Classification of Prism Object Shapes Utilizing Tactile Spatiotemporal Differential Information Obtained from Grasping by Single-Finger Robot Hand with Soft Tactile Sensor Array

Abstract

Our proposal involves classifying cylindrical objects by using soft tactile sensor arrays on a single five-link robotic finger. The front of each link is covered with semicircular silicone rubber with 235 small on-off switches. On-off data from switches obtained when an object is grasped is converted to a spatiotemporal matrix. Eight cells around the contact switch are useful in extracting local spatiotemporal contact physics, so the frequency of the 8-Cell patterns composed of binary data around the switch contacted is obtained for each object and used to form a contact-feature vector. This vector is obtained 10 times of experimental trial, corresponding to each object. Vectors are classified by the Mahalanobis distance for 12 objects - cylinders and regular polygonal prisms - resulting in 14 types of grasping (14 classes). Using 6 dimensional feature vectors, over 95% classification accuracy is obtained for 7 classes derived from 5 objects having one or two types of stable grasping.