Advertisement

Parametrically Modeled DH Table for Soft Robot Kinematics: Case Study for A Soft Gripper

  • Po Ting LinEmail author
  • Ebrahim Shahabi
  • Kai-An Yang
  • Yu-Ta Yao
  • Chin-Hsing Kuo
Conference paper
Part of the Mechanisms and Machine Science book series (Mechan. Machine Science, volume 73)

Abstract

Recently, researches and innovations were greatly developed in the field of soft robotics due to the advantages of higher flexibility and safer operations. Researchers have designed new materials, new manufacturing methodologies and advanced control techniques for soft robots. Some commercial products such as soft grippers are now available in the market and applied in the area of agriculture, medicine, machinery, etc. This paper aims to show how to mathematically describe the motions of soft robots. A method of parametrically modeled Denavit-Hartenberg (DH) parameters was used for soft robot kinematic analysis. A soft finger with soft polydimethylsiloxane (PDMS) body and rigid polylactic acid (PLA) bone structures were made by a molding process and actuated by cable. The bending motion of the soft finger changed the link angle and the link length of the soft finger, which were parametrically modeled. This case study showed the DH parameters of the soft finger nonlinearly changed with respect to the control parameter.

Keywords

DH Parameter Soft Gripper PDMS 3D Printing Cable Molding 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgement

This work was financially supported by the “Center for Cyber-physical System Innovation” and “High-Speed 3D Printing Center” from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan. This paper was also supported by the Ministry of Science and Technology, Taiwan (grant numbers MOST 106-2221-E-033-025, MOST 107-2221-E-011-088, and MOST 107-2218-E-011-021).

References

  1. 1.
    Wehner, M., Truby, R.L., Fitzgerald, D.J., Mosadegh, B., Whitesides, G.M., Lewis, J.A., Wood, R.J.: An integrated design and fabrication strategy for entirely soft, autonomous robots. Nature 536(7617), 451-455 (2016).CrossRefGoogle Scholar
  2. 2.
    Lee, C., Kim, M., Kim, Y.J., Hong, N., Ryu, S., Kim, H.J., Kim, S.: Soft robot review. International Journal of Control, Automation and Systems 15(1), 3-15 (2017).CrossRefGoogle Scholar
  3. 3.
    Onal, C.D., Chen, X., Whitesides, G.M., Rus, D.: Soft mobile robots with on-board chemical pressure generation. Robotics Research, 525-540, Springer (2017).Google Scholar
  4. 4.
    Polygerinos, P., Correll, N., Morin, S.A., Mosadegh, B., Onal, C.D., Petersen, K., Cianchetti, M., Tolley, M.T., Shepherd, R.F.: Soft robotics: Review of fluid-driven intrinsically soft devices; manufacturing, sensing, control, and applications in human-robot interaction. Advanced Engineering Materials 19(12), 1700016 (2017).CrossRefGoogle Scholar
  5. 5.
    Morrow, J.F.: Direct 3D Printing of Silicone Elastomer Soft Robots without Support. (2017).Google Scholar
  6. 6.
    Paik, J.: Soft robot design methodology for ‘push-button’ manufacturing. Nature Reviews Materials 3(6), 81 (2018).CrossRefGoogle Scholar
  7. 7.
    Gerboni, G., Diodato, A., Ciuti, G., Cianchetti, M., Menciassi, A.: Feedback control of soft robot actuators via commercial flex bend sensors. IEEE/ASME Transactions on Mechatronics 22(4), 1881-1888 (2017).CrossRefGoogle Scholar
  8. 8.
    Zhang, H., Cao, R., Zilberstein, S., Wu, F., Chen, X.: Toward effective soft robot control via reinforcement learning. In: International Conference on Intelligent Robotics and Applications, pp. 173-184, (2017).CrossRefGoogle Scholar
  9. 9.
    Wang, H., Yang, B., Liu, Y., Chen, W., Liang, X., Pfeifer, R.: Visual servoing of soft robot manipulator in constrained environments with an adaptive controller. IEEE/ASME Transactions on Mechatronics 22(1), 41-50 (2017).CrossRefGoogle Scholar
  10. 10.
    Ansari, Y., Manti, M., Falotico, E., Cianchetti, M., Laschi, C.: Multiobjective optimization for stiffness and position control in a soft robot arm module. IEEE Robotics and Automation Letters 3(1), 108-115 (2018).CrossRefGoogle Scholar
  11. 11.
    Denavit, J., Hartenberg, R.S.: A kinematic notation for low pair mechanisms based on matrices. Journal of Applied Mechanics 22, 215-221 (1955).Google Scholar
  12. 12.
    Muzan, I.W., Faisal, T., Al-Assadi, H., Iwan, M.: Implementation of industrial robot for painting applications. Procedia Engineering 41, 1329-1335 (2012).CrossRefGoogle Scholar
  13. 13.
    Chang, C.-J., Lin, P.T., Zheng, B., Wang, J., Gea, H.C., Lu, Y.-W. Compliant Mechanism Based Material Design using Micro-Molding. In: ASME 2005 International Design Engineering Technical Conferences & Computers and Information Engineering Conference, IDETC/CIE 2005, Graduate Student Mechanism Design Competition, Long Beach, CA, USA, (2005).Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringNational Taiwan University of Science and TechnologyTaipeiTaiwan
  2. 2.Center for Cyber-Physical System InnovationNational Taiwan University of Science and TechnologyTaipeiTaiwan
  3. 3.High-Speed 3D Printing CenterNational Taiwan University of Science and TechnologyTaipeiTaiwan

Personalised recommendations