In recent years, research and development of human-assist robots equipped with brain-computer interfaces (BCIs) have accelerated due to the increasing number of stroke cases worldwide, which are a major contributor to permanent disabilities and loss of independence. BCIs can be an effective neurorehabilitation tool, as they assist the impaired sensorimotor loop by providing compensatory somatosensory feedback during motor attempts. The human-assist robot must support the user’s movements based on their intended actions. However, determining whether the robot’s assist motion is as intended by the user in real time remains challenging. This paper proposes a multi-stage method that utilizes time-frequency and statistical analyses for electroencephalography (EEG) signals to detect whether the arm movements are as intended. Furthermore, it employs a binary classifier with threshold optimization—guided by the highest classification accuracy— to determine the most effective threshold for distinguishing between intentional and unintentional movements. The effectiveness of the proposed method was evaluated through experiments involving multiple arm movements that combine both simple and complex tasks. A disturbance mechanism device was built and employed during these experiments to generate the sense of motion mismatch by applying disturbance forces. The experimental results demonstrate the effectiveness of the proposed method in detecting motion mismatch and distinguishing between intentional and unintentional movements. The proposed method is applicable to human-assist robots to assess whether the robot’s assist movement matches the user’s intended motion.

Stabilizing walking motion is important to avoid unexpected falling for physically weak persons such as elderly. It is known that light touch makes human posture stable. This paper presents the way to realize the effect of light touch to make human walking stable using an omnidirectional mobile robot. The mobile robot has been developed to keep light touch contact to the lower back of the user during walking. In this study, the proper amount of light touch force for lower back and its contact location are investigated at first. Then it is realized by the mobile robot while the user is walking. The effectiveness of the proposed robotic light touch method was evaluated by performing the experiment.

Artificial motion sensation can be used for virtual reality, rehabilitation, etc. It is known that joint motion illusion is induced by giving certain vibration stimulation on human muscles even though the joint is not actually moved. In this paper, a method to generate motion illusion of arbitrary hand reaching motion, which is one of the most important daily living movements, by giving multiple vibration stimulations on shoulder and elbow muscles is proposed. In order to generate hand reaching motion illusion, frequency of vibration stimulation is controlled on each muscle at the same time considering the spatial hand movement in the proposed method. The experimental of generating hand reaching motion illusion is carried out to evaluate the effectiveness of the proposed method.

Engineering support for sports has the potential to improve training performance. Most research on sports training support has focused on support in areas that directly affect the point of force action. On the other hand, even if sports with hand-held tools, the importance of lower body motor function has been indicated. This suggests that supporting areas away from the point of force action can improve the performance of novice players. Therefore, in this study, we developed an assist suit that facilitates lower body twisting for table tennis beginners during forehand swing. U sing this assistive suit, experiments were conducted for the experimental group under the following four conditions (1) “No wear (before),” (2) “Without assist,” (3) “With assist,” and (4) “No wear (after)” to clarify the effect of wearing, assist, and training. In addition, a control group in which participants hit the ball without the assist suit was set up to examine the training effect of repetition. The results showed that both the amount of waist rotation and racket velocity increased significantly in the experimental group compared to the control group, which indicates the effect of the assist suit on training.

For universal robotic limbs, having a large workspace with high stiffness and adjustable output properties is important to adapt to various situations. A combination of parallel mechanisms that can change output characteristics is promising to meet these demands. As such, we propose a lever mechanism with double parallel link platforms. This mechanism is composed of a lever mechanism with the effort point and the pivot point; each is supported by a parallel link mechanism. First, we calculated the differential kinematics of this mechanism. Next, we investigated the workspace of the mechanism. The proposed mechanism can reach nearer positions than the posture with the most shrinking actuators thanks to the three-dimensional movable effort point. Then, we confirmed that this mechanism could change the output force profile at the end-effector by changing the lever ratio. The main change is the directional change of the maximum output force. The change range is larger when the squatting depth is larger. The changing tendency of the shape of the maximum output force profile by the position of the pivot plate depends on the force balance of the actuators. These analytical results show the potential of the proposed mechanism and would aid in the design of this mechanism for robotic limbs.

Human-assist robots are designed to reduce the burden on the body when lifting objects and assist the elderly, disabled, and other people with muscle weakness in their daily activities. They also avoid accidents and assist motions by recognizing what kind of motion the user is about to make. Therefore, human-assist systems need to estimate the motion intention of a user in real time. The sooner the assist robot can accurately recognize the user’s motion, the sooner the assist robot can plan which motion to assist and how to assist it to guarantee success. If the user’s intended motion and its phase are estimated in the early stage of the motion, the assist robot can Figure out how the user is moving by comparing with standard motion models to assist the motion as necessary. This paper proposes a method to estimate the user’s intended motion and its phase simultaneously in real time based on integrated information consisting of the user’s posture, motion, EMG signals, and the surrounding environment. Two kinds of artificial neural networks are applied in the proposed method. Damping neurons are used in the artificial neural network to estimate the motion phase effectively. The intended lower-limb motions and their phases in daily living motion are estimated in real-time. The effectiveness of the proposed method was evaluated by performing experiments of lower-limb motion.

