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A variable stiffness design method for soft robotic fingers based on grasping force compensation and linearization

Published online by Cambridge University Press:  10 May 2024

Xiaowei Shan ,
Litao Xu Open the ORCID record for Litao Xu [Opens in a new window]  and
Xuefei Li
Xiaowei Shan*
Affiliation:
School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China
Litao Xu
Affiliation:
School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China
Xuefei Li
Affiliation:
School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China
*
Corresponding author: Xiaowei Shan; Email: xiaowei.shan@mail.mcgill.ca
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Abstract

Soft fingers play an increasingly important role in robotic grippers to achieve adaptive grasping with variable stiffness features. Previous studies of soft finger design have primarily focused on the optimization of the structural parameters of existing finger structures, but limited efforts have been put into the design methodology from fundamental grasping mechanisms to finger structures with desired grasping force features. To this aim, a fundamental architecture of soft fingers is proposed for analyzing common soft finger features and the influence of the internal structures on the overall grasping performance. In addition, three general performance metrics are introduced to evaluate the grasping performance of soft finger designs. Then, a novel method is proposed to combine the variable stiffness structure with the fundamental architecture to compensate for the grasping force of the finger and linearization. Subsequently, an embodiment design is proposed with a cantilever spring-based variable stiffness (CSVS) mechanism based on the method, and a multi-objective optimization method is employed to optimize the design. Besides, the CSVS features are analyzed through finite element analysis (FEA), and by comparing the grasping performance between the V-shape finger and the CSVS finger, it is demonstrated that the design method can effectively shorten the pre-grasp stage and linearize the grasping force in the post-grasp stage while reducing the likelihood of sliding friction between the finger and the grasped object. Finally, experiments are conducted to validate the accuracy of the FEA model, the effectiveness of the design methodology, and the adaptability of the CSVS finger.

Keywords

soft robotic fingergrasping performancegrasping force compensationvariable stiffnessfinite element analysisdesign methodology
Type
Research Article
Information
Robotica , First View , pp. 1 - 23
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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References

