Masahiro Ohka

Mechanisms of fine-surface-texture discrimination in human tactile sensation

The purpose of this study was to evaluate the ability of touch to discriminate fine-surface textures and to suggest possible mechanisms of the discriminations. Two experiments were performed. In experiment 1, aluminum-oxide abrasive papers were adopted as stimuli, and psychometric functions and difference thresholds were determined in fine-surface-texture discrimination tasks. The grit values of abrasive papers were 400, 600, 1200, 2000, 3000, 4000, and 8000; corresponding average particle sizes were 40, 30, 12, 9, 5, 3, and 1 micron, respectively. Ten subjects participated in experiment 1. The difference thresholds obtained in experiment 1 were between 2.4 and 3.3 microns. In experiment 2, the tasks were discriminations of ridge height. The cross sections of the etched ridges were rectangular and the ridge heights were 6.3, 7.0, 8.6, 10.8, 12.3, 18.5, and 25.0 microns. Six subjects participated in experiment 2. The difference thresholds in experiment 2 were between 0.95 and 2.0 microns. It was reasoned, based on the Weber fraction values calculated from the difference thresholds and on the limit of neural information-processing ability of humans, that the subjects discriminate fine roughness only from the amplitude information presented in surface unevenness.

An Experimental Optical Three-axis Tactile Sensor for Micro-Robots

This paper describes a micro-optical three-axis tactile sensor capable of sensing not only normal force, but also shearing force. The normal force was detected from the integrated gray-scale values of bright pixels emitted from the contact area of conical feelers. The conical feelers were formed on a rubber sheet surface that maintains contact with an optical waveguide plate. The shearing force was detected from horizontal displacement of the conical feeler. In the experiments, a precise multi-axial loading machine was developed to measure sensing characteristics of the present sensor. Results show that the normal force was specified uniquely under combined force conditions and that the shearing force was specified by modifying the relationship between the shearing force and the horizontal displacement on the basis of normal force. We formulated a set of expressions to derive the normal force and the shearing force by taking into account this modification. Furthermore, calibration coefficients were identified for transforming the integration of gray-scale values into the normal force and for transforming the horizontal displacement into the shearing force. This result suggests that the expressions can estimate the normal force and the shearing force in wide-load regions.

Sensing Precision of an Optical Three-axis Tactile Sensor for a Robotic Finger

We are developing an optical three-axis tactile sensor capable of acquiring normal and shearing force, with the aim of mounting it on a robotic finger. The tactile sensor is based on the principle of an optical waveguide-type tactile sensor, which is composed of an acrylic hemispherical dome, a light source, an array of rubber sensing elements, and a CCD camera. The sensing element of silicone rubber comprises one columnar feeler and eight conical feelers. The contact areas of the conical feelers, which maintain contact with the acrylic dome, detect the three-axis force applied to the tip of the sensing element. Normal and shearing forces are then calculated from integration and centroid displacement of the gray-scale value derived from the conical feelers contacts. To evaluate the present tactile sensor, we have conducted a series of experiments using a y-z stage, a rotational stage, and a force gauge, and have found that although the relationship between the integrated gray-scale value and normal force depends on the sensors latitude on the hemispherical surface, it is easy to modify the sensitivity according to the latitude, and that the centroid displacement of the gray-scale value is proportional to the shearing force. When we examined repeatability of the present tactile sensor with 1,000 load-unload cycles, the respective error of the normal and shearing forces was 2 and 5%

Sensing characteristics of an optical three-axis tactile sensor mounted on a multi-fingered robotic hand

To develop a new three-axis tactile sensor for mounting on multi-fingered robotic hands, in this work we optimize sensing elements on the basis of our previous works concerning optical three-axis tactile sensors with a flat sensing surface. The present tactile sensor is based on the principle of an optical waveguide-type tactile sensor, which is composed of an acrylic hemispherical dome, a light source, an array of rubber sensing elements, and a CCD camera. The sensing element of the present tactile sensor comprises one columnar feeler and eight conical feelers. The contact areas of the conical feelers, which maintain contact with the acrylic dome, detect the three-axis force applied to the tip of the sensing element. Normal and shearing forces are then calculated from integration and centroid displacement of the gray-scale value derived from the conical feelers contacts. To evaluate the present tactile sensor, we have conducted a series of experiments using a y-z stage, a rotational stage and a force gauge, and have found that although the relationship between integrated gray-scale value and normal force depends on the latitude on the hemispherical surface, it is easy to modify the sensitivity according to the latitude, and that the centroid displacement of the gray-scale value is proportional to the shearing force. Finally, to verify the present tactile sensor, we performed a series of scanning tests using a robotic manipulator equipped with the present tactile sensor to have the manipulator scan surfaces of fine abrasive papers. Results show that the obtained shearing force increased with an increase in the particle diameter of aluminium dioxide contained in the abrasive paper, and decreased with an increase in the scanning velocity of the manipulator over the abrasive paper. Because these results are consistent with tribology, we conclude that the present tactile sensor has sufficient dynamic sensing capability to detect normal and shearing forces.

