Skip to main content
Log in

Soft Manipulator for Soft Robotic Applications: a Review

  • Survey Paper
  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

In the growing field of study known as “soft robotics,“ highly adaptable robots are built for soft interactions by utilizing the acquiescence, and flexibility of soft structures. The substantial advancement of soft grippers may be attributed to the growth of soft robotics, the investigation of various materials, and the creation of flexible electronics. The field of soft robotics does have the potential to have a substantial influence, among other applications, on the field of soft grippers and manipulators. Grippers of various varieties are necessary for handling various types of items, both hard and soft. It is crucial to adopt flexible and adaptive gripping tactics to get more item holding flexibility. Soft-robotic grasping systems that are extremely flexible in terms of workpiece shape, size, and structure are an ideal choice for increasing production flexibility. The major reason for the review is to learn about the existing soft gripper and the repeatability of existing soft-robotic grippers with large payload capacities. This article offers a thorough analysis of soft gripper recommendations, covering machine learning techniques along with physical theories, sensor technologies, actuation strategies, and device topologies. The potential effects of a soft robot manipulator for industry and the social economy are discussed as this essay comes to a close.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Data Availability

Not applicable.

Code Availability

Not applicable.

References

  1. Nof, S.Y.: Handbook of Industrial Robotics, 2nd edn. Wiley, Toronto (1999)

    Book  Google Scholar 

  2. Craig, J., Hsu, P., Sastry, S.: Adaptive control of mechanical manipulators. In: Proceedings. 1986 IEEE International Conference On Robotics and Automation, vol. 3, pp. 190–195. IEEE (1986)

  3. Laschi, C.: Soft robotics: New perspectives for robot bodyware and control. Front. Bioeng. Biotechnol 2, 3 (2014)

    Article  Google Scholar 

  4. Pfeifer, R., Lungarella, M., Fumiya Iida: The challenges ahead for bio-inspired’soft’robotics. Commun. ACM 55(11), 76–87 (2012)

    Article  Google Scholar 

  5. Shintake, J., Cacucciolo, V.: Dario Floreano, and Herbert Shea. Soft robotic grippers. Adv. Mater. 30(29):1707035. (2018)

  6. Faudzi, A.A.M., Ooga, J., Goto, T., Takeichi, M., Suzumori, K.: Index finger of a human-like robotic hand using thin soft muscles. IEEE Robot. Autom. Lett 3(1), 92–99 (2018). https://doi.org/10.1109/LRA.2017.2732059

    Article  Google Scholar 

  7. Manti, M., Cacucciolo, V., Cianchetti, M.: Stiffening in soft robotics: a review of the state of the art. In: IEEE Robotics & Automation Magazine, vol. 23, no. 3, pp. 93–106 (2016). https://doi.org/10.1109/MRA.2016.2582718

  8. Jiang, A., Xynogalas, G., Dasgupta, P., Althoefer, K., Nanayakkara, T.: Design of a variable stiffness flexible manipulator with composite granular jamming and membrane coupling. Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2922–2927 (2012)

  9. Brown, E., Rodenberg, N., Amend, J., Mozeika, A., Steltz, E., Zakin, M.R., Lipson, H., Jaeger, H.M.: Universal robotic gripper based on the jamming of granular material. Proc. Natl. Acad. Sci. U. S. A. 107(44):18809–18814 (2010). https://doi.org/10.1073/pnas.1003250107

  10. Li, Y., Chen, Y., Yang, Y., Wei, Y.: Passive particle jamming and its stiffening of Soft Robotic Grippers. IEEE Trans. Robot 33, 446–455 (2017)

    Article  Google Scholar 

  11. Hughes, J., Culha, U., Giardina, F., Guenther, F., Rosendo, A., Iida, F.: Soft manipulators and grippers: a review. Front. Robot. AI. 3(NOV):1–12 (2016). https://doi.org/10.3389/frobt.2016.00069

  12. Guoliang Zhong, Y., Hou, W., Dou, A., Soft: Pneumatic dexterous gripper with convertible grasping modes. Int. J. Mech. Sci (2019). https://doi.org/10.1016/j.ijmecsci.2019.02.028

    Article  Google Scholar 

  13. Yuan, Z., Wu, L., Xu, X., Chen, R.: Soft pneumatic gripper integrated with multi- configuration and variable‐stiffness functionality. Cogn. Comput. Syst 3, 70–77 (2021). https://doi.org/10.1049/ccs2.12009

    Article  Google Scholar 

  14. Liu, C.-H., Chen, L.-J., Chi, J.-C., Jyun-Yi, W.: Topology optimization design and experiment of a soft pneumatic bending actuator for grasping applications. IEEE Rob. Autom. Lett 7(2), 2086–2093 (2022)

    Article  Google Scholar 

  15. Zhang, H., Liu, W., Yu, M., Hou, Y., Design: Fabrication, and performance test of a New type of soft-robotic gripper for grasping. Sensors 22, 5221 (2022). https://doi.org/10.3390/s22145221

    Article  Google Scholar 

  16. Rad, C., Hancu, O., Lapusan, C.: Data-driven kinematic model of pneunets bending actuators for soft grasping tasks. Actuators 11, 58 (2022). https://doi.org/10.3390/act11020058

  17. Connolly, F., Polygerinos, P., Walsh, C.J., Bertoldi, K.: Mechanical programming of soft actuators by varying fiber angle. Soft Robot. 2:26–32 (2015)

  18. Parness, A., Soto, D., Esparza, N., Gravish, N., Wilkinson, M., Cutkosky, M.: A microfabricated wedge-shaped adhesive array displaying gecko-like dynamic adhesion, directionality and long lifetime. J. Royal Soc. Interface 6(41), 1223–1232 (2009)

