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A novel morphing soft robot design to minimize deviations

Published online by Cambridge University Press:  10 October 2024

Varell Ferrandy ,
Arnau Garriga-Casanovas ,
Enrico Franco ,
Indrawanto ,
Ferdinando Rodriguez y Baena ,
Andi Isra Mahyuddin  and
Vani Virdyawan Open the ORCID record for Vani Virdyawan [Opens in a new window]
Varell Ferrandy
Affiliation:
The Hamlyn Centre, Mechanical Engineering, Imperial College London, London SW7 2AZ, UK
Arnau Garriga-Casanovas
Affiliation:
The Hamlyn Centre, Mechanical Engineering, Imperial College London, London SW7 2AZ, UK
Enrico Franco
Affiliation:
The Hamlyn Centre, Mechanical Engineering, Imperial College London, London SW7 2AZ, UK
Indrawanto
Affiliation:
Engineering Design and Production Research Group, Faculty of Mechanical and Aerospace Engineering - Institut Teknologi Bandung, Bandung, Indonesia
Ferdinando Rodriguez y Baena
Affiliation:
The Hamlyn Centre, Mechanical Engineering, Imperial College London, London SW7 2AZ, UK
Andi Isra Mahyuddin
Affiliation:
Dynamics and Control Research Group, Faculty of Mechanical and Aerospace Engineering - Institut Teknologi Bandung, Bandung, Indonesia
Vani Virdyawan*
Affiliation:
Engineering Design and Production Research Group, Faculty of Mechanical and Aerospace Engineering - Institut Teknologi Bandung, Bandung, Indonesia
*
Corresponding author: Vani Virdyawan; Email: [email protected]
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Abstract

Soft robotics is rapidly advancing, particularly in medical device applications. A particular miniaturized manipulator design that offers high dexterity, multiple degrees-of-freedom, and better lateral force rendering than competing designs, has great potential for minimally invasive surgery. However, it faces challenges such as the tendency to suddenly and unpredictably deviate in bending plane orientation at higher pressures. In this work, we identified the cause of this deviation as the buckling of the partition wall and proposed design alternatives along with their manufacturing process to address the problem without compromising the original design features. In both simulation and experiment, the novel design managed to achieve a better bending performance in terms of stiffness and reduced deviation of the bending plane. We also developed an artificial neural network-based inverse kinematics model to further improve the performance of the prototype during vectorization. This approach yielded mean absolute errors in orientation of the bending plane below $5^{\circ }$.

Keywords

soft pneumatic actuatordesign optimizationfinite element analysismanufacturingartificial neural network
Type
Research Article
Information
Robotica , Volume 42 , Issue 11 , November 2024 , pp. 3803 - 3818
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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References

