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Research on effects of different internal structures on the grasping performance of Fin Ray soft grippers

Published online by Cambridge University Press:  15 February 2023

Jiaqiang Yao ,
Yuefa Fang Open the ORCID record for Yuefa Fang [Opens in a new window]  and
Luquan Li
Jiaqiang Yao
Affiliation:
School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, PR China, 100044
Yuefa Fang*
Affiliation:
School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, PR China, 100044
Luquan Li
Affiliation:
School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, PR China, 100044
*
*Corresponding author. E-mail: yffang@bjtu.edu.cn
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Abstract

Fin Ray soft grippers, as a notable passive compliant structures, can be easily actuated by external devices to adapt their shape to conform to a grasped object. Their unique ability is aided by their V-shaped structure and morphable material utilized by the Fin Ray finger. Thus, when the internal structure changes, the adaptability and grasping abilities also change. However, related works focus on the effects of changing key parameters on the grasping performance based on the Festo structure, and few works have explored the effects of changing the internal structure. To close the research gap, four different Fin Ray structures are presented in this article, and a parameter determination process was carried out by maximizing their adaptability by investigating the key parameters of each structure through finite element analysis. Then, the force responses of four selected Fin Ray structures are analyzed and experimentally validated. The results show that the No Internal Filling structure obtained by omitting the crossbeams is ideal for grasping delicate targets with the best adaptability and the minimum resultant force. The cross structure attained by adding vertical beams connected to crossbeams decreases the adaptability of the Fin Ray finger but significantly increases the contact force. The unsymmetric design of the branched structure significantly enhances the final contact force while improving the passive adaptation to objects. Thus, the application of the Fin Ray finger ranges from adaptive delicate grasping tasks to high-force manipulation tasks.

Keywords

soft roboticssoft robotic gripperadaptive graspingFin Ray structurefinite element analysis

Information

Type
Research Article
Information
Robotica , Volume 41 , Issue 6 , June 2023 , pp. 1762 - 1777
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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References