    Article  Google Scholar 

  19. Rus, Tolley, M.T.: Design, fabrication, and control of soft robots. Nature 521(7553), 467–475 (2015)

    Article  Google Scholar 

  20. Ilievski, F., Mazzeo, A.D., Shepherd, R.F., Chen, X., Whitesides, G.M.: Soft robotics for chemists. Angew Chem. Int. Ed. Engl 50(8), 1890–1895 (2011)

    Article  Google Scholar 

  21. 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 

  22. Moseley, P., Florez, J.M., Sonar, H.A., Agarwal, G., Curtin, W., Paik, J.: Modeling, design, and development of soft pneumatic actuators with finite element method. Adv. Eng. Mater 18(6), 978–988 (2016)

    Article  Google Scholar 

  23. de Payrebrune, K.M., Oliver, M., O’Reilly: On constitutive relations for a rod-based model of a pneu-net bending actuator. Extreme Mech. Lett 8, 38–46 (2016)

    Article  Google Scholar 

  24. Hiller, J.: Automatic design and manufacture of soft robots. IEEE Trans. Robot 28(2), 457–466 (2011)

    Article  Google Scholar 

  25. Elango, N., Faudzi, A.: A review article: Investigations on soft materials for soft robot manipulations. Int. J. Adv. Manuf. Technol 80, 1027–1037 (2015). https://doi.org/10.1007/s00170-015-7085-3

    Article  Google Scholar 

  26. Cheng, N.G., Gopinath, A., Wang, L., Iagnemma, K., Hosoi, A.E.: Thermally tunable, self-healing composites for soft robotic applications. Macromol. Mater. Eng 299, 1279–1284 (2014). https://doi.org/10.1002/mame.201400017

    Article  Google Scholar 

  27. Suzumori, K., Iikura, S., Tanaka, H.: Development of flexible microactuator and its applications to robotic mechanisms. In: Proc. 1991 IEEE Int. Conf. Robot. Autom., Sacramento, pp. 1622–1627 (1991)

  28. Homberg, B.S., Katzschmann, R.K., Dogar, M.R., Rus, D.: Haptic identification of objects using a modular soft robotic gripper, In: Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., Hamburg, 2015, pp. 1698–1705 (2015)

  29. Hao, Y., Gong, Z., Xie, Z., Guan, S., Yang, X., Ren, Z., Wang, T., Wen, L.: Universal soft pneumatic robotic gripper with variable effective length. In: 2016 35th Chinese control conference (CCC), pp. 6109–6114. IEEE (2016)

  30. Wang, Z., Or, K., Hirai, S.: A dual-mode soft gripper for food packaging. Robot. Auton. Syst 125, 103427 (2020)

    Article  Google Scholar 

  31. Yap, H.K., Lim, J.H., Nasrallah, F., Goh, J.C.H., Yeow, R.C.H.: A soft exoskeleton for hand assistive and rehabilitation application using pneumatic actuators with variable stiffness. In: Proc. 2015 IEEE Int. Conf. Robot. Autom., Seattle, pp. 4967–4972 (2015)

  32. Katzschmann, R.K., Marchese, A.D., Rus, D.: Autonomous object manipulation using a soft planar grasping manipulator. Soft Robot 2(4), 155–164 (2015)

    Article  Google Scholar 

  33. Deimel, Brock, O.: A novel type of compliant and underactuated robotic hand for dexterous grasping. Int. J. Rob. Res 35, 1–3 (2016)

    Article  Google Scholar 

  34. Marchese, A.D., Katzschmann, R.K., Rus, D.: A recipe for soft fluidic elastomer robots. Soft Robot 2(1), 7–25 (2015). https://doi.org/10.1089/soro.2014.0022

    Article  Google Scholar 

  35. Polygerinos, P., et al.: Soft robotics: Review of fluid-driven intrinsically soft devices; manufacturing, sensing, control, and applications in human-robot interaction. Adv. Eng. Mater. 19(12):Art. no. 1700016 (2017)

  36. Glick, P., Suresh, S.A., Ruffatto, D., Cutkosky, M., Tolley, M.T., Parness, A.: A soft robotic gripper with gecko-inspired Adhesive. IEEE Robot. Autom. Lett 3(2), 903–910 (2018). https://doi.org/10.1109/LRA.2018.2792688

    Article  Google Scholar 

  37. Müller, A., Aydemir, M., Glodde, A., Dietrich, F.: Design approach for heavy-duty soft-robotic-gripper. Procedia CIRP 91, 301–305 (2020). https://doi.org/10.1016/j.procir.2020.02.180

    Article  Google Scholar 

  38. MacCurdy, R., Katzschmann, R., Kim, Y., Rus, D.: Printable hydraulics: a method for fabricating robots by 3D co-printing solids and liquids, In: Proc. 2016 IEEE Int. Conf. Robot. and Autom., Stockholm, pp. 3878 – 3785 (2016)

  39. Peele, B.N., Wallin, T.J., Zhao, H., Shepherd, R.F.: 3D printing antagonistic systems of artificial muscle using projection stereolithography. Bioinspir Biomim 10(5), 055003 (2015)

    Article  Google Scholar 

  40. Yap, H.K., Ng, H.Y., Yeow, C.H.: High-force soft printable pneumatics for soft robotic applications. Soft Robot 3(3), 144–158 (2016)

    Article  Google Scholar 

  41. Cho, K.-J., Koh, J.-S., Kim, S., Chu, W.-S.: Yongtaek Hong, and Sung-Hoon Ahn. “Review of manufacturing processes for soft biomimetic robots. Int. J. Precis. Eng. Manuf 10(3), 171–181 (2009)

