Skip to main content
SpringerLink
Log in

Soft Robotic Industrial Systems

  • Conference paper
  • First Online:
Systems Collaboration and Integration (ICPR1 2021)

Part of the book series: Automation, Collaboration, & E-Services ((ACES,volume 14))

Included in the following conference series:

  • 169 Accesses

Abstract

Future industrial systems require humans and robots to work together safely and intuitively to optimize the production of goods. As a team, humans provide skills, experience, and knowledge, while robots offer physical assistance and take care of the performance of repetitive tasks at high speed. While this human-robot collaboration can minimize companies’ response time to changes in supply or demand, several safety concerns need to be addressed before human operators can safely work near industrial robots. Soft robots, fabricated using elastic and compliant materials, have been developed to become intrinsically safe to human co-workers and to provide different manipulation approaches that exploit the deformability of the robot to enhance robotic dexterity while interacting with delicate and brittle materials. These soft robotic systems, however, required the development of new flexible sensors and control methods to achieve the accuracy of existing industrial robots. This chapter describes the development of soft robotic industrial systems. First, the design principles and manufacturing methods to create dexterous soft robots will be reviewed. Next, the main modeling and control methods used to implement soft robots in industrial environments will be described. Finally, current soft robotics challenges and emerging industrial applications for soft cyber-physical systems will be analyzed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Declaration of Competing Interest

The author declares no competing financial interest.

References

  1. Runge, G., Raatz, A.: A framework for the automated design and modelling of soft robotic systems. CIRP Ann. 66(1), 9–12 (2017)

    Article  Google Scholar 

  2. Hentout, A., Aouache, M., Maoudj, A., Akli, I.: Human– robot interaction in industrial collaborative robotics: a literature review of the decade 2008–2017. Adv. Robot. 33(15–16), 764–799 (2019)

    Article  Google Scholar 

  3. Berg, J., Lu, S.; Review of interfaces for industrial human-robot interaction. Curr. Robot. Reports 1(2), 27–34 (2020)

    Google Scholar 

  4. Demir, K.A., Döven, G., Sezen, B.: Industry 5.0 and human-robot co-working. Procedia Compute. Sci. 158, 688–695 (2019)

    Google Scholar 

  5. Shih, B., et al.: Electronic skins and machine learning for intelligent soft robots. Sci. Robot. 5(41), eaaz9239 (2020)

    Google Scholar 

  6. Yogeswaran, N., et al.: New materials and advances in making electronic skin for interactive robots. Adv. Robot. 29(21), 1359–1373 (2015)

    Google Scholar 

  7. Cheng, G., Dean-Leon, E., Bergner, F., Olvera, J.R.G., Leboutet, Q., Mittendorfer, P.: A comprehensive realization of robot skin: sensors, sensing, control, and applications. Proc. IEEE 107(10), 2034–2051 (2019)

    Article  Google Scholar 

  8. Rashid, F., Burns, D., Song, Y.S.: Sensing small interaction forces through proprioception. Sci. Reports 11(1), 1–10 (2021)

    Google Scholar 

  9. Miki, T., Lee, J., Hwangbo, J., Wellhausen, L., Koltun, V., Hutter, M.: Learning robust perceptive locomotion for quadrupedal robots in the wild. Sci. Robot. 7(62), eabk2822 (2022)

    Google Scholar 

  10. Lee, J., Hwangbo, J., Wellhausen, L., Koltun, V., Hutter, V.: Learning quadrupedal locomotion over challenging terrain. Sci. Robot. 5(47), eabc5986 (2020)

    Google Scholar 

  11. Coyle, S., Majidi, C., LeDuc, P., Hsia, K.J.: Bio-inspired soft robotics: material selection, actuation, and design. Extreme Mech. Let. 22, 51– 59 (2018)

    Google Scholar 

  12. Martinez, R.V., et al.: Robotic tentacles with threedimensional mobility based on flexible elastomers. Adv. Mater. 25(2), 205–212 (2013)

    Google Scholar 

  13. Rossiter, J., Hauser, H.: Soft robotics—the next industrial revolution. IEEE Robot. Autom. Mag. 23(3), 17–20 (2016)

    Google Scholar 

  14. Chen, S., Pang, Y., Cao, Y., Tan, X., Cao, C.: Soft robotic manipulation system capable of stiffness variation and dexterous operation for safe human–machine interactions. Adv. Mater. Technol. 6(5), 2100084 (2021)

    Article  Google Scholar 

  15. Polygerinos, P., Wang, Z., Galloway, K.C., Wood, R.J., Walsh, C.J.: Soft robotic glove for combined assistance and at-home rehabilitation. Robot. Auton. Syst. 73, 135–143 (2015)

