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The human-like capabilities of robots are linked to their well-established perception systems. Tactile perception enables the robot to perceive the texture and grain of objects through touch. Robots equipped with tactile perception can grasp and manipulate objects more precisely and detect the characteristics and attributes of objects, which enhances their perceptual and cognitive abilities. Tactile perception provides robots with important advantages, helps them achieve human-like capabilities, and promotes the continuous development and innovation of robotic technology.
Herein, a bionic finger with a flexible shell, nail, finger bone, liquid, pressure-sensitive element, and temperature-sensitive element was developed based on the principle of liquid pressure conduction. The hardness, temperature, and texture sensing ability of the bionic finger were investigated; the tactile feature parameters of the bionic finger touching the textured surface were extracted; and classification and recognition of the fabric surface texture were achieved by the bionic finger using the support vector machine algorithm.
Results revealed that when the bionic finger applied pressure to three different materials, the rates of change in the pressure curve were in descending order: Lwo>Lfo>Lsp. These results were consistent with the hardness of the materials tested. The steepness of the temperature change curves obtained by the bionic finger touching the three materials was in descending order: Tss>Tpb>Two, which aligned with the thermal conductivities of the materials. As the roughness of the fabric surface increased, the peak average value and average power increased. Thus, a positive correlation existed between the peak average and average power values and roughness, namely the higher the peak average and average power, the higher the roughness of the fabric. With increasing fineness of the fabric surface, the dominant frequency and the spectral centroid increased, resulting in an enhanced sense of fineness. A significant positive correlation existed between the sense of fineness and both the dominant frequency and the spectral centroid. The larger the dominant frequency and the spectral centroid, the higher the sense of fabric fineness. The average accuracy of fabric surface texture recognition using the bionic finger and support vector machine method, based on the peak average, average power, dominant frequency, spectral centroid, and six frequency band feature intensities, was 92.8%, which was higher than the average human subjective recognition accuracy of 88.8%.
The rate of change of the touch pressure curve and the temperature curve of the bionic finger can indicate the softness and thermal conductivity of an object, indicating that the bionic finger has the ability to perceive hardness and temperature. The peak average and average power of the bionic finger extracted from the touch vibration signal can characterize fabric roughness, while the dominant frequency and the spectral centroid can characterize fabric fineness, indicating the ability of the bionic finger to perceive roughness and fineness. The average recognition accuracy of the bionic finger is higher than that of human subjective recognition, indicating the efficient and superior capability of the bionic finger to recognize and classify textile surface textures compared to human judgment.
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