Essential tremor (ET) is one of the most common movement disorders. Undesired involuntary periodic movements are generated in ET patients. Some devices have been developed to suppress ET movements up to the present. In this paper, Tonic Vibration Reflex (TVR) is studied to investigate the possibility to suppress ET movements. The TVR is a phenomenon of human reflex which is induced by applying mechanical vibration stimulation to a human muscle. If a counter-phase movement of the ET movement of the patient is artificially generated by the TVR, the ET movement would be canceled. In order to investigate its possibility, fundamental study on the TVR is carried out in this study. In order to confirm the possibility of generating the TVR in forearm pronation/supination, the effect of the amplitude and the range of frequency of vibration stimulation on the TVR in forearm pronation/supination is studied in this paper. The effect of forearm posture is also studied. Furthermore, the effect of the on-and-off vibration stimulation on the TVR in forearm pronation/supination is investigated. The experimental results show the possibility of tremor movement suppression with the TVR.

Minimally Invasive Surgery (MIS) is one of the most successful applications of surgical robots. Although the introduction of robotic technology has brought a number of benefits, further advancements in MIS are limited by the size and bending radius of instruments. In this paper, we present a compliant four degree-of-freedom manipulator that consists of elastic elements with partly thinner structures. The proposed mechanism allows the elastic element to deform locally, thus minimizing its bending radius while the low number of mechanical parts greatly contributes to its compactness. This paper describes the design strategy, optimization method using FEA, prototype implementation, and evaluations. The evaluations reveal high accuracy and repeat accuracy, which are key elements for robotic instruments in MIS. Further, the prototype is able to exert sufficient force and it is possible to perform a simulated needle insertion task using the manipulator, demonstrating the feasibility of the proposed mechanism.

A human's motion can be changed from the intended motion by giving vibration stimulation on human body. In our previous study, it is shown that the amount of human motion change can be controlled by using frequency change of vibration stimulation. In order to realize a perception-assist for elderly and/or physically handicapped persons with vibration stimulation, it is important that the amount of motion change can be changed even under loaded condition. In this study, the effect of loaded conditions on elbow joint to elbow joint extension motion change was experimentally investigated with several frequency of vibration stimulation. The results of the experiments show that the amount of motion change in elbow joint extension motion can be increased with increase of frequency even though vibrated body parts is under loaded condition.

Assisting aging or disabled people is a vital problem in today's world, due to various reasons. For this purpose, wearable power-assist robots have been proposed to assist their activities of daily living. However, in some cases there are users not only motor ability, but also sensory ability is deteriorated. In such case, the user might not be able to perceive the environment properly. As a result of that, the user might not be able to interact with other people correctly and timely. This paper presents a method to estimate interacting motion intention for perception-assist with an upper-limb wearable power-assist robot. In this method, interacting motion intention is identified using visual information taken from the wearable camera. Motions classifier is trained to identify a similar motion early to assist the perception of the user. An experiment is carried out to evaluate the effectiveness of the proposed method. A framework is suggested to identify the motion intention in real-time, by combing the existing descriptors, in terms of computational time and accuracy. Based on the results obtained by the visual information, user's motion is suggested to be modified by the power-assist wearable robot to assist the perception of the user.

Human motion can be changed from his/her intended motion by giving vibration stimulation to an antagonist muscle. One of the causes of this motion change is supposed to be the kinesthetic illusion which is induced by vibration stimulation. On the other hand, it is known that there is a relationship between the vividness of kinesthetic illusion and the frequency. When the frequency increases, the vividness of kinesthetic also increases. It indicates that the amount of human motion change induced by vibration stimulation can be controlled by frequency change. In this study, in order to evaluate the effect of frequency change to the amount of human elbow joint motion change, a psychophysical experiment was carried out with 4 healthy experimental participants. In this experiment, vibration stimulation was added to the tendon of biceps brachii during elbow joint extension motion. The frequency of vibration stimulation was 30, 60 and 90 (Hz) with each trial of the experiment. The experimental results show that the amount of elbow joint motion change increases when the frequency increases. These results indicate that the Perception-Assist with vibration stimulation can be carried out using frequency change.

A Meal Assistance Robot is an assistive device that is used to aid individuals who cannot independently direct food to their mouths for consuming. For individuals who undergo loss of upper limb functions due to amputations, spinal cord injuries or cerebral palsy, self-feeding can be impossible, and to assist such individuals in regaining their independence meal assistance robots have been introduced. In this paper we propose a meal assistance robot that is controlled using user intentions based on Electroencephalography (EEG) signals while incorporating camera-based automatic mouth position tracking and mouth open detection systems. In the proposed system, users select any solid food item that they desire to consume from three different containers by looking at corresponding flickering LED matrices. User intentions are identified through EEG signals using a Steady State Visual Evoked Potentials (SSVEP) based intention detection method. Initial motion commands for scooping food from the containers are generated and sent to the meal assistance robot from this first stage. At the second stage, a camera-based mouth position tracking method is proposed for automatically detecting the user's mouth position and thereby moving the spoon or end-effector of the meal assistance robot towards the mouth of the user. This method is capable of automatically tracking the mouth position of users irrespective of their individual body differences and seating positions. A mouth open/closed recognition method is implemented at the final stage in order to feed food to the users when they desire consumption, indicated by the opening/closing of their mouth. A set of experiments were carried out with healthy subjects to validate the proposed system and results are here presented.

In the perception-assist, user's dangerous motion is changed to safety motion when there is a possibility of accident such as the collision between user's body parts and obstacles. On the other hand, the joint motion of a human can be changed by using vibration stimulation on the tendon of muscle. By using this motion change method, there is a possibility that perception-assist by using vibration stimulation can be realized. The aim of this study is to investigate the effects of vibration stimulation to elbow joint motion under elbow joint loaded conditions. Vibration stimulation was added to the tendon of biceps brachii during elbow joint extension motion in the experiment of this study. During the experiment, subject's elbow joint load condition is changed by using weight which is grasped in their vibrated arm's hand. The results show that motion change can be generated in all trials with all subjects under the loaded condition. Furthermore, as the weight increases, the amount of elbow joint motion changing rate decreases.

