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Design and verification of a parallel elastic robotic leg
Emre Tanfener1,2, Osman Kaan Karagöz1,3, Sinan Şahin Candan4,5
1Robotics and Artificial Intelligence Technologies Application and Research Center, Middle East Technical University, 06800 Ankara, Turkey.
Bioinspiration & Biomimetics
|January 29, 2024
Summary
This study designed a parallel elastic robotic leg to mimic a linear mass-spring-damper model using a novel wrapping cam mechanism. Experimental results confirm the mechanism successfully replicates the desired linear dynamics for advanced robotic control.
Area of Science:
- Robotics
- Mechanical Engineering
- Control Systems
Background:
- Robotic legs often face challenges in accurately replicating desired dynamic models due to non-linearities in elastic elements.
- Achieving linear dynamics is crucial for simplifying model-based control design and analysis in legged robots.
Purpose of the Study:
- To design and experimentally verify a parallel elastic robotic leg mechanism capable of emulating a linear mass-spring-damper model.
- To linearize the non-linear forces from elastic elements using a wrapping cam mechanism.
- To enable the application of analytical solutions from linear models in robotic control.
Main Methods:
- Design of a parallel elastic robotic leg incorporating a wrapping cam mechanism.
- Development of a system identification procedure to estimate leg mechanism parameters.
- Cross-validation of estimated parameters to assess performance against the target linear model.
Main Results:
- The wrapping cam mechanism effectively linearizes the non-linear elastic forces.
- Experimental verification confirms the passive dynamics of the robotic leg closely match the linear mass-spring-damper model.
- Successful parameter estimation and cross-validation demonstrate the mechanism's fidelity.
Conclusions:
- The developed parallel elastic robotic leg successfully achieves the intended linear dynamics.
- This mechanism provides a viable platform for parallel elastic actuation in robots.
- The findings facilitate the use of model-based controllers leveraging analytical solutions for linear systems.

