In aging societies, there is a strong demand for robotics to tackle problems resulting from the aging population. Patient transfer, such as lifting and moving a bedridden patient from a bed to a wheelchair and back, is one of the most physically challenging tasks in nursing care. We have developed a prototype nursing-care assistant robot, RIBA, that can conduct patient transfer using human-type arms. The basic robot motion trajectories are created by interpolating several postures designated in advance. To accomplish more flexible and suitable motion, adjustment using sensor information is necessary, because the patient's posture and positions in contact with the robot may differ slightly in each trial. In this paper, we propose a motion adjustment method in patient lifting using tactile sensors mounted on the robot arms. The results of experiments using a lifesize dummy are also presented.

In aging societies, there is a strong demand for robotics to tackle problems resulting from the aging population. We have developed a prototype nursing-care assistant robot, RIBA, which was designed to come in direct contact with patients and conduct physically challenging tasks. RIBA interacts with its object, typically a human, through multiple and distributed contact regions on its arms and body. To obtain information on such whole-body contact, RIBA has tactile sensors on a wide area of its arms. The regions where hard contact with the manipulated person may occur have almost flat surfaces, leading to surface contact involving a finite area, in order to reduce contact pressure and not to cause the person's pain. When controlling the position and orientation of the person, the relative positions and orientations of the distributed contacting surfaces should be preserved as far as possible to maintain stable contact and not to graze the person's skin. Preserving the force and the pressure pattern of each contact region using tactile feedback is also important to provide stable and comfortable human-robot physical interaction. In this paper, we propose a whole-body contact manipulation method using tactile information to meet these requirements.

In aging societies, there is a strong demand for robotics to tackle problems caused by the aging population. Patient transfer, such as lifting and moving a bedridden patient from a bed to a wheelchair and back, is one of the most physically challenging tasks in nursing care, the burden of which should be reduced by the introduction of robot technologies. We have developed a new prototype robot named RIBA with human-type arms that is designed to perform heavy physical tasks requiring human contact, and we succeeded in transferring a human from a bed to a wheelchair and back. To use RIBA in changeable and realistic environments, cooperation between the caregiver and the robot is required. The caregiver takes responsibility for monitoring the environment and determining suitable actions, while the robot undertakes hard physical tasks. The instructions can be intuitively given by the caregiver to RIBA through tactile sensors using a newly proposed method named tactile guidance. In the present paper, we describe RIBA's design concept, its basic specifications, and the tactile guidance method. Experiments including the transfer of humans are also reported.

Bucky gel actuator is a novel electro-active polymer (EAP), which is a low-voltage driven dry soft actuator. In addition, the bucky gel device generates electromotive force when bending, and then it also can be used as a sensor. Its device has a bimorph structure with polymer-supported bucky gel electrodes and a polymer-supported ionic gel electrolyte. It can be fabricated by layer-by-layer casting, to form any shape easily. In this paper, we demonstrate an automatic fabrication method of the bucky gel devices based on a printing method. By using a dispensing machine, we construct the printing system, and manual forming process is replaced with automatic printing. We investigate the printing of complicated shapes and 3D electrode pattern through experiments.

Advances made in the research of IPMC (ionic polymer metal composite) actuators have shown that this technology is highly suitable for both minituarization, and use in environments that may prohibit the use of conventional actuation devices, such as submerged in a fluid. These characteristics indicate the compositepsilas suitability for implementation by small-scale aquatic vessels, such as those that operate within blood-streams to record and transmit biological data or interact with the blood to treat a medical condition. In order to develop such a robot, a suitable control system must be developed that allows for optimization of the IPMC motion to maximise thrust, given constantly changing conditions of the surrounding environment and parameters of the robot itself. This paper seeks to develop such a control system, first by developing a propulsion model for the robot, and then uses Extremum-seeking feedback control to optimise the swimming mode, with the results verified experimentally.

Human-interactive robots, such as those used for nursing, which share humans' environments and interact with them, should be covered with soft areal tactile sensors for safety and dexterous manipulation. We report the successful development of the tactile sensor system of our human-interactive robot named RI-MAN, which can lift up a dummy human.