In order to assist the daily life motion of physically weak persons such as elderly persons, many kinds of power-assist robot have been developed. In the case of physically weak persons, perception ability to perceive the surrounding environment is often deteriorated also. A method of perception-assist has been proposed to assist not only the user's motion but also interaction of the user with his/her environment. Perception-assist automatically modifies the user's motion when the power-assist robot detects the possibility of accident such as collision between the user and some obstacles. It is useful function for physically weak persons' daily motion assist. However, since unintentional motion is automatically generated by the robot, regardless of the user's motion intension, the user might feel uncomfortableness. In this paper, another motion modification method is investigated to make natural motion modification in perception-assist. It is known that kinesthetic illusion is generated and subjects feel as if their antagonist muscle is elongated when vibration stimulation is added to tendon of antagonist muscle around a joint. The possibility of the motion modification using vibration stimulation is investigated in this study. Vibration stimulation is added to biceps brachii during the elbow joint motion. Some features of motion modification using vibration stimulation were obtained during the elbow joint motion by performing the experiments.

The Rubber Hand Illusion is a well known multisensory illusion in which the participant feels their own unseen hand become a rubber hand that is placed at the position where their hand is supposedly placed, triggered by synchronized haptic stimuli on their hand and the rubber hand. Our previous study showed that a robotized 1-degree-of-freedom rubber hand in the form of a master-slave system significantly enhanced the illusion compared with the classic rubber hand illusion. The illusion gives the sense that the part of body becomes part of the robot - it is thus assumed that this multisensory illusion is a potentially effective way to increase intuitiveness, and therefore increase the operability of a multi-degrees-of-freedom master-slave system. To further investigate the concept, we built a multisensory illusion inducible microsurgical robotic system based on the findings of the previous study and conducted a series of psychophysical experiments. The experimental results reveal that the multisensory illusion and operability is significantly correlated.

A lower-limb power-assist robot is expected to assist physically weak persons to walk, stand up, or sit down. However, since the environment perception ability of the physically weak persons is sometimes deteriorated also, falling accident might occur even though the power-assist is applied. A concept of perception-assist has been proposed to avoid an unexpected accident. This paper proposes a perception-assist method that the robot monitors interaction between the user and the surrounding environment and modifies the user's motion if it is necessary. In the case of sitting on the chair which is not located in a proper position, the robot performs the perception-assist to modify falling motion to squatting motion by adding the additional force from behind. The effectiveness of the proposed method was evaluated by performing the experiments.

A power-assist exoskeleton is expected to help the motion of physically weak person in daily living. The power-assist exoskeleton can be applicable to a stroke survivor also, though the motion intention of the paralyzed limb is difficult to be estimated. This paper proposes a control method of the lower-limb power-assist for a stroke survivor. In the proposed method, the lower-limb motion of the abled side of the user is copied as the desired motion of the paralyzed side. Then the desired lower-limb motion of the paralyzed side is generated by the lower-limb power-assist exoskeleton robot. ZMP is taken into account to avoid the unexpected falling. The effectiveness of the proposed method was evaluated by performing the experiments.

An EMG signal shows almost one-to-one relationship with the corresponding muscle. Therefore, each joint motion can be estimated relatively easily based on the EMG signals to control wearable robots. However, necessary EMG signals are not always able to be measured with every user. On the other hand, an EEG signal is one of the strongest candidates for the additional input signals to control wearable robots. Since the EEG signals are available with almost all people, an EEG based method can be applicable to many users. However, it is more difficult to estimate the user's motion intention based on the EEG signals compared with the EMG signals. In this paper, a user's motion estimation method is proposed to control the wearable robots based on the user's motion intention. In the proposed method, the motion intention of the user is estimated based on the user's EMG and EEG signals. The EMG signals are used as main input signals because the EMG signals have higher correlation with the motion. Furthermore, the EEG signals are used to estimate the part of the motion which is not able to be estimated based on EMG signals because of the muscle unavailability.

In the case of some elderly or disabled persons, not only the motor ability, but also the environment perception ability is sometimes deteriorated. To assist the daily living motion of those people, power-assist robots with the perception-assist have been proposed. The power-assist robot with the perception-assist assists not only the user's motion but also the user's interaction with an environment, by applying the modification force to the user's motion if it is necessary. Since it is difficult for the robot to prepare all proper perception-assist for every task, tool, and environment previously, the robot needs to learn the proper perception-assist for each task, tool and environment by itself. The effectiveness of the performed perception-assist by the robot has been judged by the EMG signals. However, if the EMG signals do not change enough for the judgment, the learning of the robot might not succeed. In this paper, both EMG signals and EEG signals are measured at the same time to observe the features of these signals when users use the power-assist robot. EEG signals are used as the criteria of the effectiveness of the performed perception-assist in addition to EMG signals.

Many kinds of power-assist robots have been developed in order to assist self-rehabilitation and/or daily life motions of physically weak persons. Several kinds of control methods have been proposed to control the power-assist robots according to user's motion intention. In this paper, an electromyogram (EMG)-based impedance control method for an upper-limb power-assist exoskeleton robot is proposed to control the robot in accordance with the user's motion intention. The proposed method is simple, easy to design, humanlike, and adaptable to any user. A neurofuzzy matrix modifier is applied to make the controller adaptable to any users. Not only the characteristics of EMG signals but also the characteristics of human body are taken into account in the proposed method. The effectiveness of the proposed method was evaluated by the experiments.