Figure and texture presentation capabilities of a tactile mouse equipped with a display pad of stimulus pins

To obtain specifications for a tactile display that would be effective in virtual reality and tele-existence systems, we have developed two types of matrix-type experimental tactile displays. One is for virtual figures (display A) and the other is for virtual textures (display B). Display As pad has a 4 × 6 array of stimulus pins, each 0.8 mm in diameter. Three pad configurations, in which distances between any two adjacent pins (pin pitch) are 1.2, 1.9, or 2.5 mm, were developed to examine the influence of distance on a human operators determination of virtual figures. Display B has an 8 × 8 array of stimulus pins, each 0.3 mm in diameter and with 1-or 1.8-mm pin pitch, because presentation of virtual textures was presumed to require a higher pin density. To establish a design method for these matrix-type tactile displays, we performed a series of psychophysical experiments using displays A and B. By evaluating variations in the correct answer percentage and threshold caused by different pin arrays and different pin strokes, we determined under what conditions the operator could best feel the virtual figures and textures. The results revealed that the two-point threshold should be adopted as the pitch between pins in the design of the tactile display, that a pin stroke should exceed 0.25 mm, and that the adjustment method is the most appropriate to evaluate the capabilities of tactile displays. Finally, when we compared the virtual texture with the real texture, we found that the threshold for the real texture is almost 1/3rd that of the virtual texture. This result implies that it is effective to present variations in patterns caused by rotation and variation in shearing force, itself produced by relative motion between the finger surface and object surface.

Low force control scheme for object hardness distinction in robot manipulation based on tactile sensing

This paper presents an application of a low force interaction method in a control scheme of robot manipulation based on tactile sensing. Our aim is to develop an intelligent control system that can distinguish the hardness of unknown objects so that robotic fingers can effectively explore the objects surface without altering its physical properties or causing damage. Initially we developed a novel optical three-axis tactile sensor system based on an optical waveguide transduction method capable of acquiring normal and shearing forces. The sensors are mounted on the fingertips of the multi-fingered humanoid robot arm. We proposed a new control scheme applying low force interaction to distinguish the hardness of unknown objects in robot manipulation tasks based on tactile sensing. The scheme utilized new control parameters obtained by calibration experiments using hard and soft objects that enable robot fingers to precisely control grasp pressure and define the slippage sensation of the given object. Finally, verification experiments of the proposed control scheme using a humanoid robot arm were conducted whose results revealed that the fingers system managed to recognize the hardness of unknown objects and complied with sudden changes of the objects weight during object manipulation tasks.

A three-axis optical tactile sensor (FEM contact analyses and sensing experiments using a large-sized tactile sensor)

This paper describes a new three-axis tactile sensor equipped with an optical waveguide plate mounted on a robot manipulator. After a series of FEM contact analyses and evaluation experiments were conducted, an experimental large-sized tactile sensor intended for employment in evaluation experiments was designed and produced. The experimental results confirmed that the tactile sensor is capable of detecting the distribution of three-axis force and that the calculated and experimental results coincide well. On the basis of these results, a smaller tactile sensor mounted on a robot manipulator was designed and produced. This tactile sensor comprises a CCD camera, a light source, an acrylic board and a silicon rubber sheet that are assembled into a casing 180 mm long, 80 mm wide and 50 mm thick.

Object exploration and manipulation using a robotic finger equipped with an optical three-axis tactile sensor

To evaluate our three-axis tactile sensor developed in preceding papers, a tactile sensor is mounted on a robotic finger with 3-degrees of freedom. We develop a dual computer system that possesses two computers to enhance processing speed: one is for tactile information processing and the other controls the robotic finger; these computers are connected to a local area network. Three kinds of experiments are performed to evaluate the robotic fingers basic abilities required for dexterous hands. First, the robotic hand touches and scans flat specimens to evaluate their surface condition. Second, it detects objects with parallelepiped and cylindrical contours. Finally, it manipulates a parallelepiped object put on a table by sliding it. Since the present robotic hand performed the above three tasks, we conclude that it is applicable to the dexterous hand in subsequent studies.

Obstacle Avoidance in Groping Locomotion of a Humanoid Robot

This paper describes the development of an autonomous obstacle-avoidance method that operates in conjunction with groping locomotion on the humanoid robot Bonten-Maru II. Present studies on groping locomotion consist of basic research in which humanoid robot recognizes its surroundings by touching and groping with its arm on the flat surface of a wall. The robot responds to the surroundings by performing corrections to its orientation and locomotion direction. During groping locomotion, however, the existence of obstacles within the correction area creates the possibility of collisions. The objective of this paper is to develop an autonomous method to avoid obstacles in the correction area by applying suitable algorithms to the humanoid robots control system. In order to recognize its surroundings, six-axis force sensors were attached to both robotic arms as end effectors for force control. The proposed algorithm refers to the rotation angle of the humanoid robots leg joints due to trajectory generation. The algorithm relates to the groping locomotion via the measured groping angle and motions of arms. Using Bonten-Maru II, groping experiments were conducted on a walls surface to obtain wall orientation data. By employing these data, the humanoid robot performed the proposed method autonomously to avoid an obstacle present in the correction area. Results indicate that the humanoid robot can recognize the existence of an obstacle and avoid it by generating suitable trajectories in its legs.

Experiments on Stochastic Resonance Toward Human Mimetic Tactile Data Processing

In the present research, human tactile stochastic resonance (SR) capable of enhancing sensitivity by superimposing proper noise upon undetectable weak signals is utilized to enhance the tactile processing method for social robotics. We develop an experimental apparatus composed of a piezoelectric actuator and its controller, and generate a step several microns high mixed with noise to perform a series of psychophysical experiments. Since psychophysical experiments are conducted based on the Parameter Estimation by Sequential Testing (PEST) method, we produce a PEST program that generates a stimuli sequence based on PEST. The experimental result shows that variation in the difference threshold (Difference Limen; DL) has a local minimum point in the relationship between DL and noise. Therefore, the tactile sensation’s just noticeable difference (JND) is decreased by appropriate external noise. Since JND denotes the scale divisions of sensation in the human mind, the present result shows that precise tactile sensations are enhanced by the appropriate external noise. Finally, we introduce a neural network model composed of nonlinear neurons with the bi-stable equilibrium condition to explain this result. Although original sensor data do not represent the morphology of the fine texture, the neural network model extracts the morphology and distinguishes the wave amplitude of the fine texture.