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.

This paper present object handling capabilities of two robotic hands equipped with optical three-axis tactile sensor. We present optimization of grasp control in tactile sensor and robot hand control system to precisely control robot hand based on tactile sensing information. To enhance performance of the robot hand, stiffness distinction parameters were applied in the control system. These parameters are used to select velocity ratio of robot hand to control re-push motion and define optimum grasp pressure. In addition, slippage recognition method is also applied so that the robot hand can grasp and handle object located at arbitrary position. The proposed control system and parameters were evaluated in object handling experiments with hard and soft objects, and object located at arbitrary position. Experimental results revealed good performance for the robot hand in handling hard and soft objects, and object located at an arbitrary position.

In a previous paper we developed an optical three-axis tactile sensor that can acquire normal and shearing forces to be mounted on a robotic finger. Normal and shearing forces applied to the sensing element were detected separately; when we examined the repeatability of the present tactile sensor with 1,000 loading-unloading cycles, the respective error of the normal forces was 2%. In the present paper, the three-axis tactile sensor is mounted on a robotic finger of three degrees of freedom to evaluate it for dexterous hands. A series of three kinds of experiments were performed. First, the robotic hand touches and scans flat specimens to evaluate the sensing ability of the friction coefficient. Second, it detects the contour of parallelepiped and cylindrical objects. Finally, it manipulates a parallelepiped case put on a table by sliding it on the table. Since the present robotic hand was able to perform the above three tasks with appropriate precision, we expected that it would be applicable to dexterous hands in subsequent studies.

This paper presents development of tactile sensing-based control algorithm for humanoid robot finger system with optical three-axis tactile sensor mounted on fingertips. Our aim is to develop an intelligent control system that can recognize stiffness of unknown objects and respond to sudden changes of object’s weight during object manipulation. For this purpose, we developed a novel optical three-axis tactile sensor system based on an optical waveguide transduction method capable of acquiring normal and shearing forces. We proposed a control algorithm in the finger control system based on tactile and slippage sensations, and analyzed real-time grasp synthesis in object manipulation tasks. The control algorithm was designed to control fingertips movements by defining optimum grasp pressure and perform re-push movement when slippage was detected in object manipulation tasks. Verification experiments using humanoid robot fingers were conducted whose results revealed that the finger’s system managed to recognize the stiffness of unknown objects and complied with sudden changes of the object’s weight during object manipulation tasks.