In their paper, Escaida Navarro et al. introduce a new pneumatic mechanosensor dedicated to Soft Robotics and propose a generic method to reconstruct the magnitude of a contact-force acting on it. Changes in cavity volumes inside a soft silicon pad are measured by air-flow sensors. The resulting mechanosensor is characterized by its high sensitivity, repeatability, dynamic range and accurate localization capability in 2D. Using a regression found by machine learning techniques, the contact location and force magnitude can be accurately predicted when the force magnitudes are within the range of the training data.
Relying on a Finite Element Method (FEM), authors from the Defrost team formulate an optimization problem, which yields the contact load that best explains the observed changes in cavity volumes. This method makes no assumptions on the force range, the shape of the soft pad or the shape of its cavities. The prediction of the force also results in a model for the deformation of the soft pad. Authors characterize our sensor and evaluate two designs, a soft pad and a kidney-shaped sensor, in different scenarios.
- Date 13 August 2019
- Tags Haptics, Robotics, SoftRobot