    Article  Google Scholar 

  42. Lin, H.-T., Leisk, G.G., Trimmer, B.: GoQBot: A caterpillar-inspired soft-bodied rolling robot. Bioinspir. Biomim 6(2), 026007 (2011)

    Article  Google Scholar 

  43. Zongxing, L., Wanxin, L., Liping, Z.: Research development of soft manipulator: A review. Adv. Mech. Eng 12(8), 1687814020950094 (2020)

    Article  Google Scholar 

  44. Zhang, Q.M., Li, H., Poh, M., Xia, F., Cheng, Z.-Y., Xu, H., Huang, C.: An all-organic composite actuator material with a high dielectric constant. Nature 419(6904), 284–287 (2002)

    Article  Google Scholar 

  45. Cham, J.G., Bailey, S.A., Clark, J.E., Full, R.J., Cutkosky, M.R.: Fast and robust: Hexapedal robots via shape deposition manufacturing. Int. J. Robot Res 21, 869–882 (2002). https://doi.org/10.1177/0278364902021010837

    Article  Google Scholar 

  46. Kim, S., Laschi, C., Trimmer, B.: Soft robotics: A bioinspired evolution in robotics. Trends Biotechnol 31(5), 287–294 (2013)

    Article  Google Scholar 

  47. Kim, S., Spenko, M., Trujillo, S., Heyneman, B., Santos, D., Cutkosky, M.R.: Smooth vertical surface climbing with directional adhesion. IEEE Trans. Robot 24, 65–74 (2008). https://doi.org/10.1109/TRO.2007.909786

    Article  Google Scholar 

  48. Dollar, A.M., Howe, R.D.: A robust compliant grasper via shape deposition manufacturing. IEEE/ASME Trans. Mechatron 11, 154–161 (2006). https://doi.org/10.1109/TMECH.2006.871090

    Article  Google Scholar 

  49. Dollar, A.M., Wagner, C.R., Howe, R.D.: Embedded sensors for biomimetic robotics via shape deposition manufacturing. In: The First IEEE/RASEMBS International Conference on Biomedical Robotics and Biomechatronics, 2006. BioRob 2006 (Pisa: IEEE), 763–768 (2006)

  50. Xia, Y.: Whitesides. “Soft lithography. Angew. Chem. Int. Ed 37(5), 550–575 (1998)

    Article  Google Scholar 

  51. Shepherd, R.F., Ilievski, F., Choi, W., Morin, S.A., Stokes, A.A., Mazzeo, A.D., Chen, X., Wang, M., George, M.: Whitesides. Multigait soft robot. Proc. Natl. Acad Sci. 108(51):20400–20403 (2011)

  52. Shepherd, R.F., Adam, A., Stokes, J., Freake, J., Barber, P.W., Snyder, A.D., Mazzeo, L., Cademartiri, S.A., Morin, George, M.: Whitesides. “Using explosions to power a soft robot. Angew. Chem. Int. Ed 52(10), 2892–2896 (2013)

    Article  Google Scholar 

  53. Lipson, H., Kurman, M.: Fabricated: The new World of 3D Printing. Wiley, Hoboken (2013)

  54. Rossiter, J., Walters, P., Stoimenov, B.: Printing 3D dielectric elastomer actuators for soft robotics. In: SPIE Smart Structures and Materials Non-destructive Evaluation and Health Monitoring, pp. 72870H-72870H. International Society for Optics and Photonics), San Diego (2009)

    Google Scholar 

  55. Umedachi, T., Vikas, V., Trimmer, B.A.: Highly deformable 3-d printed soft robot generating inching and crawling locomotions with variable friction legs. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (Tokyo: IEEE), 4590–4595. (2013)

  56. Bartlett, N.W., Tolley, M.T., Johannes, J.T.B., Weaver, J.C., Mosadegh, B., Bertoldi, K., Whitesides, G.M., Wood, R.J.: A 3D-printed, functionally graded soft robot powered by combustion. Science 349(6244), 161–165 (2015)

    Article  Google Scholar 

  57. Wehner, M., Truby, R.L., Daniel, J., Fitzgerald, B., Mosadegh, G.M., Whitesides, J.A., Lewis: and Robert J. Wood. An integrated design and fabrication strategy for entirely soft, autonomous robots. Nature 536(7617):451–455. (2016)

  58. Wu, W., DeConinck, A., Jennifer, A.: Lewis. Omnidirectional printing of 3D microvascular networks. Adv. Mater. 23(24):H178-H183 (2011)

  59. Muth, J.T., Daniel, M., Vogt, R.L., Truby, Y., Mengüç, D.B., Kolesky, R.J., Wood, Jennifer, A., Lewis: Embedded 3D printing of strain sensors within highly stretchable elastomers. Adv, Mater. 26(36):6307–6312 (2014)

  60. Zhang, J., Jackson, A., Kramer, R.: A modular, reconfigurable mold for a soft robotic gripper design activity. Front. Rob. AI 4, 46 (2017)

    Article  Google Scholar 

  61. Liu, S., Wang, F., Liu, Z., Zhang, W., Tian, Y., Zhang, D.: A two-finger soft-robotic gripper with enveloping and pinching grasping modes. In: IEEE/ASME Trans. Mechatron. 26(1):146–155 (2021). https://doi.org/10.1109/TMECH.2020.3005782

  62. Galloway, K.C., Kaitlyn, P., Becker, B., Phillips, J., Kirby, S., Licht, D., Tchernov, R.J., Wood, Gruber, D.F.: Soft robotic grippers for biological sampling on deep reefs. Soft Robot. 3(1):23–33 (2016)

