Monolithically Printed Pneumatic Proprioceptive Actuator with Integrated Optical Waveguide Using a Single Material
In muscle tissues, muscle fibers generate active force, muscle spindles provide passive proprioceptive feedback, and connective tissues tightly bind their movements together, forming a reliable biological system. Inspired by this, we propose a single material fabrication method to monolithically print pneumatic proprioceptive actuators (MPPPAs). By leveraging the multifunctional properties of thermoplastic polyurethane (TPU), including inherent flexibility, fusibility, and translucency, and employing a desktop-level fused deposition modeling printer, airtight chambers and embedded optical waveguides are realized within a continuous printing process. Optimized printing parameters lead to fully densified chambers, resulting in a leakage rate of only 0.85% under 200 kPa and maintaining 462.55 kPa after 10 minutes from an initial 500 kPa. The optical waveguides exhibit robust proprioception, maintaining a stable signal over 5000 bending cycles with less than 0.5% drift. Mechanical tests confirm synchronized deformation and continuous structural integration across the monolithically co-printed actuator-sensor region, enabling MPPPAs to achieve reliable actuation-sensing performance with sensing errors below 1.82%. Demonstrations include precise surface contour measurement with the root mean square error of 0.16 mm and real-time gripping width estimation, validating the method's effectiveness in fabricating compact and stable proprioceptive actuators. This research advances actuation-sensing integration in soft robotics, enabling streamlined fabrication and improved reliability for future intelligent systems.
Authors
- Zhonggui Fang (ORCID: https://orcid.org/0000-0003-4988-4233)
- Zheng Wang (ORCID: https://orcid.org/0000-0002-7726-0770)
- Yige Wu
- Juan Yi
- Shaowu Tang (ORCID: https://orcid.org/0009-0007-7563-6862)
- Xiaohuang Liu (ORCID: https://orcid.org/0009-0002-8815-695X)
- Sicong Liu
- Jian S. Dai
Institutions
- University of Southern California (US)
- Southern University of Science and Technology (CN)
- Shenzhen Academy of Robotics (CN)
- Shenzhen Technology University (CN)
- Great Bay University
Publication Details
- Journal
- Soft Robotics
- Published
- 2026-02-04
- DOI
- https://doi.org/10.1177/21695172261420664
- Primary Topic
- Soft Robotics and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00