Odors induce various emotions, which in turn evoke responses in the autonomic nervous system. Therefore, attempts have been made to estimate the subjective evaluation of odors using autonomic activity. This study focuses on peripheral arterial stiffness, which is innervated by the vasoconstrictor activity of the skin sympathetic nerves, and aims to evaluate its relationship with subjective evaluation of unpleasant odors. To this end, an experiment was conducted to measure physiological signals and subjective evaluations by presenting participants with odorless air, and low and high concentrations of heptanal (N = 20). We then calculate the low- to high-frequency ratio of the heart rate variability and heart rate as indices of cardiac autonomic activity and the mean and variance of peripheral arterial stiffness as the vasoconstrictor activity of the skin sympathetic nerve indices. Analysis using a mixed linear model found a significant fixed effect of a positive slope (p < 0.01) for the variance of the peripheral arterial stiffness relative to the first principal component of the subjective evaluation interpreted as an unpleasant and high-arousal state. Previously, we reported that the maximum mean peripheral arterial stiffness during the stimulus interval correlates with subjective ratings of pleasantness and unpleasantness. The findings of this study suggest that peripheral arterial stiffness can be a predictor for the subjective evaluation of an odor.

Odor is difficult t o v erbalize and its sensation varies widely between individuals. Therefore, several attempts have been made to evaluate the subjective perception of odor objectively and quantitatively via autonomic activity. This study focuses on peripheral arterial stiffness, which is innervated by the vasoconstriction of skin sympathetic nerves, and aims to evaluate its relationship with the subjective evaluation of pleasant odor. Accordingly, an experiment was conducted to measure the physiological signals and subjective evaluation of odor by presenting participants with odorless air, and low and high concentrations of $\gamma -$Undecalactone $(N \quad =10)$. Their cardiac autonomic responses were evaluated using the mean low to high-frequency ratio of heart rate (HR) variability and heart rate. In addition, skin sympathetic responses were evaluated using the mean of the peripheral arterial stiffness $\beta$ of the earlobe and fingertip. Analysis using a mixed linear model demonstrated a significant fixed effect via a positive slope $(p \lt 0. 01)$ fo r HR, earlobe arterial stiffness, and fingertip arterial stiffness relative to the first principal component of subjective evaluation, thereby interpreted as a pleasant and high-arousal state. The findings of this study suggest that HR, earlobe arterial stiffness, and fingertip arterial stiffness can be predictors for the subjective evaluation of $\gamma -$Undecalactone odor.

Zebrafish is a model organism widely used in behavioral neurology and drug discovery to elucidate the neurobehavioral mechanisms of emotion. Traditionally, its emotional states were assessed from motion patterns, and respiratory information was neglected despite its reported importance. This paper presents a system that enables simultaneous motion and respiration measurement using a bioelectric signal, termed ventilatory signal. The proposed system measures ventilatory signals from an electrode array and applies a spatial distribution model to locate zebrafish and the instantaneous amplitude of the signal. The experimental results confirmed that position estimation was possible with an error of approximately 1/5 of the body length. Simultaneously, the system could detect an increase in respiration frequency caused by a 100 mg/L concentration of caffeine dose.

In this study, we evaluated the effects of different object parameters on fingertip deformation by examining the contact area deformation that occurs when a finger is pushed against a flat plate with a film. An experiment was conducted under various object parameters and forced displacements. First, the eccentricity, which is an index of the contact area deformation of a fingertip, was measured for different objects. As a result, it was confirmed that the eccentricity depends on the object characteristics. Next, the results of the tactile texture evaluation were modeled through a multiple regression analysis using the contact area deformation and object parameters. The relationship between the eccentricity and tactile texture evaluation was then investigated using the coefficients of determination in a multiple regression analysis. The results showed that the eccentricity can be used to evaluate a property differently from the material parameter with respect to the expression of feeling. A prediction of the tactile texture items was conducted using the eccentricity and object parameters. It was shown that it is possible to estimate the sensitivity with a correlation coefficient of 0.8 or more for smooth, granular, sticky, dry, and slippery textures.