  63. Araromi, O.A., Conn, A.T., Ling, C.S., Rossiter, J.M., Vaidyanathan, R., Burgess, S.C.: Spray deposited multilayered dielectric elastomer actuators. Sens. Actuators A: Phys 167(2), 459–467 (2011)

    Article  Google Scholar 

  64. Coulter, F.B., Ianakiev, A.: 4D printing inflatable silicone structures. 3D Print. Addit. Manuf. 2(3):140–144 (2015)

  65. Nakai, H., Kuniyoshi, Y., Inaba, M., Inoue, H.: IEEE/RSJ Int. Conf. Intelligent Robots and Systems, pp. 2025–2030. IEEE, Piscataway (2002)

    Google Scholar 

  66. Stuart, H., Wang, S., Khatib, O., Mark, R.C.: The ocean one hands: an adaptive design for robust marine manipulation. Int. J. Robot. Res. 36(2), 150–166 (2017)

  67. Odhner, L.U., Leif, P., Jentoft, M.R., Claffee, N., Corson, Y., Tenzer, R.R., Ma, M., Buehler, R., Kohout, R.D., Howe, Aaron, M.: Dollar. “A compliant, underactuated hand for robust manipulation. Int. J. Robot. Res 33(5), 736–752 (2014)

    Article  Google Scholar 

  68. Galloway, K.C., Polygerinos, P., Walsh, C.J., Wood, R.J.: Mechanically programmable bend radius for fiber-reinforced soft actuators,. In: 16th International Conference on Advanced Robotics (ICAR), Montevideo, Uruguay, pp. 1–6 (2013)

  69. Lau, G.-K., Heng, K.-R., Ahmed, A.S., Shrestha, M.: Dielectric elastomer fingers for versatile grasping and nimble pinching. Appl. Phys. Lett 110, 18 (2017)

    Article  Google Scholar 

  70. Hamburg, E., Vunder, V., Johanson, U., Kaasik, F., Aabloo, A.: Soft shape-adaptive gripping device made from artificial muscle. In: Electroactive Polymer Actuators and Devices (EAPAD) 2016, vol. 9798, pp. 296–302. SPIE (2016)

  71. Deole, U., Lumia, R., Shahinpoor, M.: Design and test of IPMC artificial muscle microgripper. J. Micro-Nano Mechatronics 4(3), 95–102 (2008)

    Article  Google Scholar 

  72. Jin, H., Dong, E., Xu, M., Liu, C., Alici, G.: Soft and smart modular structures actuated by shape memory alloy (SMA) wires as tentacles of soft robots. Smart Mater. Struct 25(8), 085026 (2016)

    Article  Google Scholar 

  73. She, Y., Li, C., Cleary, J., Hai-Jun, S.: Design and fabrication of a soft robotic hand with embedded actuators and sensors. J. Mech. Robot. 7(2) (2015)

  74. Amend, J., Cheng, N., Fakhouri, S., Culley, B.: Soft robotics commercialization: Jamming grippers from research to product. Soft Robot. 3(4):213–222 (2016)

  75. Shintake, J., Schubert, B., Rosset, S., Shea, H.R., Floreano, D.: Variable stiffness actuator for soft robotics using dielectric elastomer and low-melting-point alloy. 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1097–1102 (2015)

  76. McCoul, D., Rosset, S., Besse, N., Shea, H.: Smart Mater. Struct 26, 25015 (2017)

    Article  Google Scholar 

  77. Hubbard, A.M., Russell, W., Mailen, M.A., Zikry, M.D., Dickey, Genzer, J.: Controllable curvature from planar polymer sheets in response to light. Soft Matter 13(12), 2299–2308 (2017)

    Article  Google Scholar 

  78. Shintake, J., Rosset, S., Schubert, B., Floreano, D., Shea, H.: Versatile soft grippers with intrinsic electroadhesion based on multifunctional polymer actuators. Adv. Mater. 28(2):231–238 (2016)

  79. Mengüç, Y., Yang, S.Y., Kim, S., Rogers, J.A., Sitti, M.: Gecko-inspired controllable adhesive structures applied to micromanipulation. Adv. Funct. Mater 22(6), 1246–1254 (2012)

    Article  Google Scholar 

  80. Hawkes, E.W., Christensen, D.L., Han, A.K., Jiang, H., Cutkosky, M.R.: Grasping without squeezing: Shear adhesion gripper with fibrillar thin film. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 2305–2312. IEEE (2015)

  81. Reddy, A., Narayana, N., Maheshwari, D.K., Sahu, Ananthasuresh, G.K.: Miniature compliant grippers with vision-based force sensing. IEEE Trans. Robot 26(5), 867–877 (2010)

    Article  Google Scholar 

  82. Paek, J., Cho, I., Kim, J.: Microrobotic tentacles with spiral bending capability based on shape-engineered elastomeric microtubes. Sci. Rep. 5(1):1–11 (2015)

  83. Walker, I.D., Darren, M., Dawson, T., Flash, F.W., Grasso, Roger, T., Hanlon, B., Hochner, W.M., Kier, C.C., Pagano, C.D., Rahn, Qiming, M.: Zhang. Continuum robot arms inspired by cephalopods. In: Unmanned Ground Vehicle Technology VII, vol. 5804, pp. 303–314. SPIE (2005)

  84. Imamura, H., Kadooka, K., Taya, M.: A variable stiffness dielectric elastomer actuator based on electrostatic chucking. Soft Matter 13(18):3440–3448  (2017)

  85. Bar-Cohen, Y., Xue, T., Shahinpoor, M., Simpson, J., Smith, J., Proc. Robotics ‘98, American Society of Civil Engineers, ASCE, Albuquerque, pp. 15–21 (1998)

