Next‐generation robots are being designed to function autonomously in complex and unstructured environments. In particular, based on the real‐time measurement and differentiation of normal pressure and shear force, robots can be equipped with the capabilities of damage‐free grasp within minimum force limits, as well as dexterous operation through surface roughness and slip information. Herein, a flexible tactile sensor with a small cylinder protrusion and four arc‐shaped protrusions is developed. Due to its center symmetry and axisymmetry characteristics, the normal pressure and shear force can be decoupled from the complex stress without any interference from torsion. The flexible tactile sensor exhibits good linearity and superior cycling stability and is capable of determining the magnitude and direction of the applied force accurately. The flexible tactile sensor is comfortable to wear, and it is integrated onto the manipulator to realize various delicate and dexterous tasks, such as pressure detection, interaction with fragile objects, and roughness identification. Moreover, intelligent recognition of the sliding and stationary states can be achieved by decoding signals of sliding friction and static friction from the feedback information, leading to real time and precise adjustment of the grasping state of the manipulator.
Humans can quickly determine the force required to grasp a deformable object to prevent its sliding or excessive deformation through vision and touch, which is still a challenging task for robots. To address this issue, we propose a novel 3D convolution-based visual-tactile fusion deep neural network (C3D-VTFN) to evaluate the grasp state of various deformable objects in this paper. Specifically, we divide the grasp states of deformable objects into three categories of sliding, appropriate and excessive. Also, a dataset for training and testing the proposed network is built by extensive grasping and lifting experiments with different widths and forces on 16 various deformable objects with a robotic arm equipped with a wrist camera and a tactile sensor. As a result, a classification accuracy as high as 99.97% is achieved. Furthermore, some delicate grasp experiments based on the proposed network are implemented in this paper. The experimental results demonstrate that the C3D-VTFN is accurate and efficient enough for grasp state assessment, which can be widely applied to automatic force control, adaptive grasping, and other visual-tactile spatiotemporal sequence learning problems.
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