Hughes, J., Culha, U., Giardina, F., F. Guenther, F. Rosendo, A. Rosendo and F. lida, “Soft manipulators and grippers: A review,” Front. Robot. AI 3, 69 (2016).CrossRefGoogle Scholar
Bogue, R., “Flexible and soft robotic grippers: The key to new markets?,” Ind. Robot Int. J. 43(3), 258–263 (2016).CrossRefGoogle Scholar
Ilievski, F., Mazzeo, A. D., Shepherd, Xin. Chen and G. M., R. F. Whitesides, , “Soft robotics for chemists,” Angew. Chem. 123(8), 1930–1935 (2011).CrossRefGoogle Scholar
Shintake, J., Rosset, S., Schubert, B., Floreano, D. and Shea, H., “Versatile soft grippers with intrinsic electroadhesion based on multifunctional polymer actuators,” Adv. Mater. 28(2), 231–238 (2016).CrossRefGoogle ScholarPubMed
Wang, W. and Ahn, S. H., “Shape memory alloy-based soft gripper with variable stiffness for compliant and effective grasping,” Soft Robot. 4(4), 379–389 (2017).CrossRefGoogle ScholarPubMed
Suder, J., Bobovský, Z., Mlotek, J., M. Vocetka, P. Oščádal and Z. Zeman, “Structural optimization method of a FinRay finger for the best wrapping of object,” Appl. Sci. 11(9), 3858 (2021).CrossRefGoogle Scholar
Kim, H. I., Han, M. W., , S. H. Song,  and S. H. Ahn, “Soft Morphing Hand Driven by SMA Tendon Wire,” In: Composites Part B: Engineering, vol. 105 (2016) pp. 138–148.Google Scholar
Amend, J. R., Brown, E., Rodenberg, N., H. M. Jaeger and H. Lipson, “A positive pressure universal gripper based on the jamming of granular material,” IEEE Trans. Robot. 28(2), 341–350 (2012).CrossRefGoogle Scholar
Guo, J., Elgeneidy, K., Xiang, C., N. Lohse, L. Justham and J. Rossiter, “Soft pneumatic grippers embedded with stretchable electroadhesion,” Smart Mater. Struct. 27(5), 055006 (2018).CrossRefGoogle Scholar
Guo, J., Xiang, C. and Rossiter, J., “A soft and shape-adaptive electroadhesive composite gripper with proprioceptive and exteroceptive capabilities,” Mater. Des. 156, 586–587 (2018).CrossRefGoogle Scholar
Manti, M., Hassan, T., Passetti, G., N. D’Elia, C. Laschi and M. Cianchetti, “A bioinspired soft robotic gripper for adaptable and effective grasping,” Soft Robot. 2(3), 107–116 (2015).CrossRefGoogle Scholar
Suresh, S. A., Christensen, D. L., Hawkes, E. W. and M. Cutkosky, “Surface and shape deposition manufacturing for the fabrication of a curved surface gripper,” J. Mech. Robot. 7(2), 021005 (2015).CrossRefGoogle Scholar
Pettersson, A., Davis, S., Gray, J. O., Dodd, T. J. and 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).CrossRefGoogle Scholar
Xu, W., Zhang, H., Yuan, H.and B. Liang, “A compliant adaptive gripper and its intrinsic force sensing method,” IEEE Trans. Robot. 37(5), 1584–1603 (2021)..CrossRefGoogle Scholar
Pfaff, O., Simeonov, S., Cirovic, I. and P. Stano, “Application of fin ray effect approach for production process automation,” Ann. DAAAM & Proc. 22(1), 1247–1249 (2011).CrossRefGoogle Scholar
FESTO, “Bionic Tripod 2.0.” [Online]. Available at: https://www.festo.com.cn/net/en-cn_cn/SupportPortal/Downloads/146923. Accessed on: 2022.Google Scholar
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. D. Gan, D. Prattichizzo and  F. Renda, “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 G. Neumann, “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
Elgeneidy, K., F, A. ansa, , Hussain, I. and K. Goher, “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
Lee, L. Y., Nurzaman, S. G. and C. P., Tan, “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
Basson, C. I. and G. Bright, “Geometric Conformity Study of a Fin Ray Gripper Utilizing Active Haptic Control,” In: 2019 IEEE 15th 2019 IEEE 15th International Conference on Control and Automation (ICCA) (2019) pp. 713–718.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
Crooks, W., Vukasin, G., O’Sullivan, M., W. Messner and C. Rogers, “Fin Ray effect inspired soft robotic gripper: from the robosoft grand challenge toward optimization,” Front. Robot. AI 3, 70 (2016).CrossRefGoogle Scholar
Chen, R., Song, R., Zhang, Z., L. Bai, F. Liu, P. Jiang and J. Guo, “Bio-inspired shape-adaptive soft robotic grippers augmented with electroadhesion functionality,” Soft Robot. 6(6), 701–712 (2019).CrossRefGoogle ScholarPubMed
Hannard, F., Mirkhalaf, M., Ameri, A. and F. Barthelat, “Segmentations in fins enable large morphing amplitudes combined with high flexural stiffness for fish-inspired robotic materials,” Sci. Robot. 6(57), eabf9710 (2021).CrossRefGoogle ScholarPubMed
Wen, L., Ren, Z., Di Santo, V., Hu, K., Yuan, T., Wang, T. and Lauder, G. V., “Understanding fish linear acceleration using an undulatory biorobotic model with soft fluidic elastomer actuated morphing median fins,” Soft Robot. 5(4), 375–388 (2018).CrossRefGoogle ScholarPubMed
Ali, M. H., Zhanabayev, A., Khamzhin, S. and K. Mussin, “Biologically Inspired Gripper based on the Fin Ray Effect,” In: 5th International Conference on Control, Automation and Robotics (ICCAR) (2019) pp. 865–869.Google Scholar
Basson, C. I., Bright, G. and Walker, A. J., “Validating Object Conformity Through Geometric Considerations of Gripper Mechanisms,” In: 2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) (2017) pp. 865–869.Google Scholar
Hussain, I., Anwar, M., Iqbal, Z., R. Muthusamy, M. Malvezzi, L. Seneviratne and D. Prattichizzo, “Design and Prototype of Supernumerary Robotic Finger (SRF) Inspired by Fin Ray Effect for Patients Suffering from Sensorimotor Hand Impairment,” In: 2019 2nd IEEE International Conference on Soft Robotics (RoboSoft) (2019) pp. 398–403.Google Scholar
Deng, Z. and Li, M., “Learning optimal fin-Ray finger design for soft grasping,” Front. Robot. AI 161 (2021).Google Scholar
Shin, J. H., Park, J. G., D. , I. Kim, and H. S. Yoon, “A universal soft gripper with the optimized fin ray finger,” Int. J. Pr. Eng. Man-GT. 8(3), 889–899 (2021).Google Scholar
Barrie, D. D., Pandya, M., Pandya, H., M. Hanheide and K. Elgeneidy, “A deep learning method for vision based force prediction of a soft fin ray gripper using simulation data,” Front. Robot. AI 8, 104 (2021).CrossRefGoogle ScholarPubMed
Alben, S., Madden, P. G. and Lauder, G. V., “The mechanics of active fin-shape control in ray-finned fishes,” J. Roy. Soc. Interface 4(13), 243–256 (2007).CrossRefGoogle ScholarPubMed