MedElevator Войти в исследования
← Поиск публикаций

DOI 10.1089/soro.2021.0218

Медицинская библиотека · Soft robotics · 2023

Tuning Stiffness with Granular Chain Structures for Versatile Soft Robots.

An Si-Qi, Li Wen-Hao, Li Ji-Hui, Zou Hai-Lin, Deng Zi-Chen

СтатьяПеревод в очереди

Аннотация

Stiffness variation can greatly enhance soft robots' load capacity and compliance. Jamming methods are widely used where stiffness variation is realized by jamming of particles, layers, or fibers. It is still challenging to make the variable stiffness components lightweight and adaptive. Besides, the existing jamming mechanisms generally encounter deformation-induced softening, restricting their applications in cases where large deformation and high stiffness are both needed. Herein, a multifunctional granular chain assemblage is proposed, where particles are formed into chains with threads. The chain jamming can be classified into two types. Granular chain jamming (GCJ) utilizes typical particles such as spherical particles, which can achieve both high stiffness and great adaptability while keeping jamming components relatively lightweight, while by using cubic particles, a peculiar deformation-induced stiffening mechanism is found, which is termed as stretch-enhanced particle jamming (SPJ). The versatility of GCJ and SPJ mechanisms in soft robots is demonstrated through soft grippers, soft crawlers, or soft bending actuators, where great passive adaptability, high load capacity, joint-like bending, friction enhancement, or postponing buckling can be realized, respectively. This work thus offers a facile and low-cost strategy to fabricate versatile soft robots.

Для цитирования: An Si-Qi, Li Wen-Hao, Li Ji-Hui, Zou Hai-Lin, Deng Zi-Chen. Tuning Stiffness with Granular Chain Structures for Versatile Soft Robots.. Soft robotics. 2023. DOI: 10.1089/soro.2021.0218.

Предметные рубрики MeSH

Подготовка и перевод публикации
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…

История · было → стало

Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.

Тип публикации указан по данным источника. Уровень доказательности автоматически не присваивается.