  86. Krulevitch, P., Lee, A.P., Ramsey, P.B., Trevino, J.C., Hamilton, J., Allen Northrup, M.: Thin film shape memory alloy microactuators. J. Microelectromech. Syst 5(4), 270–282 (1996)

    Article  Google Scholar 

  87. Wang, W., Rodrigue, H., Kim, H.-I., Han, M.-W., Sung-Hoon, A.: Soft composite hinge actuator and application to compliant robotic gripper. Compos. Part B: Eng 98, 397–405 (2016)

    Article  Google Scholar 

  88. Amend, J.R., Brown, E., Rodenberg, N., Jaeger, H.M., Lipson, H.: A positive pressure universal gripper based on the jamming of granular material. IEEE Trans. Robot 28(2), 341–350 (2012)

    Article  Google Scholar 

  89. Cheng, N.G., Lobovsky, M.B., Keating, S.J., Setapen, A.M., Gero, K.I., Hosoi, A.E., Iagnemma, K.D.: IEEE Int. Conf. Robotics and Automation, pp. 4328–4333. IEEE, Piscataway, (2012)

    Google Scholar 

  90. Pettersson, A., Davis, S., Gray, J.O., Dodd, T.J., Ohlsson, T.: Design of a magnetorheological robot gripper for handling of delicate food products with varying shapes. J. Food Eng 98(3), 332–338 (2010)

    Article  Google Scholar 

  91. Ge, Q., Sakhaei, A.H., Lee, H., Dunn, C.K., Nicholas, X., Fang: and Martin L. Dunn. Multimaterial 4D printing with tailorable shape memory polymers. Sci. Rep. 6(1):1–11 (2016)

  92. Yang, Y., Chen, Y., Wei, Y., Li, Y.: Novel design and three-dimensional printing of variable stiffness robotic grippers. J. Mech. Rob 8(6), 061010 (2016)

    Article  Google Scholar 

  93. Liang, X., Sun, Y., Wang, H., Yeow, R.C.H., Kukreja, S.L., Thakor, N., Ren, H.: IEEE RAS EMBS Int. Conf. Biomedical Robotics and Biomechatronics, pp. 401–440. IEEE, Piscataway (2016)

    Google Scholar 

  94. Song, S., Drotlef, D.-M., Majidi, C. Sitti, M.: Controllable load sharing for soft adhesive interfaces on three-dimensional surfaces. Proc. Natl. Acad. Sci. 114(22):E4344-E4353(2017)

  95. Krahn, J.M., Fabbro, F., Menon, C.: A soft-touch gripper for grasping delicate objects. IEEE/ASME Trans. Mechatron 22(3), 1276–1286 (2017)

    Article  Google Scholar 

  96. Shujiro, D.O.H.T.A., Takashi, S.H.I.N.O.H.A.R.A., Hisashi, M.A.T.S.U.S.H.I.T.A.: Development of a pneumatic rubber hand. In: Proceedings of the JFPS International Symposium on Fluid Power, vol. no. 5 – 1, pp. 49–54. The Japan Fluid Power System Society, 2002 (2002)

  97. Jain, R.K., Datta, S., Majumder, S., Dutta, A.: Two IPMC fingers based micro gripper for handling. Int. J. Adv. Robot. Syst. (2011)

  98. Hao, Y., Wang, T., Fang, X., Yang, K., Mao, L., Guan, J., Wen, L.: Chinese Control Conf, pp. 6781–6786. CCC, IEEE, Piscataway (2017)

    Google Scholar 

  99. Kenaley, G.L., Cutkosky, M.R.: In: Proc. 1989 Int. Conf. Robotics and Automation. IEEE, Piscataway, pp. 132–136 (1989)

  100. Yang, Y., Chen, Y., Li, Y., Chen, M.Z.Q., Wei, Y.: Bioinspired robotic fingers based on pneumatic actuator and 3D printing of smart material. Soft Robot. 4(2):147–162 (2017)

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

    Article  Google Scholar 

  102. Ongaro, F., Scheggi, S., Yoon, C.K., van den Brink, F., Oh, S.H., Gracias, D.H., Misra, S.: Autonomous planning and control of soft untethered grippers in unstructured environments. J. Micro-Bio Robot. 12(1):45–52  (2017)

  103. Faudzi, A.A., Azmi, N.I., Sayahkarajy, M., Xuan, W.L., Suzumori, K.: Soft manipulator using thin McKibben actuator. In: 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 334–339. IEEE (2018)

  104. Hsiao, L.-Y., Jing, L., Li, K., Yang, H., Li, Y., Chen, P.-Y.: Carbon nanotube-integrated conductive hydrogels as multifunctional robotic skin. Carbon 161, 784–793 (2020)

    Article  Google Scholar 

  105. Mosadegh, B., Polygerinos, P., Keplinger, C., Wennstedt, S., Shepherd, R.F., Gupta, U., Shim, J., Bertoldi, K., Walsh, C.J., Whitesides, G.M.: Pneumatic networks for soft robotics that actuate rapidly. Adv. Funct. Mater 24(15), 2163–2170 (2014)

    Article  Google Scholar 

  106. Ellis, D., Rostin, M.P., Venter, Venter, G.: Soft pneumatic actuator with bimodal bending response using a single pressure source. Soft Rob 8(4), 478–484 (2021)

    Article  Google Scholar 

  107. Gu, G., Wang, D., Ge, L., Zhu, X.: Analytical modeling and design of generalized pneu-net soft actuators with three-dimensional deformations. Soft Robot. 8(4):462–477 (2021)

  108. Jolaei, M., Hooshiar, A., Dargahi, J., Packirisamy, M.: Toward task autonomy in robotic cardiac ablation: Learning-based kinematic control of soft tendon-driven catheters. Soft Rob 8(3), 340–351 (2021)