    Google Scholar 

  16. Awad, L.N., Esquenazi, A., Francisco, G.E., Nolan, K.J., Jayaraman, A.: The rewalk restore™ soft robotic exosuit: a multi-site clinical trial of the safety, reliability, and feasibility of exosuit-augmented post-stroke gait rehabilitation. J. Neuroeng. Rehabil. 17(1), 1–11 (2020)

    Google Scholar 

  17. Dahl, T.S., Boulos, M.N.K.: Robots in health and social care: a complementary technology to home care and telehealthcare? Robotics 3(1), 1–21 (2013)

    Google Scholar 

  18. Ansari, Y., Manti, M., Falotico, E., Mollard, Y., Cianchetti, M., Laschi, C.: Towards the development of a soft manipulator as an assistive robot for personal care of elderly people. Int. J. Adv. Robot. Syst. 14(2), 1729881416687132 (2017)

    Google Scholar 

  19. Park, C., et al.: An organosynthetic dynamic heart model with enhanced biomimicry guided by cardiac diffusion tensor imaging. Sci. Robot. 5(38), eaay9106 (2020)

    Google Scholar 

  20. Cianchetti, M., Laschi, C., Menciassi, A., Dario, P.: Biomedical applications of soft robotics. Nat. Rev. Mater. 3(6), 143–153 (2018)

    Article  Google Scholar 

  21. Koo, S.K.: Design factors and preferences in wearable soft robots for movement disabilities. Int. J. Cloth. Sci. Technol. (2018)

    Google Scholar 

  22. Martinez, R.V., Glavan, A.C., Keplinger, C., Oyetibo, A.I., Whitesides, G.M.: Soft actuators and robots that are resistant to mechanical damage. Adv. Function. Mater. 24(20), 3003–3010 (2014)

    Google Scholar 

  23. Kovač, M.: The bioinspiration design paradigm: a perspective for soft robotics. Soft Rob. 1(1), 28–37 (2014)

    Article  Google Scholar 

  24. Pal, A., Restrepo, V., Goswami, D., Martinez, R.V.: Exploiting mechanical instabilities in soft robotics: control, sensing, and actuation. Adv. Mater. 33(19), 2006939 (2021)

    Google Scholar 

  25. Martinez, R.V., Fish, C.R., Chen, X., Whitesides, G.M.: Elastomeric origami: programmable paper-elastomer composites as pneumatic actuators. Adv. Function. Mater. 22(7), 1376–1384 (2012)

    Google Scholar 

  26. Pal, A., Goswami, D., Martinez, R.V.: Elastic energy storage enables rapid and programmable actuation in soft machines. Adv. Function. Mater. 30(1), 1906603 (2020)

    Google Scholar 

  27. Kwok, S.W., et al.:. Magnetic assembly of soft robots with hard components. Adv. Function. Mater. 24(15), 2180–2187 (2014)

    Google Scholar 

  28. Goswami, D., Liu, S., Pal, A., Silva, L.G., Martinez, R.V.: 3darchitected soft machines with topologically encoded motion. Adv. Function. Mater. 29(24), 1808713 (2019)

    Google Scholar 

  29. Goswami, D., Zhang, Y., Liu, S., Abdalla, O.A., Zavattieri, P.D., Martinez, R.V.: Mechanical metamaterials with programmable compression-twist coupling. Smart Mater. Struct. 30(1), 015005 (2020)

    Google Scholar 

  30. Schmitt, F., Piccin, O., Barbé, L., Bayle, B.: Soft robots manufacturing: a review. Front. Robot. AI 5, 84 (2018)

    Article  Google Scholar 

  31. Stano, G., Percoco, G.: Additive manufacturing aimed to soft robots fabrication: a review. Extreme Mech. Let. 42, 101079 (2021)

    Article  Google Scholar 

  32. Gul, J.Z., et al.: 3d printing for soft robotics–a review. Sci. Technol. Adv. Mater. 19(1), 243–262 (2018)

    Google Scholar 

  33. Layani, M., Wang, X., Magdassi, S.: Novel materials for 3d printing by photopolymerization. Adv. Mater. 30(41), 1706344 (2018)

    Article  Google Scholar 

  34. Bagheri, A., Jin, J.: Photopolymerization in 3d printing. ACS Appl. Polymer Mater. 1(4), 593–611 (2019)

    Article  Google Scholar 

  35. Luo, M., et al.: Motion planning and iterative learning control of a modular soft robotic snake. Front. Robot. AI 191 (2020)

    Google Scholar 

  36. Skylar-Scott, M.A., Mueller, J., Visser, C.W., Lewis, J.A.: Voxelated soft matter via multimaterial multinozzle 3d printing. Nature 575(7782), 330–335 (2019)

    Google Scholar 

  37. Valentine, A.D., et al.: Hybrid 3d printing of soft electronics. Adv. Mater. 29(40), 1703817 (2017)

    Google Scholar 

  38. Baumgartner, M., et al. Resilient yet entirely degradable gelatin-based biogels for soft robots and electronics. Nat. Mater. 19(10), 1102–1109 (2020)

