dynamic simulation. In: International Conference on Simulation, Modeling, and Programming for Autonomous Robots (2016). submitted

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Acknowledgments

The research leading to these results has received funding from the European Community’s Seventh Framework Programme FP7/2007-2013 (Programme and Theme: ICT-2011.2.1, Cognitive Systems and Robotics) under grant agreement no. 600578, ACAT and by Danish Agency for Science, Technology and Innovation, project CARMEN.

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Correspondence to A. Wolniakowski.

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Wolniakowski, A., Miatliuk, K., Gosiewski, Z. et al. Task and Context Sensitive Gripper Design Learning Using Dynamic Grasp Simulation. J Intell Robot Syst 87, 15–42 (2017). https://doi.org/10.1007/s10846-017-0492-y

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Keywords

  • Gripper design
  • Industrial assembly
  • Simulation
  • Optimization