    Article  Google Scholar 

  109. Dang, Y., Liu, Y., Hashem, R., Bhattacharya, D., Allen, J., Stommel, M., Cheng, L.K., Xu, W.: SoGut: A soft robotic gastric simulator. Soft Rob 8(3), 273–283 (2021)

    Article  Google Scholar 

  110. Kim, Y., Cha, Y.: Soft pneumatic gripper with a tendon-driven soft origami pump. Front. Bioeng. Biotechnol 8, 461 (2020)

    Article  Google Scholar 

  111. Xu, Z., Todorov, E.: Design of a highly biomimetic anthropomorphic robotic hand towards artificial limb regeneration. IEEE International Conference on Robotics and Automation (ICRA), 3485–3492 (2016)

  112. De Barrie, D., Margetts, R., Goher, K.: Simpa: Soft-grasp infant myoelectric prosthetic arm. IEEE Rob. Autom. Lett 5(2), 699–704 (2020)

    Article  Google Scholar 

  113. Shih, B., Drotman, D., Christianson, C., Huo, Z., White, R., Christensen, H.I., Tolley, M.T.: Custom soft robotic gripper sensor skins for haptic object visualization. In 2017 IEEE/RSJ International Conference On Intelligent Robots and Systems (IROS), pp. 494–501. IEEE (2017)

  114. Zhou, J., Chen, S., Wang, Z.: A soft-robotic gripper with enhanced object adaptation and grasping reliability. IEEE Rob. Autom. Lett 2(4), 2287–2293 (2017)

    Article  Google Scholar 

  115. Yirmibesoglu, O., Dogan, J., Morrow, S., Walker, W., Gosrich, R., Cañizares, H., Kim, U., Daalkhaijav, C., Fleming, C., Branyan, Menguc, Y.:  Direct 3D printing of silicone elastomer soft robots and their performance comparison with molded counterparts. In: 2018 IEEE International Conference on Soft Robotics (RoboSoft), pp. 295–302. IEEE (2018)

  116. Zhong, G., Hou, Y., Dou, W.: A soft pneumatic dexterous gripper with convertible grasping modes. Int. J. Mech. Sci 153, 445–456 (2019)

    Article  Google Scholar 

  117. Yang, H., Chen, Y., Sun, Y., Hao, L.: A novel pneumatic soft sensor for measuring contact force and curvature of a soft gripper. Sens. Actuators A: Phys 266, 318–327 (2017)

    Article  Google Scholar 

  118. Chen, Y., Guo, S., Li, C., Yang, H., Hao, L.: Size recognition and adaptive grasping using an integration of actuating and sensing soft pneumatic gripper. Robot. Auton. Syst 104, 14–24 (2018)

    Article  Google Scholar 

  119. Batsuren, K., Yun, D.: Soft robotic gripper with chambered fingers for performing in-hand manipulation. Appl. Sci 9(15), 2967 (2019)

    Article  Google Scholar 

  120. Wang, Z., Kanegae, R., Hirai, S.: Circular shell gripper for handling food products. Soft Robot. 8(5):542–554 (2021)

  121. Manti, M., Hassan, T., Passetti, G., D’Elia, N., Laschi, C., Cianchetti, M.: A bioinspired soft robotic gripper for adaptable and effective grasping. Soft Rob 2(3), 107–116 (2015)

    Article  Google Scholar 

  122. Seibel, A., Yıldız, M., Zorlubaş, B.: A gecko-inspired soft passive gripper. Biomimetics 5(2):12 (2020)

  123. Teeple, C.B., Koutros, T.N., Graule, M.A., Wood, R.J.: Multi-segment soft robotic fingers enable robust precision grasping. Int. J. Robot. Res 39(14), 1647–1667 (2020)

    Article  Google Scholar 

  124. Zhu, M., Xie, M., Lu, X., Okada, S., Kawamura, S.: A soft robotic finger with self-powered triboelectric curvature sensor based on multi-material 3D printing. Nano Energy 73, 104772 (2020)

    Article  Google Scholar 

  125. Galley, A., Knopf, G.K., Kashkoush, M.: Pneumatic hyperelastic actuators for grasping curved organic objects. Actuators. 8(4):76 (2019)

  126. Breitman, P., Matia, Y.: Gat. “Fluid mechanics of pneumatic soft robots. Soft Rob 8(5), 519–530 (2021)

    Article  Google Scholar 

  127. Hohimer, C.J., Wang, H., Bhusal, S., Miller, J., Mo, C., Karkee, M.: Design and field evaluation of a robotic apple harvesting system with a 3D-printed soft-robotic end-effector. Trans. ASABE 62(2), 405–414 (2019)

    Article  Google Scholar 

  128. Grabit Inc:.: Grabit electroadhesion robotic each pick gripper - boxes, bags, cans, bare goods. https://www.youtube.com/watch?v=RiAiNjd6ukk. Accessed Nov 2017

  129. Tan, N., Gu, X., Ren, H.: Design, characterization and applications of a novel soft actuator driven by flexible shafts. Mech. Mach. Theory 122 (2018)

  130. Akbari, S., Sakhaei, A.H., Panjwani, S., Kowsari, K., Serjouei, A., Ge, Q.: Multimaterial 3D printed soft actuators powered by shape memory alloy wires. Sens. Actuators A: Phys 290, 177–189 (2019)

    Article  Google Scholar 

  131. Navas, E., Fernández, R., Sepúlveda, D., Armada, M., Gonzalez-de-Santos, P.: Soft grippers for automatic crop harvesting: A review. Sensors. 21(8):2689 (2021)

