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An obstacle-avoiding and stiffness-tunable modular bionic soft robot

Published online by Cambridge University Press:  06 January 2022

Zhaoyu Liu ,
Yuxuan Wang Open the ORCID record for Yuxuan Wang [Opens in a new window] ,
Jiangbei Wang ,
Yanqiong Fei Open the ORCID record for Yanqiong Fei [Opens in a new window]  and
Qitong Du
Zhaoyu Liu
Affiliation:
Research Institute of Robotics, Shanghai Jiaotong University, Shanghai 200240, China
Yuxuan Wang
Affiliation:
Research Institute of Robotics, Shanghai Jiaotong University, Shanghai 200240, China
Jiangbei Wang
Affiliation:
Research Institute of Robotics, Shanghai Jiaotong University, Shanghai 200240, China
Yanqiong Fei*
Affiliation:
Research Institute of Robotics, Shanghai Jiaotong University, Shanghai 200240, China
Qitong Du
Affiliation:
Research Institute of Robotics, Shanghai Jiaotong University, Shanghai 200240, China
*
*Corresponding author. E-mail: fyq_sjtu@163.com
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Abstract

The aim of this work is to design and model a novel modular bionic soft robot for crawling and crossing obstacles. The modular bionic soft robot is composed of several serial driving soft modules, each module is composed of two parallel soft actuators. By analyzing the influence of working pressure and manufacturing size on the stiffness of the modular bionic soft robot, the nonlinear variable stiffness model of the modular bionic soft robot is established. Based on this model, the spatial states and design parameters of the modular bionic soft robot are discussed when the modular bionic soft robot can pass through the obstacle. Experiments show that when the inflation air pressure of the modular bionic soft robot is 70 kPa, its speed can reach 7.89 mm/s and the height of obstacles passed by it can reach 42.8 mm. The feasibility of the proposed modular bionic soft robot and nonlinear variable stiffness model is verified by locomotion experiments.

Keywords

soft robotmodular robotnonlinear variable stiffnessobstacle-avoiding ability
Type
Research Article
Information
Robotica , Volume 40 , Issue 8 , August 2022 , pp. 2651 - 2665
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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References