Birglen, L. and Schlicht, T., “A statistical review of industrial robotic grippers,” Robot Comput Integr Manuf 49, 88–97 (2018).CrossRefGoogle Scholar
Dong, M. and Zhang, J., “A review of robotic grasp detection technology,” Robotica 41(12), 3846–3885 (2023).CrossRefGoogle Scholar
Elgeneidy, K., Fansa, A., Hussain, I. and Goher, K., “Structural Optimization of Adaptive Soft Fin Ray Fingers with Variable Stiffening Capability,” In: 3rd IEEE International Conference on Soft Robotics (RoboSoft), (2020) pp. 779–784.Google Scholar
Marwan, Q. M., Chua, S. C. and Kwek, L. C., “Comprehensive review on reaching and grasping of objects in robotics,” Robotica 39(10), 1849–1882 (2021).CrossRefGoogle Scholar
FESTO, MultiChoiceGripper, (2023). https://www.festo.com/net/SupportPortal/Files/333986/Festo_MultiChoiceGripper_en.pdf.Google Scholar
Shintake, J., Cacucciolo, V., Floreano, D. and Shea, H., “Soft robotic grippers,” Adv Mater 30(29), 1707035 (2018).CrossRefGoogle Scholar
Hu, J., Liang, L. and Zeng, B., “Design, modeling, and testing of a soft actuator with variable stiffness using granular jamming,” Robotica 40(7), 2468–2484 (2022).CrossRefGoogle Scholar
Ferrandy, V., Indrawanto, , Ferryanto, F., Sugiharto, A., Franco, E., Garriga-Casanovas, A., Mahyuddin, A. I., Rodriguez y Baena, F., Mihradi, S. and Virdyawan, V., “Modeling of a two-degree-of-freedom fiber-reinforced soft pneumatic actuator,” Robotica 41(12), 3608–3626 (2023).CrossRefGoogle Scholar
Xiao, W., Liu, C., Hu, D., Yang, G. and Han, X., “Soft robotic surface enhances the grasping adaptability and reliability of pneumatic grippers,” Int J Mech Sci 219, 107094 (2022).CrossRefGoogle Scholar
Chen, R., Song, R., Zhang, Z., Bai, L., Liu, F., Jiang, P., Sindersberger, D., Monkman, G. J. and Guo, J., “Bio-inspired shape-adaptive soft robotic grippers augmented with electroadhesion functionality,” Soft Robot 6(6), 701–712 (2019).CrossRefGoogle ScholarPubMed
Chen, R., Zhang, Z., Guo, J., Liu, F., Leng, J. and Rossiter, J., “Variable stiffness electroadhesion and compliant electroadhesive grippers,” Soft Robot 9(6), 1074–1082 (2022).CrossRefGoogle ScholarPubMed
Choi, D.-S., Kim, T.-H., Lee, S.-H., Pang, C., Bae, J. W. and Kim, S.-Y., “Beyond human hand: Shape-adaptive and reversible magnetorheological elastomer-based robot gripper skin,” ACS Appl Mater Interf 12(39), 44147–44155 (2020).CrossRefGoogle ScholarPubMed
Crooks, W., Vukasin, G., O’Sullivan, M., Messner, W. and Rogers, C., “Fin ray® effect inspired soft robotic gripper: From the roboSoft grand challenge toward optimization,” Front Robot AI 3, 70–78 (2016).CrossRefGoogle Scholar
FESTO, Adaptive gripper fingers DHAS, (2023). https://www.festo.com/media/pim/049/D15000100122049.PDF.Google Scholar
Shan, X. and Birglen, L., “Bistable stopper design and force prediction for precision and power grasps of soft robotic fingers for industrial manipulation,” J Mech Design 146(4), 045001 (2024).CrossRefGoogle Scholar
Mouazé, N. and Birglen, L.,” Bistable compliant underactuated gripper for the gentle grasp of soft objects,” Mech Mach Theory 170, 104676 (2022).CrossRefGoogle Scholar
Liu, C.-H., Chiu, C.-H., Hsu, M.-C., Chen, Y. and Chiang, Y.-P., “Topology and size-shape optimization of an adaptive compliant gripper with high mechanical advantage for grasping irregular objects,” Robotica 37(8), 1383–1400 (2019).CrossRefGoogle Scholar
Liu, C.-H., Hsu, M.-C., Chen, T.-L. and Chen, Y., “Optimal design of a compliant constant-force mechanism to deliver a nearly constant output force over a range of input displacements,” Soft Robot 7(6), 758–769 (2020).CrossRefGoogle Scholar
Lee, J. Y., Seo, Y. S., Park, C., Koh, J. S., Kim, U., Park, J., Rodrigue, H., Kim, B. and Song, S. H., “Shape-adaptive universal soft parallel gripper for delicate grasping using a stiffness-variable composite structure,” IEEE Trans Ind Electron 68(12), 12441–12451 (2021).CrossRefGoogle Scholar
