Optical Soft Tactile Sensor Algorithm Based on Multiscale ResNet

When grabbing an object, a robot needs to be installed with a sensor on its finger to sense the position of the object and the intensity of the contact force. Therefore, the accuracy of the tactile sensor's recognition is very important to grasp the object successfully. In our work, we design a data acquisition scheme and establish the corresponding deep-learning dataset taking the optical-fiber-based tactile sensor as the research object. This kind of sensor is composed of different layers. Those layers are deformed when the force is applied to the surface. Since the extent of deformation is proportional to the value of the force, the color and brightness of the collected pictures will change according to the Poisson effect. A novel multiscale (MS) ResNet is proposed and compared with some mainstream networks such as AlexNet, ResNet, VGG, DenseNet, and GoogLeNet under the same dataset. The dataset we collected contains normal force, shear force, and torsion. It can provide better calibrations between the image change and force value. The proposed network is investigated experimentally. The results demonstrate that it can achieve enhanced performance by extracting image features of different scales. This MS ResNet can be widely applied to the deep-learning training of mechanic sensors based on the image and is also well-suited for the calibration tasks such as classification and regression prediction of mechanics sensors based on multiple data features.