Studies suggest that the level of muscle activation may influence force reproduction. The purpose of this study was to develop a sensorimotor-enhancing suit (SEnS)-unpowered assistive clothing without actuators or electrical devices-and to examine its efficacy for reducing voluntary muscle activation and improving force reproduction in the upper limb in healthy young adults. The SEnS was made of elastic fabric and designed to produce assistive shoulder flexion moment that can partially mitigate the required activation of the user's shoulder flexor muscles. A series of human experiments were then conducted in healthy young adults. As a proof of concept, reduction in force reproduction performance of the shoulder moment was confirmed with mitigation of the shoulder moment by using an external string. To examine the efficacy of the SEnS, voluntary muscle activation and force reproduction performance was examined with and without using the SEnS, by comparing the amount of activation in the shoulder flexor muscles and the error in force reproduction in the upper limb. The results showed that wearing SEnS reduced muscle activation and force reproduction error. These results suggest that the accuracy of force reproduction can be improved substantially by partially mitigating the shoulder moment and associated voluntary muscle activation, using the SEnS.

Human's skeletal muscles have sensory systems. Human-machine interfaces that consider muscle effort should improve the usability and manipulability of daily products. This paper presents an interface design method that considers muscle effort and illustrates possible applications. A three-dimensional musculoskeletal model is developed based on the upper extremity model with 29 muscles and 16 joints. Muscle activity is estimated by using the OpenSim. We define the muscle effort as the mean of each muscle activity, and the muscle effort is used to determine the physical design or the physical response of the human interface. We applied the proposed physical design modification method to the design of the key height of a keyboard. The key height is changed proportional to the normalized muscle effort. The estimated muscle effort suggests that users can type every keys of the modified keyboard with a similar muscle effort. We also explore to improve the manipulation feeling of the key touching of mobile devices by changing the feedback response according to the estimated muscle effort when users touch keys. The experimental results indicate that the manipulation feeling can be improved by changing the feedback response according to the muscle effort.

This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.

This paper presents the experimental data of human mechanical impedance properties (HMIPs) of the arms measured in steering operations according to the angle of a steering wheel (limbs posture) and the steering torque (muscle cocontraction). The HMIP data show that human stiffness/viscosity has the minimum/maximum value at the neutral angle of the steering wheel in relax (standard condition) and increases/decreases for the amplitude of the steering angle and the torque, and that the stability of the arms' motion in handling the steering wheel becomes high around the standard condition. Next, a novel methodology for designing an adaptive steering control system based on the HMIPs of the arms is proposed, and the effectiveness was then demonstrated via a set of double-lane-change tests, with several subjects using the originally developed stationary driving simulator and the 4-DOF driving simulator with a movable cockpit.

The paper discusses the arterial stiffness during the flow-mediated dilation (FMD) test The FMD test is a method of evaluating the vascular endothelial function and has been popular as it is non-invasive and readily performed by a skillful ultrasound technician. The FMD test, however, evaluates only the maximal increase in vascular diameter mediated by the increases in blood flow after the release of the occlusive cuff and does not evaluate the arterial viscoelastic properties. This paper thus estimates the log-linearlized stiffness, to evaluate the arterial stiffness properties using the arterial diameter and blood pressure measured in a beat-to-beat manner during the FMD test. To six healthy volunteers, we performed the FMD test to measure the arterial diameter and blood pressure with ultrasound diagnostic imaging equipment and non-invasive continuous arterial blood pressure monitor, respectively. As a result, the maximal vasodilatation ratio of FMD (FMD) was obtained after cuff occlusion. In comparison with the arterial stiffness before the FMD test, the stiffness of the arterial wall is temporarily decrease and increase. It was concluded the the arterial stiffness can be estimated on a beat-to-beat basis during the FMD test.