    Google Scholar 

  39. Pang, G., et al.: Coboskin: soft robot skin with variable stiffness for safer human–robot collaboration. IEEE Trans. Industr. Electron. 68(4), 3303–3314 (2020)

    Article  Google Scholar 

  40. Roberts, P., Zadan, M., Majidi, C.: Soft tactile sensing skins for robotics. Curr. Robot. Reports 2(3), 343–354 (2021)

    Google Scholar 

  41. Jenett, B., et al.: Digital morphing wing: active wing shaping concept using composite lattice-based cellular structures. Soft Robot. 4(1), 33–48 (2017)

    Google Scholar 

  42. Shui, L., Zhu, L., Yang, Z., Liu, Y., Chen, X.: Energy efficiency of mobile soft robots. Soft Matter 13(44), 8223–8233 (2017)

    Article  Google Scholar 

  43. Dong, W., et al.: Soft human–machine interfaces: design, sensing and stimulation. Int. J. Intell. Robot. Appl. 2(3), 313–338 (2018)

    Google Scholar 

  44. Jadhav, S., Majit, M.R.A., Shih, B., Schulze, J.P., Tolley, M.T.: Variable stiffness devices using fiber jamming for application in soft robotics and wearable haptics. Soft Robot. 9(1), 173–186 (2022)

    Google Scholar 

  45. Grazioso, S., Di Gironimo, G., Siciliano, B.: A geometrically exact model for soft continuum robots: the finite element deformation space formulation. Soft Rob. 6(6), 790–811 (2019)

    Article  Google Scholar 

  46. Xavier, M.S., Fleming, A.J., Yong, Y.K.: Finite element modeling of soft fluidic actuators: overview and recent developments. Adv. Intell. Syst. 3(2), 2000187 (2021)

    Google Scholar 

  47. Gorissen, B., Donose, R., Reynaerts, D., De Volder, M.: Flexible pneumatic micro-actuators: analysis and production. Procedia Eng. 25, 681–684 (2011)

    Article  Google Scholar 

  48. Dilibal, S., Sahin, H., Celik, Y.: Experimental and numerical analysis on the bending response of the geometrically gradient soft robotics actuator. Arch. Mech. 70(5), 391–404 (2018)

    Google Scholar 

  49. Ding, L., et al.: Dynamic finite element modeling and simulation of soft robots. Chin. J. Mech. Eng.g 35(1), 1–11 (2022)

    Google Scholar 

  50. Majidi, C.: Soft-matter engineering for soft robotics. Adv. Mater. Technol. 4(2), 1800477 (2019)

    Article  Google Scholar 

  51. Salem, L., Gat, A.D., Or, Y.: Fluid-driven traveling waves in soft robots. Soft Robot. (2022)

    Google Scholar 

  52. Hwang, Y., Paydar, O.H., Candler, R.N.: Pneumatic microfinger with balloon fins for linear motion using 3d printed molds. Sens. Actuat. A Phys. 234, 65–71 (2015)

    Google Scholar 

  53. Ranzani, T., Cianchetti, M., Gerboni, G., Falco, I.D., Menciassi, A.: A soft modular manipulator for minimally invasive surgery: design and characterization of a single module. IEEE Trans. Robot. 32(1), 187–200 (2016)

    Google Scholar 

  54. Gorissen, B., Reynaerts, D., Konishi, S., Yoshida, K., Kim, J.-W., De Volder, M.: Elastic inflatable actuators for soft robotic applications. Adv. Mater. 29(43), 1604977 (2017)

    Article  Google Scholar 

  55. Santina, C.D., Bicchi, A., Rus, D.: On an improved state parametrization for soft robots with piecewise constant curvature and its use in model based control. IEEE Robot. Autom. Let. 5(2), 1001–1008 (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Industrial Engineering and Weldon School of Biomedical Engineering, Purdue University, Grissom Hall (GRIS) Rm. 284 315 N. Grant Street, West Lafayette, IN, 47907-2023, USA

    Ramses V. Martinez

Authors
  1. Ramses V. Martinez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ramses V. Martinez .

Editor information

Editors and Affiliations

  1. Industrial Engineering and Enterprise Information, Tunghai University, Taichung, Taiwan

    Chin-Yin Huang

  2. Department of Systems Science and Industrial Engineering, State University of New York at Binghamton, New York, NY, USA

    Sang Won Yoon

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Martinez, R.V. (2023). Soft Robotic Industrial Systems. In: Huang, CY., Yoon, S.W. (eds) Systems Collaboration and Integration. ICPR1 2021. Automation, Collaboration, & E-Services, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-031-44373-2_24

Download citation

Publish with us

Policies and ethics

Search

Navigation