Crespi, A. and Ijspeert, A. J., “AmphiBot II: An amphibious snake robot that crawls and swims using a central pattern generator,” Color Res. Appl. 27(2), 130–135 (2006).Google Scholar
Lin, H.T., Leisk, G. G., and Trimmer, B., “GoQBot: A caterpillar-inspired softbodied rolling robot,” Bioinspir. Biomim. 6(2), 026007 (2011).CrossRefGoogle Scholar
Shepherd, R. F., Stokes, A. A., Freake, J., et al., “Using explosions to power a soft robot,” Angew. Chem. Int. Ed. 52(10), 2892–2896 (2013).CrossRefGoogle Scholar
Mazzolai, B., Margheri, L., Cianchetti, M., et al., “Soft-robotic arm inspired by the octopus: II. From artificial requirements to innovative technological solutions,” Bioinspir. Biomim. 7(2), 025005 (2012).CrossRefGoogle ScholarPubMed
Zheng, T., et al., “Model validation of an octopus inspired continuum robotic arm for use in underwater environments,” J. Mech. Robot. 5(2), 021004(2013).CrossRefGoogle Scholar
Seok, S., et al., “Peristaltic Locomotion with Antagonistic Actuators in Soft Robotics,” In: IEEE International Conference on Robotics & Automation (IEEE, 2010).Google Scholar
Fei, Y., and Pang, W., “Analysis on nonlinear turning motion of multi-spherical soft robots,” Nonlinear Dyn. 88(2), 883–892 (2017).CrossRefGoogle Scholar
Pei, Q., et al., “Multiple-degrees-of-freedom electroelastomer roll actuators,” Smart Mater. Struct. 13(5), N86 (2004).CrossRefGoogle Scholar
Katzschmann, R. K., Marchese, A. D., and Rus, D., “Hydraulic Autonomous Soft Robotic Fish for 3D Swimming,” International Symposium on Experimental Robotics, 2016.CrossRefGoogle Scholar
Kim, H. J., Song, S. H., and Ahn, S. H., “A turtle-like swimming robot using a smart soft composite (SSC) structure”, Smart Mater. Struct. 22(1), 014007 (2012).CrossRefGoogle Scholar
Hubbard, J. J., Fleming, M., Palmre, V., et al., “Monolithic IPMC fins for propulsion and maneuvering in bioinspired underwater robotics,” IEEE J. Ocean. Eng. 39(3), 540–551 (2014).CrossRefGoogle Scholar
Nakamaru, S., Maeda, S., Hara, Y., et al., “Development of Novel Selfoscillating Gel Actuator for Achievement of Chemical Robot,” In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009 (IEEE, 2009) pp. 4319–4324.CrossRefGoogle Scholar
Pang, W., Fei, Y., and He, W., “Study on Motion Process of Modular Soft Robot,” In: International Conference on Machine Learning and Cybernetics (IEEE, 2017) pp. 146–151.Google Scholar
Seok, S., et al., “Meshworm: A peristaltic soft robot with antagonistic nickel titanium coil actuators,” IEEE/ASME Trans. Mechatron. 18(5), 1485–1497 (2013).CrossRefGoogle Scholar
Jenkins, T. E., Chapman, E. M., and Bryant, M., “Bio-inspired online variable recruitment control of fluidic artificial muscles,” Smart Mater. Struct. 25(12), 125016 (2016).CrossRefGoogle Scholar
Shepherd, R. F., Ilievski, F., Choi, W., et al., “Multigait soft robot,” Proc. Nat. Acad. Sci. 108(51), 20400–20403 (2011).CrossRefGoogle ScholarPubMed
Wang, W., et al., “Locomotion of inchworm-inspired robot made of smart soft composite (SSC),” Bioinspir. Biomim. 9(4), 046006 (2014).CrossRefGoogle Scholar
Naigong, Y. U., et al., “Optical flow based mobile robot obstacle avoidance method in unstructured environment,” J. Beijing Univ. Technol. (2017).Google Scholar
Nasrinahar, A., and Chuah, J. H., “Intelligent motion planning of a mobile robot with dynamic obstacle avoidance,” J. Veh. Rout. Algor. 1, 1–16 (2018).Google Scholar
Zong, L., et al., “Obstacle avoidance handling and mixed integer predictive control for space robots,” Advances in Space Research 61(8), S027311771830070X (2018).CrossRefGoogle Scholar
Fei, Y. and Wang, X., “Study on nonlinear obstacle avoidance on modular soft robots,” Nonlinear Dyn. 82(1–2), 891–898 (2015).CrossRefGoogle Scholar
Wang, T., et al. “Electrostatic layer jamming variable stiffness for soft robotics,” IEEE/ASME Trans. Mechatron. 24(2), 424–433 (2019).CrossRefGoogle Scholar
Mohammadi Nasab, A., et al., “A soft gripper with rigidity tunable elastomer strips as ligaments,” Soft Robot. 4(4), 411–420 (2017).CrossRefGoogle Scholar
Giannaccini, M. E., et al., “Novel design of a soft lightweight pneumatic continuum robot arm with decoupled variable stiffness and positioning,” Soft Robot. 5(1), 54–70 (2018).CrossRefGoogle ScholarPubMed
Abeach, L. A., et al., “A variable stiffness soft gripper using granular jamming and biologically, inspired pneumatic muscles,” J. Bionic Eng. 2, 236–246 (2018).CrossRefGoogle Scholar
Ranzani, T., et al., “A bioinspired soft manipulator for minimally invasive surgery,” Bioinspir. Biomim. 10(3), 035008 (2015).CrossRefGoogle ScholarPubMed
Wei, Y., et al. “A soft robotic spine with tunable stiffness based on integrated ball joint and particle jamming,” Mechatronics 33, 84–92 (2016).CrossRefGoogle Scholar
Sadati, , Hadi, Sm, et al. “Stiffness control of soft robotic manipulator for minimally invasive surgery (mis) using scale jamming.” International Conference on Intelligent Robotics and Applications Springer, Cham, 2015.CrossRefGoogle Scholar
Yingtian, Li, et al. “Passive Particle Jamming and Its Stiffening of Soft Robotic Grippers.” IEEE Transactions on Robotics 33.2(2017):446–455.CrossRefGoogle Scholar
Wu, P., Jiangbei, W., and Yanqiong, F., “The structure, design, and closed-loop motion control of a differential drive soft robot,” Soft Robot. 5(1), 71 (2018).CrossRefGoogle ScholarPubMed
Jianlong, H., Guangjuan, X., and Zhengwei, L., “Finite element analysis of super-elastic rubber materials based on the Mooney-Rivlin and Yeoh model,” China Rubber/Plastics Technol. Equip. (2008).Google Scholar
Yeoh, O. H., “Some forms of the strain energy function for rubber,” Rubber Chem. Technol. 66(5), 754–771 (2012).CrossRefGoogle Scholar