The present study clarifies the human characteristics of motion and perception, and develops a force perception model in vehicle operation. As the first step, this paper analyzes experimentally human force perception properties during steering operation, in which multiple joint motions and large postural changes of arms are necessary. The experimental results reveal the following three points: a) force perception follows the Weber-Fechner law under the constant arm posture, b) force perception changes depending on the steering wheel angle and is affected by arm weight and force perception properties, and c) the same tendency of force perception properties is observed even if the steering direction changes. Finally, a force perception model in sending a steering wheel is proposed.

The present paper proposes an evaluation method of operation loads in vehicular driving, such as the joint-load, the seat pressure, and the perceiving force, based on biomechanical and psychophysical evidences to assist the human-centered design of driving interfaces. The prototype simulator is developed by means of a big experimental data of human motor properties and force-perception properties related with driving operations. The usefulness of the proposed methodology is then demonstrated through a set of simulation experiments in the case of the curve traveling situation.

A neural oscillator with a double-chain structure is one of the central pattern generator models used to simulate and understand rhythmic movements in living organisms. However, it is difficult to reproduce desired rhythmic signals by tuning an enormous number of parameters of neural oscillators. In this study, we propose an automatic tuning method consisting of two parts. The first involves tuning rules for both the time constants and the amplitude of the oscillatory outputs based on theoretical analyses of the relationship between parameters and outputs of the neural oscillators. The second involves an evolutionary tuning method with a two-step genetic algorithm (GA), consisting of a global GA and a local GA, for tuning parameters such as neural connection weights that have no exact tuning rule. Using numerical experiments, we confirmed that the proposed tuning method could successfully tune all parameters and generate sinusoidal waves. The tuning performance of the proposed method was less affected by factors such as the number of excitatory oscillators or the desired outputs. Furthermore, the proposed method was applied to the parameter-tuning problem of some types of artificial and biological wave reproduction and yielded optimal parameter values that generated complex rhythmic signals in Caenorhabditis elegans without trial and error.

This paper presents a novel approach to increase the amount of visual stimuli in sensor measurements using saliency maps. A saliency map is a combination of normalized feature maps in different channels (i.e. color, intensity) to represent the relative strength of visual stimuli in an image. The total saliency is higher when the camera is looking at a scene with more interesting things in the field of view and vise versa. We employ methods of extremum seeking control to find a camera position that corresponds to local maximum saliency value. We combine the global properties of simplex optimization methods with the local search properties and dynamic response of extremum seeking control to create a novel algorithm that is more likely to find a global maximum than conventional extremum seeking control. Simulations and experiments are presented to show the strength of this approach.

Systems capable of monitoring the biological condition of a driver and issuing warnings during instances of drowsiness have recently been studied. Moreover, many researchers have reported that biological signals, such as brain waves, pulsation waves, and heart rate, are different between people who have and have not consumed alcohol. Currently, we are developing a noninvasive system to detect individuals driving under the influence of alcohol by measuring biological signals. We used the frequency time series analysis to attempt to distinguish between normal and intoxicated states of a person as the basis of the sensing system.

This paper proposes an active-steering control method that uses human hand impedance properties. In this method, the dynamic properties of a steering device manipulated by the upper limbs are automatically regulated according to the damping coefficient of the human-steering system. Human hand impedance in steering operations is measured and modeled depending on the steering angle and torque for use in the proposed control structure. The effectiveness of the proposed method was demonstrated by performing an emergency avoidance task using a stationary driving simulator.