Neppalli, S., Jones, B., McMahan, W., Chitrakaran, V., Walker, I., Pritts, M., Csencsits, M., Rahn, C. and Grissom, M. O., “A soft robotic manipulator,” IEEE/RSJ International Conference On Intelligent Robots And Systems, San Diego, USA (2007).CrossRefGoogle Scholar
Wen, T., Hu, J., Zhang, J., Li, X., Kang, S. and Zhang, N. D., “Performance analysis, and experiments of a soft robot for rescue,” J. Mech. Robot. 16(7), 071011 (2024).CrossRefGoogle Scholar
Zhang, X., Pan, T., Heung, H., Chiu, P. and Li, Z.. “A Biomimetic Soft Robot for Inspecting Pipeline with Significant Diameter Variation,” In: IEEE/RSJ International Conference On Intelligent Robots And Systems (IROS), Madrid, Spain (2018).Google Scholar
Liu, Z., Wang, Y., Wang, J., Fei, Y. and Du, Q., “An obstacle-avoiding and stiffness-tunable modular bionic soft robot,” Robotica 40(8), 2651–2665 (2022).CrossRefGoogle Scholar
Zhu, N., Zang, H., Liao, B., Qi, H., Yang, Z., Chen, M., Lang, X. and Wang, Y., “A quadruped soft robot for climbing parallel rods,” Robotica 39(4), 686–698 (2020). doi: 10.1017/s0263574720000661.CrossRefGoogle Scholar
Li, Y., Wang, Y., Yuan, S. and Fei, Y., “Design, modeling, and control of a novel soft-rigid knee joint robot for assisting motion,” Robotica 42(3), 1–16 (2024).CrossRefGoogle Scholar
Cianchetti, M., Ranzani, T., Gerboni, G., Falco, I., Laschi, C. and Menciassi, A., “STIFF-FLOP surgical manipulator: Mechanical design and experimental characterization of the single module,” In: IEEE/RSJ International Conference On Intelligent Robots And Systems, Tokyo, Japan (2013).Google Scholar
Ahmed, J. F., Franco, E., Rodriguez, F. Y. B., Darzi, A.and Patel, N., “A review of bioinspired locomotion in lower GI endoscopy,” Robotica 1–11 (2024). doi: 10.1017/s0263574724000055.CrossRefGoogle Scholar
Chen, G., Pham, M., Maalej, T., Fourati, H., Moreau, R. and Sesmat, S., A Biomimetic Steering Robot for Minimally Invasive Surgery Application (In-Tech, Ernest Hall, 2009).Google Scholar
Suzumori, K., Iikura, S. and Tanaka, H., “Development of flexible microactuator and its applications to robotic mechanisms,” In: Proceedings IEEE International Conference On Robotics And Automation, Sacramento, CA, USA (1991).Google Scholar
Treratanakulchai, S., Franco, E., Garriga-Casanovas, A., Minghao, H., Kassanos, P. and Baena, F., “Development of a 6 DOF Soft Robotic Manipulator with Integrated Sensing Skin,” In: IEEE/RSJ International Conference On Intelligent Robots And Systems (IROS), Kyoto, Japan (2022).Google Scholar
Garriga-Casanovas, A., Collison, I. and Baena, F., “Toward a common framework for the design of soft robotic manipulators with fluidic actuation,” Soft Robot. 5(5), 622–649 (2018).CrossRefGoogle Scholar
Garriga-Casanovas, A., Treratanakulchai, S., Franco, E., Zari, E., Ferrandy, V., Virdyawan, V. & Baena, F., “Optimised Design and Performance Comparison of Soft Robotic Manipulators,” In: 7th International Conference On Mechanical Engineering And Robotics Research (ICMERR), Krakow, Poland (2022).Google Scholar
Virdyawan, V., Ayatullah, T., Sugiharto, A., Franco, E., Garriga-Casanovas, A., Mahyuddin, A., Baena, F. and Indrawanto, , “Design and Manufacturing of an Affordable Soft-Robotic Manipulator for Minimally Invasive Diagnosis,” In: 7th International Conference On Robotics And Automation Engineering (ICRAE), Singapore (2022).CrossRefGoogle Scholar
Franco, E., Casanovas, A., Baena, F. and Astolfi, A., “Model based adaptive control for a soft robotic manipulator,” In: IEEE 58th Conference On Decision And Control (CDC), Nice, France (2019) pp. 1019–1024.Google Scholar
Franco, E., Casanovas, A. and Donaire, A., “Energy shaping control with integral action for soft continuum manipulators,” Mech. Mach. Theory 158, 104250 (2021).CrossRefGoogle Scholar
Franco, E., Garriga-Casanovas, A., Tang, J., Baena, F. and Astolfi, A., “Adaptive energy shaping control of a class of nonlinear soft continuum manipulators,” IEEE/ASME Trans. Mechatron. 27(1), 280–291 (2022).CrossRefGoogle Scholar
Franco, E., Ayatullah, T., Sugiharto, A., Garriga-Casanovas, A. and Virdyawan, V., “Nonlinear energy-based control of soft continuum pneumatic manipulators,” Nonlinear Dynam. 106(1), 229–253 (2021).CrossRefGoogle Scholar
Franco, E., Casanovas, A., Tang, J., Baena, F. and Astolfi, A., “Position regulation in cartesian space of a class of inextensible soft continuum manipulators with pneumatic actuation,” Mechatronics 76, 102573 (2021).CrossRefGoogle Scholar
Thuruthel, T., Falotico, E., Renda, F. and Laschi, C., “Model-based reinforcement learning for closed-loop dynamic control of soft robotic manipulators,” IEEE Trans. Robot. 35(1), 124–134 (2018).CrossRefGoogle Scholar
George Thuruthel, T., Ansari, Y., Falotico, E. and Laschi, C., “Control strategies for soft robotic manipulators: A survey,” Soft Robot. 5(2), 149–163 (2018).CrossRefGoogle ScholarPubMed
Runciman, M., Darzi, A. and Mylonas, G., “ Soft robotics in minimally invasive surgery,” Soft Robot. 6(4), 423–443 (2019).CrossRefGoogle ScholarPubMed
Ferrandy, V., Indrawanto, F., Sugiharto, F., Franco, A., Garriga-Casanovas, E., Mahyuddin, A., Baena, A., Mihradi, F., S and Virdyawan, V., “Modeling of a two-degree-of-freedom fiber-reinforced soft pneumatic actuator,” Robotica 41(12),1–19 (2023).CrossRefGoogle Scholar
Zhang, B., Fan, Y., Yang, P., Cao, T. and Liao, H., “Worm-like soft robot for complicated tubular environments,” Soft Robot. 6(3), 399–413 (2019).CrossRefGoogle ScholarPubMed
Decroly, G., Mertens, B., Lambert, P. and Delchambre, A., “Design, characterization and optimization of a soft fluidic actuator for minimally invasive surgery,” Inter. J. Comp. Assis. Radio. Surg. 15(2), 333–340 (2020).CrossRefGoogle ScholarPubMed
Suzumori, K., “Elastic materials producing compliant robots,” Robot. Auton. Sys. 18(1-2),135–140 (1996).CrossRefGoogle Scholar
Franco, E. and Garriga-Casanovas, A., “Energy-shaping control of soft continuum manipulators with in-plane disturbances,” Inter. J. Robot. Res. 40(1), 236–255 (2021).CrossRefGoogle Scholar
Forte, A., Hanakata, P., Jin, L., Zari, E., Zareei, A., Fernandes, M., Sumner, L., Alvarez, J. and Bertoldi, K., “Inverse design of inflatable soft membranes through machine learning,” Adv. Funct. Mater. 32(16), 2111610 (2022).CrossRefGoogle Scholar
Kim, D., Kim, S., Kim, T., Kang, B., Lee, M., Park, W., Ku, S., Kim, D., Kwon, J., Lee, H., Bae, J., Park, Y., Cho, K. and Jo, S., “Review of machine learning methods in soft robotics,” PLOS One 16(2), e0246102 (2021).CrossRefGoogle ScholarPubMed