Fluidic innervation sensorizes structures from a single build material

Author:

Truby Ryan L.12ORCID,Chin Lillian1ORCID,Zhang Annan1ORCID,Rus Daniela1ORCID

Affiliation:

1. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

2. Departments of Materials Science and Engineering and Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA.

Abstract

Multifunctional materials with distributed sensing and programmed mechanical properties are required for myriad emerging technologies. However, current fabrication techniques constrain these materials’ design and sensing capabilities. We address these needs with a method for sensorizing architected materials through fluidic innervation, where distributed networks of empty, air-filled channels are directly embedded within an architected material’s sparse geometry. By measuring pressure changes within these channels, we receive feedback regarding material deformation. Thus, this technique allows for three-dimensional printing of sensorized structures from a single material. With this strategy, we fabricate sensorized soft robotic actuators on the basis of handed shearing auxetics and accurately predict their kinematics from the sensors’ proprioceptive feedback using supervised learning. Our strategy for facilitating structural, sensing, and actuation capabilities through control of form alone simplifies sensorized material design for applications spanning wearables, smart structures, and robotics.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Cited by 21 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A retrofit sensing strategy for soft fluidic robots;Nature Communications;2024-01-15

2. General assembly rules for metamaterials with scalable twist effects;International Journal of Mechanical Sciences;2023-12

3. Vision-controlled jetting for composite systems and robots;Nature;2023-11-15

4. LattiSense: A 3D-Printable Resistive Deformation Sensor with Lattice Structures;Proceedings of the 8th ACM Symposium on Computational Fabrication;2023-10-08

5. Machine Learning Best Practices for Soft Robot Proprioception;2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2023-10-01

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