Haptic identification of objects using tactile sensing and computer vision

A key component lacking in the most humanoid grasping type robot is its ability to accurately predict what it is holding. This article presents a new function to the robotic hands such as vision-based shape analysis and tactile sensing–based object identification. The vision-based shape analysis uses contour approximation–based algorithms to distinguish between soft and hard objects by comparing the contours. Tactile sensors attached to the fingertip of the robotic hand can grasp objects and predict the hardness and softness of the grasped object by analyzing the pressure data. An algorithm to control the robotic hand and acquire pressure and vision data is presented. With these added functions to the hard robotic hand, it is ideally suited for the control of active prosthesis and other engineering applications where delicate handling of the object is needed. The results show that hard objects have a sharper pressure slope compared to soft objects. Analyzing the pressure values from the touch surface was used to predict the hardness/softness of the object. The contour comparison of soft/hard objects which were grasped under the same conditions had different patterns that can be used to differentiate between soft and hard objects