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Outline of an evolutionary morphology generator towards the modular design of a biohybrid catheter

Tsompanas, Michail Antisthenis; Balaz, Igor

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Authors

Igor Balaz



Abstract

Biohybrid machines (BHMs) are an amalgam of actuators composed of living cells with synthetic materials. They are engineered in order to improve autonomy, adaptability and energy efficiency beyond what conventional robots can offer. However, designing these machines is no trivial task for humans, provided the field’s short history and, thus, the limited experience and expertise on designing and controlling similar entities, such as soft robots. To unveil the advantages of BHMs, we propose to overcome the hindrances of their design process by developing a modular modeling and simulation framework for the digital design of BHMs that incorporates Artificial Intelligence powered algorithms. Here, we present the initial workings of the first module in an exemplar framework, namely, an evolutionary morphology generator. As proof-of-principle for this project, we use the scenario of developing a biohybrid catheter as a medical device capable of arriving to hard-to-reach regions of the human body to release drugs. We study the automatically generated morphology of actuators that will enable the functionality of that catheter. The primary results presented here enforced the update of the methodology used, in order to better depict the problem under study, while also provided insights for the future versions of the software module.

Journal Article Type Article
Acceptance Date Apr 2, 2024
Online Publication Date Apr 12, 2024
Publication Date Apr 12, 2024
Deposit Date Apr 16, 2024
Publicly Available Date Apr 16, 2024
Journal Frontiers in Robotics and AI
Electronic ISSN 2296-9144
Publisher Frontiers Media
Peer Reviewed Peer Reviewed
Volume 11
Article Number 1337722
DOI https://doi.org/10.3389/frobt.2024.1337722
Keywords biohybrid machines, evolutionary algorithms, optimization, machine learning, 3D voxel-based simulator
Public URL https://uwe-repository.worktribe.com/output/11897780

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