Hostname: page-component-848d4c4894-nr4z6 Total loading time: 0 Render date: 2024-06-10T22:50:13.689Z Has data issue: false hasContentIssue false
  • English
  • Français

Experimental verification of soft-robot gaits evolved using a lumped dynamic model

Published online by Cambridge University Press:  28 January 2011

Frank Saunders ,
Ethan Golden ,
Robert D. White  and
Jason Rife
Frank Saunders*
Affiliation:
Department of the Mechanical Engineering, Tufts University, Medford, MA, USA
Ethan Golden
Affiliation:
Department of the Biology, Tufts University, Medford, MA, USA
Robert D. White
Affiliation:
Department of the Mechanical Engineering, Tufts University, Medford, MA, USA
Jason Rife
Affiliation:
Department of the Mechanical Engineering, Tufts University, Medford, MA, USA
*
*Corresponding author. E-mail: frank.saunders@tufts.edu
Get access Rights & Permissions [Opens in a new window]

Summary

When generating gaits for soft robots (those with no explicit joints), it is not evident that undulating control schemes are the most efficient. In considering alternative control schemes, however, the computational costs of evaluating continuum mechanic models of soft robots represent a significant bottleneck. We consider the use of lumped dynamic models for soft robotic systems. Such models have not been employed previously to design gaits for soft robotic systems, though they are widely used to simulate robots with compliant joints. A major question is whether these methods are accurate enough to be representations of soft robots to enable gait design and optimization. This paper addresses the potential “reality gap” between simulation and experiment for the particular case of a soft caterpillar-like robot. Experiments with a prototype soft crawler demonstrate that the lumped dynamic model can capture essential soft-robot mechanics well enough to enable gait optimization. Significantly, experiments verified that a prototype robot achieved high performance for control patterns optimized in simulation and dramatically reduced performance for gait parameters perturbed from their optimized values.