In the case of some physically weak persons such as elderly persons, the environment perception ability is sometimes deteriorated also. To assist the daily living motion of those people, power-assist robots with the perception-assist have been proposed. The power-assist robot with the perception-assist assists not only the user's motion but also the user's interaction with an environment, by applying the modification force to the user's motion if it is necessary. In the case of lower-limb motion, the walking is the very important motion for a person to achieve daily activities. Some elderly persons might stumble on the bump and fall down in the stairs because they might not be able to lift own lower-limb well and/or recognize stairs correctly. In this paper, specifically, the stairs-ascending/descending assist for a lower-limb power-assist robot is proposed to prevent the user from falling down in stairs. ZMP of the user is taken into account in the proposed method. The effectiveness of the proposed method has been evaluated by the performing experiments.

In the case of some elderly whose motor ability is deteriorated, the environment perception ability is sometimes deteriorated also. In order to deal with that problem, the power-assist robot with the perception-assist, which assists not only the user's motion but also the user's interaction with an environment, has been proposed. The power-assist robot with the perception-assist has some sensors in order to monitor the interaction between the user and the environment. The robot tries to modify the user's motion automatically during the power-assist if the robot recognizes any problems in the user's motion. A human has a dexterous hand with many DOFs, and performs tasks while using many tools in the hand. Since many kinds of tools are used in daily activities, the undesired motion such as a risky behavior to the person is different between the tasks or tools. From these reasons, when the power-assist robot carries out the perception-assist, the robot needs to perform the perception-assist based on the user's motion and the grasped tool by the user. However, it is not realistic to prepare the perception-assist required for every tool or task because there are many kinds of tools and tasks. In this paper, the perception-assist for an upper-limb power-assist robot is discussed. The robot learns the properly required perception-assist based on EMG signals. The effectiveness of the proposed method has been evaluated by the performing experiments.

In this paper, a new desktop orthogonal-type robot, which has abilities of compliant motion and stick-slip motion, is first presented for lapping small metallic molds with curved surface. The robot consists of three single-axis devices with a high position resolution of 1 μm. A thin wood stick tool is attached to the tip of the z-axis. The tool tip has a small ball-end shape. The control system is composed of a force feedback loop, position feedback loop and position feedforward loop. The force feedback loop controls the polishing force consisting of tool contact force and kinetic friction forces. The position feedback loop controls the position in pick feed direction, e.g., z-direction. The position feedforward loop leads the tool tip along a desired trajectory called cutter location data (CL data). The CL data are generated from the main-processor of a CAM system. The proposed robot realizes a compliant motion required for the surface following control along a spiral path. In order to improve the lapping performance, a small stick-slip motion control strategy is further added to the control system. The small stick-slip motion is orthogonally generated to the direction of the tool moving direction. Generally, the stick-slip motion is an undesirable phenomenon and should be eliminated in precision machineries. However, the proposed robot employs a small stick-slip motion to improve the lapping quality. The effectiveness of the robot is examined through an actual lapping test of an LED lens mold with a diameter of 4 mm.

This paper proposes a method for interpretation of fuzzy voice commands based on the vocal cues. The fuzzy voice commands include fuzzy linguistic terms like “little” and quantitative meaning of such terms depends on the environmental conditions. Therefore the robot's perception of the corresponding environment is modified by acquiring the user's perception through a series of vocal cues. The user's willingness to change the robot's perception is identified based on the vocal cues to improve the adaptation process. The primitive behaviors related to the end-effector movements of a robot manipulator are considered and evaluated by a behavior evaluation network (BEN). A vocal cue evaluation system (VCES) is used to evaluate the vocal cues to adapt the robot's perception by modifying the BEN. The user's satisfactory level for the robot's movements is utilized to track the satisfaction of the user. A situation of cooperative rearrangement of the user's working space is used to illustrate the proposed system by a PA-10 robot manipulator.

This paper proposes an electromyography (EMG) signal based control method for a seven degrees of freedom (7DOF) upper-limb motion assist exoskeleton robot (SUEFUL-7). The SUEFUL-7 is able to assist the motions of shoulder vertical and horizontal flexion/extension, shoulder internal/external rotation, elbow flexion/extension, forearm supination/pronation, wrist flexion/extension, and wrist radial/ulnar deviation of physically weak individuals. In the proposed control method, an impedance controller is applied to the muscle-model-oriented control method by considering the end effector force vector. Impedance parameters are adjusted in real time by considering the upper-limb posture and EMG activity levels. Experiments have been performed to evaluate the effectiveness of the proposed robotic system.

This paper proposes a method to adapt robot behaviors toward user's perception by human teaching. Human-friendly robotic system should be able to understand the fuzzy linguistic information based on the user's guidance and the environmental conditions. The contextual meaning of fuzzy linguistic information depends on the conditions of the environment. Therefore, user's perception is acquired to evaluate the fuzzy linguistic information in user commands based on fuzzy voice feedback. The primitive behaviors are evaluated by behavior evaluation network (BEN). Feedback evaluation system (FES) is introduced to evaluate the user feedback to correct the robot's perception by adapting the BEN. This yields the adaptation of the system for understanding fuzzy linguistic information toward the corresponding environment. A situation of cooperative rearrangement of user's working space is simulated to illustrate the system. This is demonstrated by using a PA-10 robot manipulator.

