Highly Compressible 3D-Printed Soft Magnetoelastic Sensors for Human–Machine Interfaces
Hyeonseo Song, Yeonwoo Jang, Jin Pyo Lee, Jun Kyu Choe, Minbyeol Yun, Youn‐Kyoung Baek, Jiyun Kim
- Year
- 2023
- Citations
- 18
Abstract
Incorporating perception into robots or objects holds great potential to revolutionize daily human life. To achieve this, critical factors include the design of an integrable three-dimensional (3D) soft sensor with self-powering capability, a wide working range, and tuneable functionalities. Here, we introduce a highly compressible 3D-printed soft magnetoelastic sensor with a wide strain sensing range. Inspired by the lattice metamaterial, which offers a highly porous structure with tuneable mechanical properties, we realized a remarkably compliant 3D self-powering sensor. Using magnetoelastic composite materials and 3D printing combined with sacrificial molding, a broad design space for constituent materials and structures is investigated, allowing for tuneable mechanical properties and sensor performances. These sensors are successfully integrated with two robotic systems as the robot operation and perception units, enabling robot control and recognition of diverse physical interactions with a user. Overall, we believe that this work represents a cornerstone for compliant 3D self-powered soft sensors, giving impetus to the development of advanced human-machine interfaces.
Keywords
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