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Fluidic Multichambered Actuator and Multiaxis Intrinsic Force Sensing.
Dionysios Malas1,2, Guokai Zhang1,2, Shuai Wang2
1Department of Surgical and Interventional Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Soft Robotics
|July 30, 2024
Summary
Soft robots can now sense forces using their internal fluid pressure, enhancing safety and control. This innovation allows for precise interaction with the environment without compromising flexibility.
Area of Science:
- Robotics
- Materials Science
- Mechanical Engineering
Background:
- Soft robots require force sensing for safe and autonomous physical interaction.
- Integrating sensors without compromising inherent flexibility is a key challenge.
- Soft-fluidic actuators (SFAs) offer compliance and address pneumatic compressibility issues.
Purpose of the Study:
- To investigate the use of incompressible actuation fluid in SFAs for both actuation and multiaxial force sensing.
- To develop and validate a pressure-based sensing method for soft robots.
- To create a calibration algorithm for accurate force mapping.
Main Methods:
- Developed a hyperelastic model for actuation pressure baseline.
- Utilized pressure disparities to map external forces using a fluidic soft sensor principle.
- Employed computed tomography (CT) imaging for internal deformation analysis.
- Used COMSOL simulations to analyze induced stresses and develop a calibration algorithm.
- Conducted experiments with 'Point Load' and 'Distributed Force' setups.
Main Results:
- Validated the analytical actuation-pressure model with CT imaging.
- Developed a calibration algorithm accounting for nonlinearities.
- Achieved high accuracy in force sensing, with a maximum absolute error of 0.32N for forces up to 6N.
- Demonstrated sensing capabilities for both concentrated and distributed forces.
Conclusions:
- Incompressible fluid in SFAs can serve as a dual-purpose medium for actuation and multiaxial force sensing.
- The developed pressure-based sensing method is accurate and reliable for soft robots.
- This approach enhances soft robot safety, controllability, and autonomy in physical interactions.
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