  132. Wilson, M.: Festo drives automation forwards. Assembly Automation (2011)

  133. Terryn, S., Brancart, J., Lefeber, D., Van Assche, G., Vanderborght, B.: Self-healing soft pneumatic robots. Sci. Rob 2(9), eaan4268 (2017)

    Article  Google Scholar 

  134. Gong, Z., Chen, B., Liu, J., Fang, X., Liu, Z., Wang, T., Li, Wen: An opposite-bending-and-extension soft robotic manipulator for delicate grasping in shallow water. Front. Rob. AI 6, 26 (2019)

    Article  Google Scholar 

  135. 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 

  136. Bozhkov, L., Georgieva, P.: ‘Overview of deep learning architectures for EEG-based brain imaging,’’ in Proc. Int. Joint Conf. Neural Netw. (IJCNN). Rio de Janeiro, Brazil: IEEE, pp. 1–7 (2018)

  137. Shen, X., Kim, H.-S., Komatsu, S., Markman, A., Javidi, B.: ‘Spatial-temporal human gesture recognition under degraded conditions using three-dimensional integral imaging: An overview. In: Proc. 17th Workshop Inf. Opt. (WIO). Québec, QC, Canada: IEEE, pp. 13938–13951 (2018)

  138. Gite, B., Nikhal, K., Palnak, F.: ‘Evaluating facial expressions in real time. In: Proc. Intell. Syst. Conf. (IntelliSys). IEEE, London, pp. 849–855 (2017)

  139. Panchal, P., Raman, V.C., Mantri, S.: ‘Plant diseases detection and classification using machine learning models. In: Proc. 4th Int. Conf. Comput. Syst. Inf. Technol. Sustain. Solution (CSITSS). Bengaluru, India: IEEE, pp. 1–6 (2019)

  140. Gao, M., Jiang, J., Zou, G., John, V., Liu, Z.: RGB-D-Based object recognition using multimodal convolutional neural networks: A survey. IEEE Access. 7, 43110–43136 (2019)

  141. Wang, H., Du, H., Zhao, Y., Yan, J.: A comprehensive overview of person re-identification approaches. IEEE Access 8, 45556–45583 (2020)

    Article  Google Scholar 

  142. Celebi, M.E., Codella, N., Halpern, A.: Dermoscopy image analysis: Overview and future directions. IEEE J. Biomed. Health Inform. 23(2), 474–478 (2019)

  143. Greenspan, H., van Ginneken, B., Summers, R.M.: Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Trans. Med. Imag. 35(5), 1153–1159 (2016)

  144. Bai, Q., Li, S., Yang, J., Song, Q., Li, Z., Zhang, X.: Object detection recognition and robot grasping based on machine learning: a survey. IEEE Access. 8, 181855–181879 (2020). https://doi.org/10.1109/ACCESS.2020.3028740

  145. Zhang, Q., Yang, L.T., Chen, Z.: Deep computation model for unsupervised feature learning on big data. IEEE Trans. Services Comput. 9(1), 161–171 (2016)

  146. Wang, W., Zhang, M.: Tensor deep learning model for heterogeneous data fusion in internet of things. IEEE Trans. Emerg. Topics Comput. Intell. 4(1), 32–41 (2020)

  147. Lei, Y., Jia, F., Lin, J., Xing, S., Ding, S.X.: An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Trans. Ind. Electron. 63(5), 3137–3147 (2016)

  148. Ergene, M.C., Durdu, A.: Robotic hand grasping of objects classified by using support vector machine and bag of visual words. In: Proc. Int. Artif. Intell. Data Process. Symp. (IDAP), pp. 1–5. IEEE, Malatya (2017)

  149. Yuan, H., Li, D., Wu, J.: Efficient learning of grasp selection for five-finger dexterous hand. In: Proc. IEEE 7th Annu. Int. Conf. CYBER Technol. Autom., Control, Intell. Syst. (CYBER), pp. 1101–1106. IEEE, Honolulu (2017)

  150. Hu, Y., Li, Z., Li, G., Yuan, P., Yang, C., Song, R.:  Development of sensory-motor fusion-based manipulation and grasping control for a robotic hand-eye system.  IEEE Trans. Syst. Man. Cybern. Syst. 47(7), 1169–1180 (2017)

  151. Harada, K., Tsuji, T., Nagata, K., Yamanobe, N., Onda, H., Yoshimi, T., Kawai, Y.: Object placement planner for robotic pick and place tasks. In: Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 980–985. IEEE, Vilamoura (2012)

  152. Verma, N.K., Mustafa, A., Salour, A.: Stereo-vision based object grasping using robotic manipulator. In: Proc. 11th Int. Conf. Ind. Inf. Syst. (ICIIS), pp. 95–100. IEEE, Roorkee (2016)

  153. Song, H.O., Fritz, M., Goehring, D., Darrell, T.: Learning to detect visual grasp affordance. IEEE Trans. Autom. Sci. Eng. 13(2), 798–809 (2016)

  154. Mattar, E.: PCA Learning for Non-brain Waves-Controlled Robotic Hand (Prosthesis): Grasp Stabilization and Control. In: Proc. UKSimAMSS 16th Int. Conf. Comput. Modeling Simulation, pp. 211–216. IEEE, Cambridge (2014)

  155. Kumra, S., Kanan, C.: Robotic grasp detection using deep convolutional neural networks. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 769–776. IEEE (2017)

  156. Jeon, M.: Robotic arts: Current practices, potentials, and implications. Multimodal Technol. Interact 1(2), 5 (2017)

    Article  Google Scholar 

  157. Caldera, S., Rassau, A., Chai, D.: Review of deep learning methods in robotic grasp detection. Multimodal Technol. Interact 2(3), 57 (2018)

