John F. Zimmerman, Daniel J. Drennan, James Ikeda, Qianru Jin, Herdeline Ann M. Ardoña, Sean L. Kim, Ryoma Ishii, Kevin Kit Parker
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引用次数: 0
Abstract
In biomimetic design, researchers recreate existing biological structures to form functional devices. For biohybrid robotic swimmers assembled with tissue engineering, this is problematic because most devices operate at different length scales than their naturally occurring counterparts, resulting in reduced performance. To overcome these challenges, here, we demonstrate how machine learning–directed optimization (ML-DO) can be used to inform the design of a biohybrid robot, outperforming other nonlinear optimization techniques, such as Bayesian optimization, in the selection of high-performance geometries. We show how this approach can be used to maximize the thrust generated by a tissue-engineered mobuliform miniray. This results in devices that can swim at the millimeter scale while more closely preserving natural locomotive scaling laws. Overall, this work provides a quantitatively rigorous approach for the engineering design of muscular structure-function relationships in an automated fashion.
期刊介绍:
Science Robotics publishes original, peer-reviewed, science- or engineering-based research articles that advance the field of robotics. The journal also features editor-commissioned Reviews. An international team of academic editors holds Science Robotics articles to the same high-quality standard that is the hallmark of the Science family of journals.
Sub-topics include: actuators, advanced materials, artificial Intelligence, autonomous vehicles, bio-inspired design, exoskeletons, fabrication, field robotics, human-robot interaction, humanoids, industrial robotics, kinematics, machine learning, material science, medical technology, motion planning and control, micro- and nano-robotics, multi-robot control, sensors, service robotics, social and ethical issues, soft robotics, and space, planetary and undersea exploration.