Soft robots promise improved safety and capability over rigid robots when deployed near humans or in complex, delicate, and dynamic environments. However, infinite degrees of freedom and the potential for highly nonlinear dynamics severely complicate their modeling and control. Analytical and machine learning methodologies have been applied to model soft robots but with constraints: quasi-static motions, quasi-linear deflections, or both. Here, we advance the modeling and control of soft robots into the inertial, nonlinear regime. We controlled motions of a soft, continuum arm with velocities 10 times larger and accelerations 40 times larger than those of previous work and did so for high-deflection shapes with more than 110° of curvature. We leveraged a data-driven learning approach for modeling, based on Koopman operator theory, and we introduce the concept of the static Koopman operator as a pregain term in optimal control. Our approach is rapid, requiring less than 5 min of training; is computationally low cost, requiring as little as 0.5 s to build the model; and is design agnostic, learning and accurately controlling two morphologically different soft robots. This work advances rapid modeling and control for soft robots from the realm of quasi-static to inertial, laying the groundwork for the next generation of compliant and highly dynamic robots.
Localized Motion Dynamics Modeling of A Soft Robot: A Data-Driven Adaptive Learning Approach
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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- Award ID(s):
- 1929729
- NSF-PAR ID:
- 10345160
- Date Published:
- Journal Name:
- Proceedings of the American Control Conference
- ISSN:
- 0743-1619
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.23919/ACC53348.2022.9867191
