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This research proposes a Cosserat-rod-based differentiable framework for the autonomous design of soft, programmable slender structures, addressing complex nonlinearities in biomedical applications. By enabling end-to-end optimization of material layout and actuation mechanisms like swelling or magnetic fields, the method facilitates patient-specific solutions for minimally invasive surgery and wearable devices. The work advances computational engineering by providing a rigorous mathematical tool to handle large deformations and multiphysics interactions in soft robotics.
CROPS: Cosserat-Rod-Based Co-Design of Soft Slender Programmable Material Systems for Biomedical Applications
arXiv:2610.24209v1 Announce Type: cross Abstract: Slender structures made of stimuli-responsive materials represent an emerging class of programmable material systems, with applications in soft robotics, minimally invasive surgery, wearable electronics, and deployable structures. In biomedical applications, the on-demand design of slender structures, encompassing both their material layout and actuation, is essential to harnessing their large deformation and multiphysics actuation to meet patient-specific design requirements. However, owing to geometric and material nonlinearities, their structure-actuation-response relationship is highly nonintuitive and computationally demanding. This often makes trial-and-error and conventional design approaches challenging for addressing patient-specific needs. To this end, we present a Cosserat-rod-based design optimization framework for the autonomous design of programmable slender structures. Leveraging the Cosserat rod representation, the end-to-end differentiable framework is demonstrated on two case studies: (i) design of swellable stents that morph to the shape of carotid vessels; (ii) co-design of the remnant magnetization and the applied field of magnetically-active catheters to reduce the force exerted on the patient-specific vessel wall during insertion. Both self-contact and external contact with surrounding structures are accounted for. We consider two actuation mechanism, namely the swelling of gel-elastomer composites and the magnetic actuation of hard-magnetic composites. The proposed framework presents an autonomous design platform of soft slender material systems that support large deformations and multiphysics actuation toward personalized biomedical design.
Abstract:Slender structures made of stimuli-responsive materials represent an emerging class of programmable material systems, with applications in soft robotics, minimally invasive surgery, wearable electronics, and deployable structures. In biomedical applications, the on-demand design of slender structures, encompassing both their material layout and actuation, is essential to harnessing their large deformation and multiphysics actuation to meet patient-specific design requirements. However, owing to geometric and material nonlinearities, their structure-actuation-response relationship is highly nonintuitive and computationally demanding. This often makes trial-and-error and conventional design approaches challenging for addressing patient-specific needs. To this end, we present a Cosserat-rod-based design optimization framework for the autonomous design of programmable slender structures. Leveraging the Cosserat rod representation, the end-to-end differentiable framework is demonstrated on two case studies: (i) design of swellable stents that morph to the shape of carotid vessels; (ii) co-design of the remnant magnetization and the applied field of magnetically-active catheters to reduce the force exerted on the patient-specific vessel wall during insertion. Both self-contact and external contact with surrounding structures are accounted for. We consider two actuation mechanism, namely the swelling of gel-elastomer composites and the magnetic actuation of hard-magnetic composites. The proposed framework presents an autonomous design platform of soft slender material systems that support large deformations and multiphysics actuation toward personalized biomedical design.
Computational Engineering, Finance, and Science (cs.CE); Robotics (cs.RO)
arXiv:2610.24209 [cs.CE]
(or arXiv:2610.24209v1 [cs.CE] for this version)
https://doi.org/10.48550/arXiv.2610.24209 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 310 words