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May 29, 2026ACS Applied Electronic Materials0 citations

Self-Sensing Electromagnetically Driven Artificial Muscles with Submillimeter Precision

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JCJunji ChenTTTian TangLDLu Dai

Key Points

  • This research aims to develop an advanced artificial muscle with integrated sensing capabilities to improve robotic adaptability.
  • Developed an electromagnetically driven artificial muscle with a built-in liquid-metal dual-coil system.
  • Tested performance on contraction ratio (42%) and response time (100 ms) over 1000 loading cycles.
  • Implemented muscle in a multi-DOF dexterous hand for task execution.
  • Achieved 0.5 mm sensing precision without performance decay after 1000 cycles.
  • Successfully identified different object shapes and heights using the muscle-driven finger module.
  • Demonstrated superior integration and functionality compared to traditional artificial muscles.

Abstract

The insufficient proprioceptive ability of bionic artificial muscles will greatly restrict the robot's ability to adaptively and precisely control in different scenarios. Inspired by the “sliding filament theory” of biological muscles, we developed an electromagnetically driven artificial muscle that integrates actuation and sensing based on a built-in liquid‒metal dual-coil system, and achieved a structural fusion of actuation and sensing functions. The artificial muscle exhibited a contraction ratio of 42%, a response time of 100 ms, and proprioceptive deformation sensing precision at the submillimeter level (0.5 mm), with no visible decay in the sensing performance after 1000 loading cycles. Through the process of touching and sliding, the finger module driven by artificial muscle could identify the different shapes and heights of objects. Furthermore, these artificial muscles were successfully used in a multi-DOF dexterous hand and could perform tasks such as gesture simulation. This research overcomes traditional limitations and features a high level of integration, offering a solution for the miniaturization and enhanced autonomy of soft robots.

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Cite This Study

Chen et al. (2026) studied this question.

synapsesocial.com/papers/6a192c8bfab5b468c441561dhttps://doi.org/10.1021/acsaelm.6c00580
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