Trajectory generation of robotic fingers based on tri-axial tactile data for cap screwing task

Abstract

In a previous paper, we developed a robotic finger equipped with optical three-axis tactile sensors, of which the sensing cell can separately detect normal and shearing forces. With appropriate precision, the robotic finger was able to perform three tasks: scanning flat specimens to obtain the friction coefficient, following the contour of objects, and manipulating a parallelepiped case put on a table by sliding it on the table. In the present study, designed as a follow-up to the above study, a robotic hand is composed of two robotic fingers. Not only tri-axial force distribution directly obtained from the tactile sensor but also the time derivative of the shearing force distribution are used for the hand control algorithm: if grasping force measured from normal force distribution is lower than a threshold, grasping force is increased; the time derivative is defined as slippage; if slippage arises, grasping force is enhanced to prevent fatal slippage between the finger and an object. In the verification test, the robotic hand screws a bottle cap to close it. Although input finger trajectories were a rectangular roughly decided to touch and screw the cap, a segment of the rectangular was changed from a straight line to a curved line to fit the cap contour. We concluded that higher order tactile information such as tri-axial tactile data can reduce the complexity of the control algorithm.