Soft robots have recently drawn extensive attention
thanks to their unique ability of adapting to complicated
environments. Soft robots are designed in a variety of shapes
of aiming for many different applications. However, accurate
modelling and control of soft robots is still an open problem
due to the complex robot structure and uncertain interaction
with the environment. In fact, there is no unified framework
for the modeling and control of generic soft robots. In this
paper, we present a novel data-driven machine learning method
for modeling a cable-driven soft robot. This machine learning
algorithm, named deterministic learning (DL), uses soft robot
motion data to train a radial basis function neural network
(RBFNN). The soft robot motion dynamics are then guaranteed
to be accurately identified, represented, and stored as an
RBFNN model with converged constant neural network weights.
To validate our method, We have built a simulated soft robot
almost identical to our real inchworm soft robot, and we have
tested the DL algorithm in simulation. Furthermore, a neural
network weight combining technique is used which can extract
and combine useful dynamics information from multiple robot
motion trajectories.
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This content will become publicly available on August 4, 2025
Curve Parametric Modeling of Planar Soft Robots
Soft robots, due to their flexibility, adaptability, and gentle handling over rigid robots, have shown better potential in numerous applications requiring operating in constrained spaces. Most of the soft robotic prototypes are of a linear form that can be modeled as a curve in space and are found in manipulators and limbs of locomoting robots. Planar soft robots have been proposed recently that are modeled as a surface and deform in 3D. Research on planar soft robots has been less extensive due to the challenges associated with modeling surface deformations efficiently. We present a curve-parametric approach for the deformation modeling of planar soft robot modules. Along with the Bezier patch method to approximate the surface at 30 Hz. Experimental evaluations on a prototype were developed and tested to validate that the proposed model can reasonably approximate the planar robot boundaries, and the surface derived from it.
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- PAR ID:
- 10538622
- Publisher / Repository:
- IEEE
- Date Published:
- ISSN:
- 2152-744X
- ISBN:
- 979-8-3503-8807-7
- Page Range / eLocation ID:
- 299 to 304
- Subject(s) / Keyword(s):
- surface modeling, curve parametric, Bezier surface, planar robots, soft robots
- Format(s):
- Medium: X
- Location:
- Tianjin, China
- Sponsoring Org:
- National Science Foundation
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