Many studies on power-assist exoskeleton robots have been carried out in order to assist daily activities and/or rehabilitation of physically weak persons. EMG-based control is one of the most effective control methods to realize the power-assist with the exoskeleton based on user’s motion intention. In this paper, a muscle-model, which is adjusted by a neuro-fuzzy modifier according to the user’s upper-limb posture, is introduced to realize an effective EMG-based controller of the power-assist exoskeleton. Force/torque generated between the user’s wrist part and the tip of the exoskeleton is used to train the neuro-fuzzy modifier. The effectiveness of the proposed control method was evaluated by performing experiment.

We have been developing exoskeleton robots for assisting the motion of physically weak individuals such as elderly or slightly disabled in daily life. In this paper we propose an EMG-based control of a three degree of freedom (3-DOF) exoskeleton robot for the forearm pronation/supination motion, wrist flexion/extension motion and ulnar/radial deviation. The paper describes the hardware design of the exoskeleton robot and the control method. The skin surface electromyographic (EMG) signals of muscles in forearm of the exoskeleton's user and the hand force/forearm torque are used as input information for the proposed controller. By applying the skin surface EMG signals as main input signals to the controller, automatic control of the robot can be realized without manipulating any other equipment. Fuzzy control method has been applied to realize the natural and flexible motion assist. An experiment has been performed to evaluate the proposed exoskeleton robot.

This paper presents a concept of an upper-limb power assist intelligent exoskeleton with visual information as a second stage of the research on power-assist exoskeleton systems in order to help daily activities of physically weak persons. The proposed exoskeleton assists not only the motion of the user but also the perception of the user using sensors and a stereo camera. In the proposed power-assist method, the assisted user’s motion can be modified based on the environmental information obtained by the sensors and the camera if problems are found in the user’s motion. The effectiveness of the proposed intelligent exoskeleton was evaluated by experiments.

To assist physically disabled, injured, and/or elderly persons, we have been developing a 3DOF exoskeleton robot for assisting upper-limb motion, since upper-limb motion is involved in a lot of activities of everyday life. The exoskeleton robot is mainly is controlled by the skin surface electromyogram (EMG) signals, since EMG signals of muscles directly reflect how the user intends to move. This paper introduces the mechanism of the exoskeleton robot and also proposes a control method of the exoskeleton robot considering the generated end-effector force vectors

Exoskeletons are expected to be used as wearable haptic devices and power assist robot systems. We have been studying exoskeletons to assist the human motion in daily activity and rehabilitation. The EMG (electromyogram), which directly reflects the human motion intention, has been used to control the exoskeleton without any special control equipment. This paper presents a strategy that realizes the effective adaptation of the EMG-based exoskeleton controller (i.e., robot-human interface) to any persons.

We have been developing exoskeleton systems to assist the motion of physically weak persons such as elderly, disabled, and injured persons. The proposed exoskeletons are controlled basically based on the electromyogram (EMG) signals. Even though the EMG signals contain very important information, however, it is not very easy to predict the user's upper-limb motion (elbow and shoulder motion) based on the EMG signals in real-time because of the difficulty in using the EMG signals as the controller input signals. In this paper, we propose a control method for a 3DOF exoskeleton system for human upper-limb motion assist considering the effect of bi-articular muscles.

This paper presents an exoskeleton for the assist of forearm motion (elbow flexion-extension and forearm pronationsupination motion) in daily activity and rehabilitation. The exoskeleton is controlled based on the activation patterns of the electromyogram (EMG) signals of the patient's muscles, which directly reflects the motion intention of the patient, in order to realize natural motion assist The sophisticated real-time neurofuzzy control method, in which the effect of a muscle common to both motions is taken into account, is proposed. The proposed control method enables the cooperative motion of elbow and forearm of the patient by learning the muscle activation patterns of each patient. The effectiveness of the proposed method was evaluated by experiment.

We have been developing robotic exoskeletons to assist motion of physically weak persons such as elderly, disabled, and injured persons. The robotic exoskeleton is controlled basically based on the electromyogram (EMG) signals, since the EMG signals of human muscles are important signals to understand how the user intends to move. Even though the EMG signals contain very important information, however, it is not very easy to predict the user's upper-limb motion (elbow and shoulder motion) based on the EMG signals in real-time because of the difficulty in using the EMG signals as the controller input signals. In this paper, we propose a robotic exoskeleton for human upper-limb motion assist, a hierarchical neuro-fuzzy controller for the robotic exoskeleton, and its adaptation method.

In this paper, we investigate the viability of multilayered neural network (NN)-based extension of a conventional "perception" control concept. The perception process selects and completes the information from the system to be controlled before passing it to the controlling agent so that control is not lost when sensory information from the system is incomplete. The perception process produces an expectation of the next set of information to be received from the system. The expectation is used to replace missing parts of the information received and it also influences the next perception. In the existing work, each of the expectation elements is linearly acquired such that the expectation tells only the dominant information in the recent past, i.e., this approach has no capability to sense the trend and the dynamics in the information. This handicap could become a serious problem when the perception process is applied to real physical systems. Here, we introduce an extension of the perception control process by using a radial basis function (RBF) feedforward NN to learn the trend and the dynamics in the information and produce the expectation of the next observation. Through some simulation comparisons, we show that the proposed RBFNN-based method is better than the existing one.