Li, L., Xie, F., Wang, T., Wang, G., Tian, Y., Jin, T. and Zhang, Q., “Stiffness-tunable soft gripper with soft-rigid hybrid actuation for versatile manipulations,” Soft Robot 9(6), 1108–1119 (2022).CrossRefGoogle ScholarPubMed
Shan, X. and Birglen, L., “Modeling and analysis of soft robotic fingers using the fin ray effect,” Int J Robot Res 39(14), 1686–1705 (2020).CrossRefGoogle Scholar
Armanini, C., Hussain, I., Iqbal, M. Z., Gan, D., Prattichizzo, D. and Renda, F., “Discrete cosserat approach for closed-chain soft robots: Application to the fin-ray finger,” IEEE Trans Robot 37(6), 2083–2098 (2021).CrossRefGoogle Scholar
Elgeneidy, K., Lightbody, P., Pearson, S. and Neumann, G., “Characterising 3D-Printed Soft Fin Ray Robotic Fingers with Layer Jamming Capability for Delicate Grasping,” In: 2019 2nd IEEE International Conference on Soft Robotics (RoboSoft), (2019) pp. 143–148.Google Scholar
Basson, C. I., Bright, G. and Walker, A. J., “Analysis of Flexible End-Effector for Geometric Conformity in Reconfigurable Assembly Systems: Testing Geometric Structure of Grasping Mechanism for Object Adaptability,” In: 2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech), (2017) pp. 92–97.CrossRefGoogle Scholar
Basson, C. I. and Bright, G., “Geometric Conformity Study of a Fin Ray Gripper Utilizing Active Haptic Control,” In: 2019 IEEE 15th International Conference on Control and Automation (ICCA), (2019) pp. 713–718.Google Scholar
Lee, L. Y., Nurzaman, S. G. and Tan, C. P., “Design and Analysis of a Gripper with Interchangeable Soft Fingers for Ungrounded Mobile Robots,” In: 2019 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), (2019) pp. 221–226.Google Scholar
Shin, J. H., Park, J. G., Kim, D. I. and Yoon, H. S., “A universal soft gripper with the optimized fin ray finger,” Int J Pr Eng Man-Gt 8, 889–899 (2021).Google Scholar
Yao, J., Fang, Y. and Li, L., “Research on effects of different internal structures on the grasping performance of fin ray soft grippers,” Robotica 41(6), 1762–1777 (2023).CrossRefGoogle Scholar
Hota, R. K. and Kumar, C. S., “Effect of design parameters on strong and immobilizing grasps with an underactuated robotic hand,” Robotica 40(11), 3769–3785 (2022).CrossRefGoogle Scholar
Bicchi, A., “Hands for dexterous manipulation and robust grasping: A difficult road toward simplicity,” IEEE Trans Robot Automat 16(6), 652–662 (2000).CrossRefGoogle Scholar
Kragten, G. A. and Herder, J. L., “The ability of underactuated hands to grasp and hold objects,” Mech Mach Theory 45(3), 408–425 (2010).CrossRefGoogle Scholar
Suder, J., Bobovský, Z., Mlotek, J., Vocetka, M., Oščádal, P. and Zeman, Z., “Structural optimization method of a finRay finger for the best wrapping of object,” Appl Sci 11(9), 3858 (2021).CrossRefGoogle Scholar
Kniese, L., “Load carrying element with flexible outer skin,” European Patent Office EP1040999A2, (1999).Google Scholar
Falco, J., Van Wyk, K. and Messina, E., “Performance Metrics and Test Methods for Robotic Hands,” DRAFT NIST Spec Public 1227, 2–2 (2018).Google Scholar
De Barrie, D., Pandya, M., Pandya, H., Hanheide, M. and Elgeneidy, K., “A deep learning method for vision based force prediction of a soft fin ray gripper using simulation data,” Front Robot AI 8, 631371 (2021).CrossRefGoogle ScholarPubMed
Reppel, T. and Weinberg, K., “Experimental determination of elastic and rupture properties of printed ninjaflex,” Tech Mech 38(1), 104–112 (2018).Google Scholar
Yeo, S. H., Yang, G. and Lim, W. B., “Design and analysis of cable-driven manipulators with variable stiffness,” Mech Mach Theory 69, 230–244 (2013).CrossRefGoogle Scholar
Wu, Y.-S. and Lan, C.-C., “Linear variable-stiffness mechanisms based on preloaded curved beams,” J Mech Design 136(12), 122302 (2014).CrossRefGoogle Scholar
Xie, Z., Qiu, L. and Yang, D., “Analysis of a novel variable stiffness filleted leaf hinge,” Mech Mach Theory 144, 103673 (2020).CrossRefGoogle Scholar