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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.
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Quasi-static Modeling of Feeding Behavior in Aplysia Californica
Behaviors that are produced solely through geometrically complex three-dimensional interactions of soft-tissue muscular elements, and which do not move rigid articulated skeletal elements, are a challenge to mechanically model. This complexity often leads to simulations requiring substantial computational time. We discuss how using a quasi-static approach can greatly reduce the computational time required to model slow-moving soft-tissue structures, and then demonstrate our technique using the biomechanics of feeding behavior by the marine mollusc, Aplysia californica. We used a conventional 2nd order (from Newton’s equations), forward dynamic model, which required 14 s to simulate 1 s of feeding behavior. We then used a quasi-static reformulation of the same model, which only required 0.35 s to perform the same task (a 40-fold improvement in computation speed). Lastly, we re-coded the quasi-static model in Python to further increase computation speed another 3-fold, creating a model that required just 0.12 s to model 1 s of feeding behavior. Both quasi-static models produce results that are nearly indistinguishable from the original 2nd order model, showing that quasi-static formulations can greatly increase the computation speed without sacrificing model accuracy.
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- Award ID(s):
- 2015317
- PAR ID:
- 10424949
- Date Published:
- Journal Name:
- Biomimetic and Biohybrid Systems
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-031-20470-8_8