We present a methodology of controlling machines using spoken language commands. The two major problems relating to the speech interfaces for machines, namely, the interpretation of words with fuzzy implications and the out-of-vocabulary (OOV) words in natural conversation, are investigated. The system proposed in this paper is designed to overcome the above two problems in controlling machines using spoken language commands. The present system consists of a hidden Markov model (HMM) based automatic speech recognizer (ASR), with a keyword spotting system to capture the machine sensitive words from the running utterances and a fuzzy-neural network (FNN) based controller to represent the words with fuzzy implications in spoken language commands. Significance of the words, i.e., the contextual meaning of the words according to the machine's current state, is introduced to the system to obtain more realistic output equivalent to users' desire. Modularity of the system is also considered to provide a generalization of the methodology for systems having heterogeneous functions without diminishing the performance of the system. The proposed system is experimentally tested by navigating a mobile robot in real time using spoken language commands.

We have been developing exoskeletons (exoskeletal robots) for assisting the motion of physically weak persons such as elderly persons or slightly disabled persons in daily life. In this paper, we propose a 3 DOF exoskeleton and its control system to assist the human upper-limb motion (shoulder joint motion and elbow joint motion) of physically weak persons. The proposed robot automatically assists the human motion mainly based on the skin surface electromyogram (EMG) signals. Fuzzy control has been applied to realize the sophisticated real-time control of the exoskeleton. Experiment has been performed to evaluate the proposed exoskeleton.

We have already proposed the "rings gymnastic robot" aiming at an application to the gymnastic coaching. We have been also focusing on performance skill acquisition and obtained a skill of a backward giant circle using various models step by step. To apply the skill obtained by the robot to the coaching, many skills collected by a three-dimensional model and experiments are required. However, a handstand has been only realized by a two-link model. Therefore, in this study, we further acquire the performance skill of a handstand from backward giant circle using a three-link model, as a preliminary step toward analyses in three-dimension and experiments.

A concept called "common reference object" is proposed for cooperative transportation and decentralized system consisting of multiple noholonomic mobile robots is constructed to demonstrate the present concept. In this system, one agent acts as the leader, which is able to plan and manipulate the omnidirectional motion of the object. Other agents referred to, as followers are equipped with compliance arms and cooperatively transport the object by keeping a constant position relative to the object. During transportation operation, the leader robot can not only plan the motion of the object but also broadcast the local velocity of the object to other agents. Then, each follower receives such information and generates its own velocity in the local coordinate using a mapping process. In this paper, neural network (NN) and genetic algorithm (GA) are tested in identifying the mapping process. Simulation results show a good performance of the present system.

We present an exoskeleton to assist elbow and forearm motion of physically weak persons such as elderly, injured, or disabled persons. The forearm pronation/supination motion and the elbow flexion/extension motion, which are essential motions for the activities of daily living, are assisted by the proposed exoskeleton. The electromyogram (EMG) signals of muscles in forearm and upper-arm of the exoskeleton's user and the wrist force are also used as input information for the controller. By applying the EMG signals as main input signals to the controller, automatic control can be realized for the physically weak persons without manipulating any equipment. Fuzzy control has been applied to realize the natural and flexible motion assist. Experiment has been performed to evaluate the effectiveness of the proposed exoskeleton.

Recently robot manipulators have been expected to perform sophisticated tasks such as object manipulation, assembly tasks, or cooperative tasks with human workers. In order to realize these tasks with robot manipulators, it is important to understand the human strategy of object grasping and manipulation. In this study, we have examined how a human being decides the grasping force necessary to manipulate an unknown object in order to apply human object-grasping strategy for robotic systems. Experiments have been performed with several kinds of objects under several kinds of conditions to investigate how much grasping force human subjects generate. Adjustment strategy of human grasping force when the object is manipulated or in contact with an environment is also examined. Neural networks (the desired grasping force planner) that generate the humanlike desired grasping force are then designed for robotic systems. The effectiveness of the proposed desired grasping force planner is evaluated via experiments.

We develop exoskeletal robots for the motion support of physically weak persons. In this study, the human elbow motion is assisted by the exoskeletal robot system, since the elbow motion is one of the simplest and the most important motion of human beings in everyday life activities. The angular position and impedance of the exoskeletal robot system are controlled by multiple fuzzy-neuro controllers. The skin surface electromyogram (EMG) signals and the generated wrist force by the human subject during the elbow motion are used as input information of the controller. Since the activation level of working muscles tends to vary with the human subject's arm posture, an intelligent interface that cancels out the effect of posture changes of the human subject's arm is proposed in this paper. The experimental results show the effectiveness of the proposed intelligent interface.

This paper presents a new concept for solving fuzzy controller stability problems on matrix inequalities via evolutionary computation (EC). The gain scheduling problem of a multi-model fuzzy system, which satisfies the Lyapunov stability criteria, is solved. The generalized eigenvalue problem can be directly introduced into EC in searching positive definite or positive semi-definite matrices, by making a penalty for an individual, that violates the inequality condition in order to solve the nonlinear constraints or linear matrix inequalities. Examples are given to illustrate the effectiveness of the proposed methodology.

We have been developing exoskeletal robots in order to support the motion of physically weak persons such as elderly persons or handicapped persons. In our previous research, the 2-DOF exoskeletal robots for shoulder motion support and its control method have been developed since the shoulder motion is especially important for people to take care of themselves in everyday life. In this paper, we propose intelligent interface, which realizes the fuzzy-neuro controller adjustment in accordance with the human subject's arm posture, in order to effectively control the 2-DOF exoskeletal robot for shoulder motion support. The intelligent interface is realized by applying a neural network. The effectiveness of the proposed intelligent interface of the exoskeletal robot has been evaluated by experiment.