Recently, active researches have been performed to increase a robot's intelligence so as to realize the dexterous tasks in complex environment such as in the street or homes. However, since the skillful human-like task ability is so difficult to be formulated for the robot, not only the analytical and theoretical control researches but also the direct human motion mimetic approach is necessary. In this paper, we propose that to realize the environmental interactive tasks, such as human care tasks, it is insufficient to replay the human motion along. We show a novel motion generation approach to integrate the cognitive information into the mimic of human motions so as to realize the final complex task by the robot.

Bucky gel actuator is a novel electro-active polymer (EAP), which is a low-voltage driven dry soft actuator. Its device has a bimorph structure with polymer-supported bucky gel electrodes and a polymer-supported ionic gel electrolyte. It can be fabricated by layer-by-layer casting, to form any shape easily. In addition, the bucky gel device generates electromotive force when bending, and then it also can be used as a sensor. Since sensor and actuator functions exist in a same device, flexible and miniature integrated actuator-sensor systems for soft robotics can be constructed easily. In this study, we conduct some experiments to verify the possibility of the bucky gel actuator/sensor. Feedback controls of the actuator are demonstrated and the characteristics of the sensor are investigated. By utilizing both functions of the actuator and the sensor, feedback control based on the sensor signal is demonstrated.

We investigated readiness potential of the motor cortex of human subjects while they performed motor imagery tasks. The purpose was to find possible applications for use in brain-computer interfaces. The results showed that there was some similarity in electroencephalogram (EEG) potentials when subjects performed either motor execution tasks or motor imagery tasks. Fourier transform analysis revealed that frequencies less than 5Hz played a particularly important role while subjects performed both types of task. Our results show that measuring EEG potentials as a way of “reading” peoples’ thoughts while they performs motor imagery tasks involving their hands may have applications in brain-computer interfaces.

Our goal is to create advanced engineering systems such as a soft human interactive robot. The robot developed here is named RI-MAN. RI-MAN exhibits the skill and ability to realize human care and welfare tasks. RI-MAN can search out a specific person in real time by fuing audio and visual information, and understand human speech based on a sound recognition function. In addition, RI-MAN's body is coverd with soft touch sensors, and RI-MAN can react to the amplitude and location of external forces. Using all these sensor functions, RI-MAN can successfully follow human commands and hold up a dummy of the same size as an adult human. RI-MAN will become an invaluable partner robot.

This paper describes an eye-gaze interface using a biological signal, electro-oculorgram (EOG). This interface enables a user to move a computer cursor on a graphical user interface using eye gaze movement alone. It will be useful as a communication aid for individuals with mobility handicaps. Although EOG is easily recordable, drifting and blinking problems must be solved to produce a reliable eye-gaze interface. Here we introduced a calibration method and a feedback control to overcome these problems.

We describe a two-dimensional (2-D) multi-degree-of-freedom (DOF) robot manipulator made from a soft polymer gel actuator, which we call an artificial muscle. The independent voltage control of three segments of patterned electrodes on the artificial muscle enables independent partial bending motions of each segment and realizes a multi-DOF control of the manipulator. We introduce the kinematic modeling of these multi-DOF bending motions and propose visual feedback control of the artificial muscle. For high-speed visual feedback, we have developed a visual sensing system with a 1ms sampling rate. Finally, we show the experimental results of the visual sensing and kinematic modeling of the bending motions of the manipulator.

In this research, we have developed a variable baseline stereo tracking vision system using a 1ms high-speed vision system and a high-speed linear slider. The vision system tracks a moving object, captures a stereo image pair and estimates its distance in only 1ms. It also calculates stereo matching and estimates its 3D shape every 2ms. The high speed linear slider moves stereo cameras horizontally and independently so the baseline length can be changed. We also propose a baseline control method which is adaptive to the distance to the object. When the object appears large in the image, the system reconstructs its 3D shape, and when the object appears small in the image, the system only estimates the 3D position of its center of gravity. The experimental results show that the proposed baseline control method improves the depth accuracy and the system can track and estimate the 3D shapes of fast moving objects.

