No abstract

Tactile sensing is essentially required for dexterous manipulation in robotic applications. Mimicking human perception of softness identification in a non-invasive fashion, thus achieving satisfactory interaction with fragile objects remains a grand challenge. Here, a scatheless measuring methodology based on the multisensory electronic skins to quantify the elastic coefficient of soft materials is reported. This recognition approach lies in the preliminary classification of softness by piezoelectric signals with a modified machine learning algorithm, contributing to an appropriate contact force assignment for subsequent quantitative measurements via strain sensing feedback. The integration of multifunctional sensing system allows the manipulator to hold capabilities of self-sensing and adaptive grasping motility in response to objects with the various softness (i.e., kPa-MPa). As a proof-of-concept demonstration, the biomimetic manipulator cooperates with the robotic arm to realize the intelligent sorting of oranges varying in freshness, paving the way for the development of microsurgery robots, human-machine interfacing, and advanced prosthetics.

A flexible piezoelectric sensor with frequency-response behaviour can enable the detection of the magnitude and loading rate of stimuli.