An adaptive actor-critic algorithm is proposed under the assumption that a predictive model is available and only the measurement at time k is used to update the learning algorithms. Two value-functions are realized as a pure static mapping, according to the fact that they can be reduced to nonlinear current estimators, which can be easily constructed by using any artificial neural networks (NNs) with sigmoidal function or radial basis function (RBF), if all the inputs to the present value-functions are based on simulated experiences generated from the predictive model. In addition, if a predictive model is assumed to be used to construct a model-based actor (MBA) in the framework of adaptive actor-critic approach, then this type of MBA can be viewed as a network whose connection weights are composed of the elements of feedback gain matrix, so that the temporal difference (TD) learning can also be naturally applied to update the weights of the actor. Since the present method can update the learning by using only one measurement at time k, a relatively fast learning is expected, compared with the previous approach that needs two measurements at times k and k + 1 to update the actor-critic networks. The effectiveness of the proposed approach is illustrated by simulating a trajectory-tracking control problem for a nonholonomic mobile robot.

We (2001) have proposed the "rings gymnastic robot" aiming at an application to the gymnastic coaching by understanding the ring exercises through the robot. The ring exercises have the characteristics that the apparatus can move in all directions freely unlike the other gymnastic events. Fuzzy control is adopted as a control method for the rings gymnastic robot. As the joint torque in performance is useful for the coaching and gymnasts can easily understand the skill to realize performance if the control method is represented as "if-then" rules. In this paper, we derive a three-dimensional model of the robot and acquire the performance skill of a backward giant circle using the genetic algorithm. Three-dimensional graphics sequence based on simulation results shows that the obtained skill is effective.

The exoskeletal robots are expected to support the daily activities of physically weak persons such as elderly persons or handicapped persons in these days. In our previous research, the exoskeletal robots for elbow or shoulder motion support have been developed since the elbow and shoulder motion is especially important for people to perform tasks in everyday life. In this paper, we present a mechanism of the moving center of rotation of the shoulder joint of the exoskeletal robot for human shoulder motion support in order to fit the center of rotation of the robot shoulder joint to that of the physiological human shoulder joint during the shoulder motion. The effectiveness of the proposed mechanism of the robot has been evaluated by experiment.

A novel concept for controlling of nonlinear systems using chaos and fuzzy model-based regulators is presented. In the control of such systems, we employ two phases, the first of which uses open-loop control forming a chaotic attractor or using chaotic inherent features in a system itself. Once the system states reach a predefined convex domain, open-loop control is cut off and a fuzzy model-based controller is employed under state feedback control in the second phase. The relaxed stability conditions and linear matrix inequalities (LMIs)-based design for a fuzzy regulator is introduced to construct a fuzzy attractive domain, in which a global solution is obtained so as to achieve the desired stability condition of the closed-loop system. The proposed controller architecture has been tested using three nonlinear systems: the Henon map, the Lorenz attractor, and a two-link manipulator. The simulation results show the effectiveness of the proposed controller.

We deal with exoskeletal robots for human (especially for physically weak people) motion support. In this paper, we propose a fuzzy neurocontrol method for a 1-DOF exoskeletal robot to support the human elbow motion. The proposed controller controls the angular position and impedance of the exoskeletal robot system based on vague biological signals that reflect the human subject's intention. Skin surface electromyogram (EMG) signals and the generated wrist force by the human subject during the elbow motion are used as input information of the controller. Due to the adaptation ability of the fuzzy neurocontrol, the robot is flexible enough to deal with vague biological signals such as the EMG. The experimental results show the effectiveness of the proposed controller.

The ring exercises have the characteristics that the apparatus can move in all directions freely unlike the horizontal bar exercises with the constraint between hands and the horizontal bar. If a robot that performs rings exercise is realized practically by clarifying the motion control method, an application to the coaching can be expected. Therefore, we propose a novel "rings gymnastic robot" in this paper. We acquire fuzzy rules to realize a series of exercises that is a handstand through a backward giant circle by the genetic algorithm, because the rules are considered to be useful for coaching. The effectiveness of the fuzzy controller is confirmed through a simulation using a derived dynamics of two-link rings gymnastic robot.

We have been developing exoskeletal robots in order to realize the human motion support (especially for physically weak people). We propose a 2 DOF exoskeletal robot to support the human shoulder motion. In this exoskeletal robot, the flexion-extension and abduction-adduction motions of the shoulder are supported by activating the arm holder of the robot, which is attached to the upper arm of the human subject, using wires driven by DC motors. A fuzzy controller has been designed to control the robot according to skin surface electromyogram (EMG) signals in which intention of the human subject is reflected. The proposed controller controls the flexion-extension and abduction-adduction motion of the human subject. The effectiveness of the proposed exoskeletal robot has been evaluated by experiment.

In order to help everyday life of physically weak people, we are developing exoskeletal robots for human (especially for physically weak people) motion support. In this paper, we propose a one degree-of-freedom (1 DOF) exoskeletal robot and its control system to support the human elbow motion. The proposed controller controls the angular position and impedance of the exoskeletal robot system based on biological signals that reflect the human subject's intention. The skin surface electromyogram (EMG) signals and the generated wrist force by the human subject during the elbow motion have been fused and used as input information of the controller. In order to make the robot flexible enough to deal with vague biological signal such as EMG, fuzzy neuro control has been applied to the controller. The experimental results show the effectiveness of the proposed exoskeletal robot system.

This paper presents an effective force control method in which multiple fuzzy-neuro force controllers are suitably and automatically combined with a proper rate in accordance with the unknown dynamics of an environment. The optimal combination rate of the fuzzy-neuro force controllers according to the environment dynamics is defined online by a neural network which is off-line trained with genetic algorithms. The effectiveness of the proposed method has been evaluated by computer simulation.