    Article  Google Scholar 

  158. Kim, S.-H., Geem, Z.W., Gi-Tae, H.: Hyperparameter optimization method based on harmony search algorithm to improve performance of 1D CNN human respiration pattern recognition system. Sensors 20, 13 (2020)

    Google Scholar 

  159. Sundaram, S., Kellnhofer, P., Li, Y., Zhu, J.-Y., Torralba, A., Matusik, W.: Learning the signatures of the human grasp using a scalable tactile glove. Nature 569(7758), 698–702 (2019)

    Article  Google Scholar 

  160. Calandra, R., Owens, A., Jayaraman, D., Lin, J., Yuan, W., Malik, J., Adelson, E.H., Levine, S.: More than a feeling: Learning to grasp and regrasp using vision and touch. IEEE Rob. Autom. Lett 3(4), 3300–3307 (2018)

    Article  Google Scholar 

  161. Yuan, W., Zhu, C., Owens, A., Srinivasan, M.A., Adelson, E.H.: Shape-independent hardness estimation using deep learning and a gelsight tactile sensor. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 951–958. IEEE (2017)

  162. Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE. 86(11), 2278–2324 (1998)

  163. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM. 60(6), 84–90 (2017)

  164. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection,’’ in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 779–788. IEEE, Las Vegas (2016)

  165. Cheng, H., Meng, M.Q.-H.: A grasp pose detection scheme with an end-to-end CNN regression approach. In: Proc. IEEE Int. Conf. Robot. Biomimetics (ROBIO), pp. 544–549. IEEE, Kuala Lumpur (2018)

  166. Zunjani, F.H., Sen, S., Shekhar, H., Powale, A., Godnaik, D., Nandi, G.C.: ‘Intent-based object grasping by a robot using deep learning, In: Proc. IEEE 8th Int. Advance Comput. Conf. (IACC), pp. 246–251. IEEE, Greater Noida (2018)

  167. Corona, E., Alenya, G., Gabas, A., Torras, C.: Active garment recognition and target grasping point detection using deep learning. Pattern Recognit. 74, 629–641 (2018)

  168. Yu, Y., Zhang, K., Liu, H., Yang, L., Zhang, D.: Real-time visual localization of the picking points for a ridge-planting strawberry harvesting robot. IEEE Access 8, 116556–116568 (2020)

    Article  Google Scholar 

  169. Yamazaki, K.: Selection of grasp points of cloth product on a table based on shape classification feature. In: Proc. IEEE Int. Conf. Inf. Autom. (ICIA), pp. 136–141, IEEE, Macau (2017)

  170. Park, D., Chun, S.Y.: Classification based grasp detection using spatial transformer network. arXiv preprint arXiv:1803.01356 (2018)

  171. Pas, A., Gualtieri, M., Saenko, K., Platt, R.: Grasp pose detection in Point Clouds. Int. J. Robot. Res 36(December), 13–14 (2017)

    Google Scholar 

  172. Lu, Q., Chenna, K., Sundaralingam, B., Hermans, T.: Planning multi-fingered grasps as probabilistic inference in a learned deep network. In: Robotics Research, pp. 455–472. Springer, Cham (2020)

    Chapter  Google Scholar 

  173. Mahler, J., Liang, J., Niyaz, S., Laskey, M., Doan, R., Liu, X., Ojea, J.A., Goldberg, K.: Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics. arXiv preprint arXiv:1703.09312 (2017)

  174. Bicchi, A., Kumar, V.: Robotic grasping and contact: A review. In: Proceedings 2000 ICRA Millennium conference. IEEE International conference on robotics and automation. Symposia proceedings (Cat. No. 00CH37065), vol. 1, pp. 348–353. IEEE (2000) 

  175. Redmon, J., Angelova, A.: Real-time grasp detection using convolutional neural networks. In 2015 IEEE international conference on robotics and automation (ICRA), pp. 1316–1322. IEEE (2015)

  176. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778 (2016)

  177. Dollar, A.M., Howe, R.D.: The SDM Hand: A Highly Adaptive Compliant Grasper for Unstructured Environments, vol. 54. Springer, Berlin, Heidelberg (2009)

    Google Scholar 

  178. Lan, C.-C., Lin, C.-M., Chen-Hsien, F.: A self-sensing microgripper module with wide handling ranges. IEEE/ASME Trans. Mechatron 16(1), 141–150 (2010)

    Article  Google Scholar 

  179. Wang, W., Sung-Hoon, A.: Shape memory alloy-based soft gripper with variable stiffness for compliant and effective grasping. Soft Robot. 4(4):379–389 (2017)

  180. Schaler, E.W., Ruffatto, D.F., Glick, P.E., White, V., Parness, A.: An electrostatic gripper for flexible objects. 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1172–1179 (2017)

  181. Song, S., Sitti, M.: Soft grippers using micro-fibrillar adhesives for transfer printing. Adv. Mater 26(28), 4901–4906 (2014)

    Article  Google Scholar 

  182. Soft Robotics Inc: Soft Robotics. https://www.softroboticsinc.com/. Accessed Nov 2017

  183. Festo Co:. Ltd., OctopusGripper | Festo Corporate. https://www.festo.com/group/en/cms/12745.htm. Accessed Nov 2017

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Prabhu Sethuramalingam.

Ethics declarations

Ethical Approval

Not applicable.

Consent to Participate

Not applicable.

Consent to Publish

Not applicable.

Competing Interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sut, D.J., Sethuramalingam, P. Soft Manipulator for Soft Robotic Applications: a Review. J Intell Robot Syst 108, 10 (2023). https://doi.org/10.1007/s10846-023-01877-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10846-023-01877-4

Keywords

Navigation