We have thus far developed two types of sound source localization system, one of which can localize the horizontal direction and the other the vertical direction. These systems can acquire the localization ability by self-organization through repetition of movement and perception. In this paper, we report a newly built sound source localization system that can detect the direction of a sound source arbitrarily located in front of it. This system is composed of a robot that has two microphones with reflectors corresponding to human's pinnas. To acquire the horizontal direction, the interaural time difference is used as the auditory cue. To acquire the vertical direction, the features on the audio spectrum induced by the reflectors are used as the auditory cue. The robot can establish the relationship between the cues and the sound direction through learning.

A human being understands the environment by integrating information obtained by sight, hearing and touch. To integrate information across different senses, a human being must find the correspondence of events observed by different senses. The paper seeks to relate the audio-visual events caused by more than one movement according to general physical laws without object-specific knowledge. As corresponding cues, we use Gestalt's grouping law; simultaneity of the occurrence of sound and change in movement, similarity of time variation between sound and movement, etc. We conducted experiments in a real environment and obtained satisfactory results showing the effectiveness of the proposed method.

We obtain an exact 3D model of a scene using multiple images from different camera positions. The recovery of a 3D shape using two images has problems such as being weak in noise. Therefore we present methods to improve the accuracy of 3D shapes with multiple images. The system is divided into three stages: (1) the recovery of 3D shapes from different viewpoints, (2) fusing the 3D shapes to obtain a para-ideal shape, and (3) removing the outlier shapes and feature points by an evaluation function, and fusing the rest of the shapes. We demonstrate a significant improvement in the recovered shape. Experimental results show that our system performs well in removing noise with robustness. The maximum noise reduction rate was 82% in real image experiments.

Object shape and camera motion recovery from an image sequence is a crucial issue in computer vision and many methods have been proposed by researchers. Theoretically, these methods are perfect, but they are sensitive to noise, so that in many practical situations, satisfactory results cannot be obtained. To solve this problem, we propose a shape and motion recovery method using a gyro sensor attached an a video camera for compensating images. We made an experimental system with a CCD camera and a gyro sensor. Using this system, we have examined the accuracy of our method and obtained good results.

Shape recovery methods from an image sequence have been studied by many researchers. Theoretically, these methods are perfect, but they are sensitive to noise, so that, in many practical situations, we could not obtain satisfactory results. In addition, we could not obtain the scale of the recovered object because of the image-projection property. For solving these problems, we propose a method for shape and scale recovery which uses an acceleration-gyro sensor attached on a CCD camera for compensating images.

Image processing systems, each consisting of massively parallel photodetectors and digital processing elements on a monolithic circuit, are being developed by several researchers. This approach is promising for applications in robot vision and visual inspection. Some early vision-like processing algorithms, such as edge-detection, smoothing and thinning, are installed in the vision systems. However, they are not sufficient for applications because their output is in the form of pattern information, so that, in order to respond to input, some feature values are required to be extracted from the pattern. We propose a method for extracting feature values associated with images in a massively parallel vision system. The feature can be a complex one, such as the rotation angle of an object, if the feature value changes continuously as the image changes. The method is based on the eigenspace method, but modified to be robust. In addition, we propose some computation accelerating methods and report some experimental results.

Image changes produced by a moving camera (optical flow) are an important information source of the observer's motion and structure of environment. Determination of the 3D motion and structure from optical flow is one of the most important problems in computer vision and intensively studied by researchers. However, previous works in this field have been unsatisfactory because they require the solution of nonlinear simultaneous equations using iterative search. In the present paper, we propose a method for the recovery of motion and structure from prospectively projected optical flow of feature points which move rigidly. In order to simplify the computation and obtain clear perspectives, we adopt a spherical image screen and arrange equations in linear form. As a result, our method does not require the solution of nonlinear simultaneous equations, but only requires solving linear ones. Simulation results of our method are also presented.

A new method for sensor fusion is proposed and a sensor fusion system using the method with parallel processing architecture is shown. The method utilizes mappings between sensor data and can be applied not only to static coordination transforms between sensor data, but also to the dynamics of actuators. Capabilities of the method are confirmed by edge tracking experiments using the experimental sensor fusion system with a PSD (position sensitive detector), a CCD camera and an XY-recorder. The system is based on parallel processing architecture using transputers in a network. It is concluded that parallel processing is an intrinsic architecture for sensor fusion.