A fuzzy behavior-based system, which act as a planning system, is realized for the task control of a six-degree of freedom PUMA robot manipulator. A fuzzy behavior control system that was applied to a three-link manipulator by Dassanayake et al. (1999) is modified as a planning system for the PUMA robot. Fuzzy behavior elements are trained by a genetic algorithm. This has been conducted for three behavior groups namely objective behavior group, free behavior group and reactive behavior group. Simulation has been carried out for the PUMA robot to reach a target from a given point while avoiding an obstacle. Result shows that the fuzzy behavior based approach can be applied to plan the manipulator's joint angles and angular velocities to reach a particular point while avoiding obstacles.

We are stepping into an aging society rapidly. In that society, it is important that physically weak people are able to take care of themselves. In this paper, we introduce a 1DOF exoskeletal robot to support the elbow motion of physically weak people. Fuzzy control has been applied to control the exoskeletal robot system based on vague biological signals that reflect the human subject's intention. Skin surface electromyogram (EMG) signals and the generated force by the human subject's wrist during the human elbow motion have been used as input information of the fuzzy controller.

In this paper, an effective control policy for robot contact tasks with an unknown environment is proposed using fuzzy-neural techniques. In this control policy, a neural network is applied to classify the unknown environment based on its dynamic response and then fuzzy selector selects the suitable fuzzy neural force controllers. The selected fuzzy neural force controllers are able to realize the desired contact force precisely using their online adaptation ability. The fuzzy selection of controllers realize force control with the environment whose suitable fuzzy neural force controller is not prepared. The effectiveness of the proposed method is evaluated by experiment with a 2-DOF planar robot manipulator.

Position/force control is one of the most important and fundamental tasks of robot manipulators. Since the desired position and force required to perform certain tasks are usually designated in the operational space, the control force vector should be given to the end-effector in the operational space. However, friction of each joint of a robot manipulator impedes control accuracy. Therefore, friction should be effectively compensated for in order to realize precise control of robot manipulators. The fuzzy-neuro approach, a combination of fuzzy reasoning and neural networks, has been playing an important role in the control of robots. Applying the fuzzy-neuro approach, learning/adaptation ability and human knowledge can be incorporated into a robot controller. We propose an effective robot manipulator fuzzy-neuro position/force control method in which joint friction is effectively compensated for using adaptive friction models. The effectiveness of the proposed control method was evaluated by experiments.

Friction of each joint of a robot manipulator has to be effectively compensated for in order to realize precise position/force control of robot manipulators. Recently, soft computing techniques (fuzzy reasoning, neural networks, and genetic algorithms) have played an important role in the control of robots. By applying soft computing techniques, learning/adaptation ability and human knowledge can be incorporated into a robot controller. In this paper, we propose a two-stage adaptive robot manipulator position/force control method in which uncertain/unknown dynamics of the environment are compensated for in the task space and joint friction is effectively compensated for in the joint space using soft computing techniques. The effectiveness of the proposed control method was evaluated by experiments.

We describe a method for the path planning of an omnidirectional mobile manipulator by applying an evolutionary strategy. For a path using a B-spline, it is important how to choose some appropriate data points and the corresponding end points. The proposed approach automatically selects their points in a wide range, minimizing or maximizing the total cost function, which consists of several sub-cost functions such as motion smoothness, movable range of joint, singular orientation and falling down. The simulation result shows that our method is effective for the path planning of a robot, which has many boundary conditions and complicated evaluation such as the mobile manipulator considered.

Robot manipulators are expected to perform more sophisticated tasks under unlimited environment. In order to realize these tasks, the robot manipulators have to be flexible enough to work in an unknown environment. In this paper, we propose an effective adaptive neural network feedback controller for hybrid position/force control of robot manipulators for an unknown environment by applying new types of neurons which possess visco-elastic properties. The unexpected overshooting and oscillation caused by the unknown and/or unmodeled dynamics of a robot manipulator and an environment can be decreased efficiently by the proposed visco-elastic neurons. The effectiveness of the proposed visco-elastic neural network controllers is evaluated by simulation with the model of a 3-DOF direct-drive planar robot manipulator.

Position and force controls are important and fundamental tasks of robot manipulators. In order to control position of the robot which simultaneously applies force to the environment, the friction between the robot and the environment has to be compensated. However, the friction force varies according to the applied force to the environment. Therefore, it is difficult to compensate the friction effectively with conventional controllers if we do not know the friction coefficient. Many researches have been done on fuzzy neural control, the combination of neural networks and fuzzy control, in order to make up for each other's weak points. The fuzzy neural control is expected to perform more sophisticated control than conventional control in an unknown environment. In this paper, we propose a new friction compensation method using the fuzzy neural network which contains a specialized neuron for friction compensation and a switch-learning. Simulation has done using a 3DOF planar robot manipulator to confirm the effectiveness of the proposed method.

A lot of researches have been done on fuzzy-neural control, the combination of neural networks control which has a learning ability from experiments and fuzzy control which has an ability of dealing with human knowledge, in order to make up for each other's weak points. In this paper, fuzzy-neural controller is introduced for robot manipulator contact force control to an unknown environment. A robot manipulator controller, which approaches, contacts and applies force to the environment, is designed using fuzzy logic in order to realize human like control and then modeled as a neural network to adjust membership functions and rules in order to achieve desired contact force control. Error between desired force and measured force and momentum of robot manipulator are used as input signals of the controller. Simulation has done using a 3DOF planar robot manipulator to confirm the effectiveness of the controller for the tasks of approach, contact and force control.