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Modelling and implementation of soft bio-mimetic turtle using echo state network and soft pneumatic actuators

Abstract:
AbstractAdvances of soft robotics enabled better mimicking of biological creatures and closer realization of animals’ motion in the robotics field. The biological creature’s movement has morphology and flexibility that is problematic deportation to a bio-inspired robot. This paper aims to study the ability to mimic turtle motion using a soft pneumatic actuator (SPA) as a turtle flipper limb. SPA’s behavior is simulated using finite element analysis to design turtle flipper at 22 different geometrical configurations, and the simulations are conducted on a large pressure range (0.11–0.4 Mpa). The simulation results are validated using vision feedback with respect to varying the air pillow orientation angle. Consequently, four SPAs with different inclination angles are selected to build a bio-mimetic turtle, which is tested at two different driving configurations. The nonlinear dynamics of soft actuators, which is challenging to model the motion using traditional modeling techniques affect the turtle’s motion. Conclusively, according to kinematics behavior, the turtle motion path is modeled using the Echo State Network (ESN) method, one of the reservoir computing techniques. The ESN models the turtle path with respect to the actuators’ rotation motion angle with maximum root-mean-square error of $$1.04 \times 10^{-11}$$ 1.04 × 10 - 11 . The turtle is designed to enhance the robot interaction with living creatures by mimicking their limbs’ flexibility and the way of their motion.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1038/s41598-021-91136-z

Authors

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Role:
Author
ORCID:
0000-0003-2486-5933
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Institution:
University of Oxford
Role:
Author
ORCID:
0000-0001-5071-2282
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Role:
Author
ORCID:
0000-0002-1184-6701
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Role:
Author
ORCID:
0000-0001-6433-1384
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Role:
Author
ORCID:
0000-0002-6119-8482


Publisher:
Nature Research
Journal:
Scientific Reports More from this journal
Volume:
11
Issue:
1
Pages:
12076-12076
Publication date:
2021-06-08
DOI:
EISSN:
2045-2322
ISSN:
2045-2322


Language:
English
Keywords:
Pubs id:
2349346
Local pid:
pubs:2349346
Source identifiers:
W3170959987
Deposit date:
2025-12-12
ARK identifier:
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