Keywords

Soft robotBiomimetic designGait optimization
Type
Articles
Information
Robotica , Volume 29 , Issue 6 , October 2011 , pp. 823 - 830
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Dobrolyubov, A. I., “The mechanism of locomotion of some terrestrial animals by traveling waves of deformation,” J. Theor. Biol. 119, 457–466 (1986).CrossRefGoogle Scholar
2.Keller, J. B. and Falkovitz, M. S., “Crawling of worms,” J. Theor. Biol. 104, 417–442 (1983).CrossRefGoogle Scholar
3.Tanev, I., “Automated evolutionary design, robustness and adaptation of sidewinding locomotion of a simulated snake-like robot,” IEEE Trans. Robot. 21 (4), 632–645 (2005).CrossRefGoogle Scholar
4.Ijspeert, A. J., “A connectionist central pattern generator for the aquatic and terrestrial gaits of a simulated salamander,” Biol. Cybern. 84, 331–348 (2001).CrossRefGoogle ScholarPubMed
5.Chen, L., Ma, S., Wang, Y., Li, B. and Duan, D., “Design and modeling of a snake robot in traveling wave locomotion,” Mech. Mach. Theory 42, 1632–1642 (2007).CrossRefGoogle Scholar
6.Brackenbury, J., “Fast locomotion in caterpillars,” J. Insect Physiol. 45, 525–533 (1999).CrossRefGoogle ScholarPubMed
7.Trimmer, B. A., Takesian, A. E., Sweet, B. M., Rogers, C. B., Hake, D. C. and Rogers, D. J., “Caterpillar Locomotion: A New Model for Soft-Bodied Climbing and Burrowing Robots,” Proceedings of the 7th International Symposium on Technology and the Mine Problem, Monterey, CA (May 2–5, 2006).Google Scholar
8.Woods, W. A., Fusillo, S. J. and Trimmer, B. A., “Dynamic properties of a locomotory muscle of the tobacco hornworm manduca sexta during strain cycling and simulated natural crawling,” J. Exp. Biol. 211, 873–882 (2008).CrossRefGoogle ScholarPubMed
9.Pfeifer, R., Lungarella, M. and Iida, F., “Self-organization, embodiment, and biologically inspired robotics,” Science 318, 1088–1093 (2007).CrossRefGoogle ScholarPubMed
10.Otake, M., Kagami, Y., Inaba, M. and Inoue, H., “Motion design of a starfish-shaped gel robot made of electro-active polymer gel,” Robotics and Autonomous Systems 40, 185–191 (2002).CrossRefGoogle Scholar
11.Sugiyama, Y. and Hirai, S., “Crawling and jumping by a deformable robot,” Int. J. Robot. Res. 25 (5–6), 603–620 (2006).CrossRefGoogle Scholar
12.Otake, M., Inaba, M. and Inoue, H., “Development of a Gel Robot Made of Electro-Active Polymer PAMPS Gel,” Proceedings of the IEEE SMC Conference, Tokyo, Japan. (Oct. 12–15, 1999), pp. 788–793.Google Scholar
13.Cuttino, J. F., Van Dijck, B. and Brown, A. M., “Design and development of a toroidal flexure for extended motion applications,” Precis. Eng. 29, 135–145 (2005).CrossRefGoogle Scholar
14.Autumn, K., Hsieh, S., Dudek, D., Chen, J., Chitaphan, C. and Full, R., “Dynamics of geckos running vertically,” J. Exp. Biol. 209, 260–272 (2006).CrossRefGoogle ScholarPubMed
15.Full, R. and Koditschek, D., “Templates and anchors: Neuromechanical hypotheses of legged locomotion on land,” J. Exp. Biol. 202, 3325–3332 (1999).CrossRefGoogle ScholarPubMed
16.Kim, S., Clark, J. and Cutkosky, M., “iSprawl: Design and tuning for high-speed autonomous open-loop running,” Int. J. Robot. Res. 25, 903–912 (2006).CrossRefGoogle Scholar
17.Goldman, D., Komsuoglu, H. and Koditschek, D., “March of the sandbots,” IEEE Spectr. 46, 30–35 (2009).CrossRefGoogle Scholar
18.Moon, Y., “Biomimetic design of finger mechanism with contact-aided compliant mechanism,” Mech. Mach. Theory 42 (5), 600–661 (2007).CrossRefGoogle Scholar
19.Dario, P., Stefanini, C. and Scarfogliero, U., “The use of compliant joints and elastic energy storage in bio-inspired legged robots,” Mech. Mach. Theory 44 (3), 580–590 (2009).Google Scholar
20.Yekutieli, Y., Sagiv-Zohar, R., Aharonov, R., Engel, Y., Hochner, B. and Flash, T., “Dynamic model of the octopus arm I: Biomechanics of the octopus reaching movement,” J. Neurophys. 94, 1443–1458 (2005).CrossRefGoogle Scholar
21.Liang, Y., McMeeking, R. M. and Evans, A. G., “A finite element simulation scheme for biological muscular hydrostats,” J. Theor. Biol. 242, 142–150 (2006).CrossRefGoogle ScholarPubMed
22.Walker, I. et al. , “Continuum Robot Arms Inspired by Cephalopods,” In: Unmanned Ground Vehicle Technology VII, Proceedings of SPIE, vol. 5804 (Gerhart, G. R., Shoemaker, C. M. and Gage, D. W., eds.) (SPIE Digital Library, 2005) pp. 303–314.CrossRefGoogle Scholar
23.Saunders, F., Rieffel, J. and Rife, J., “A Method of Accelerating Convergence for Genetic Algorithms Evolving Morphological and Control Parameters for a Biomimetic Robot,” Proceedings of the IEEE International Conference on Autonomous Robots and Agents (ICARA 2009), Wellington, New Zealand (Feb. 10–12, 2009), pp. 155–160.CrossRefGoogle Scholar
24.Maglino, O., Lund, H. H. and Nolfi, S., “Evolving mobile robots in simulated and real environments,” Artif. Life 2, 417–434 (1995).CrossRefGoogle Scholar
25.Rincon, D. and Sotelo, J., “Dynamic and experimental analysis for inchworm-like biomimetic robots,” IEEE Robots Autom. Mag. December, 53–57 (2003).CrossRefGoogle Scholar
26.Rieffel, J., Valero-Cuevas, F. and Lipson, H., “Morphological communication: Exploiting coupled dynamics in a complex mechanical structure to achieve locomotion,” J. R. Soc. Interface 7 (45), 613–621 (2010).CrossRefGoogle Scholar