In the field of molecular biology, extending now to the more comprehensive area of systems biology, the development of computer models for synthetic cell simulation has accelerated extensively and has begun to be used for various purposes, such as biochemical analysis. These models, describing the highly efficient environmental searching mechanisms and adaptability of living organisms, can be used as machine-control algorithms in the field of systems engineering. To realize this biomimetic intelligent control, we require a stripped-down model that expresses a series of information-processing tasks from stimulation input to movement. Here we selected the bacterium Escherichia coli as a target organism because it has a relatively simple molecular and organizational structure, which can be characterized using biochemical and genetic analyses. We particularly focused on a motility response known as chemotaxis and developed a computer model that includes not only intracellular information processing but also motor control. After confirming the effectiveness and validity of the proposed model by a series of computer simulations, we applied it to a mobile robot control problem. This is probably the first study showing that a bacterial model can be used as an autonomous control algorithm. Our results suggest that many excellent models proposed thus far for biochemical purposes can be applied to problems in other fields.

This paper proposes a control method for a human-assisting manipulator using acceleration sensors. The technique involves an arm control part (ACP) and a hand-and-wrist control part (HWCP); the ACP controls the manipulator's shoulder and elbow joints using acceleration signals, while the HWCP controls the corresponding joints using mechanomyogram (MMG) signals measured from the human operator. A distinctive feature of the proposed method is its estimation of information on force and motion from measured acceleration signals using MMG processing and a probabilistic neural network. Experiments demonstrated that the MMG patterns seen during hand and wrist motion can be classified sufficiently (average rate: 94.3%), and that a prosthetic manipulator can be controlled using the acceleration signals measured. Such manipulators are expected to prove useful as assistive devices for people with physical disabilities.

A human can maneuver mechanical systems by adjusting his/her own body naturally and effectively according to a target task by utilizing the kinematical and dynamic characteristics of operating systems acquired through sensory organs. If such human sensory and motor characteristics changing in the task can be quantitatively described, it would be useful to design and develop a novel human-machine system so that humans can manipulate a machine more instinctively and comfortably. This paper investigates the interaction between human sensory and motor properties at the foot during the operation of an automobile pedal, as an example of human-machine systems, and demonstrates the close relationship between the perceptual properties of force resistance at the foot and the loads for foot joints much depending on the pedal layout. Finally, based on biomechanical and perceptual analyses, a human-inspired design method of pedal dynamic properties is discussed.

In a human-robot interface, the prediction of motion, which is based on context information of a task, has the potential to improve the robustness and reliability of motion classification to control prosthetic devices or human-assisting manipulators. This paper proposes a task model using a Bayesian network (BN) for motion prediction. Given information of the previous motion, this task model is able to predict occurrence probabilities of the motions concerned in the task. Furthermore, a hybrid motion classification framework has been developed based on the BN motion prediction. Besides the motion prediction, electromyogram (EMG) signals are simultaneously classified by a probabilistic neural network (NN). Then, the motion occurrence probabilities are combined with the NN classifier's outputs to generate motion commands for control. With the proposed motion classification framework, it is expected that classification performance can be enhanced so that motion commands can be more robust and reliable. Experiments have been conducted with four subjects to demonstrate the feasibility of the proposed methods. In these experiments, forearm motions are classified with EMG signals considering a cooking task. Finally, robot manipulation experiments were carried out to verify the proposed human interface system with a task of taking meal. The experimental results indicate that the proposed methods improved the robustness and stability of motion classification.

This paper proposes a piercing based grasping by using the self-tightening effect of objects with elasticity. We suppose a piercing hand with palm where the piercing motion by needles can be independently achieved irrespective of the palm motion. The palm first approaches and touches with the object, with a slight pushing motion. This motion produces an increase of potential energy of object. After the motion, the needles pierce the object. When the object is lifted up, the accumulated potential energy is released and the object tries to recover the original shape under the piercing condition. We found a particular mechanical configuration between the object and the needle, under which the constraint of object is tightened due to the object deformation during the release of the potential energy. In order to confirm the robustness of the proposed method, we have done a couple of experiments. The results show that the proposed method keeps an extremely high robustness compared with the other piercing methods.

This paper proposes a novel human interface that can be used to operate domestic appliances and game machines. In this system, finger tapping movement is measured by magnetic sensors, and is evaluated by computing five features, such as the finger tapping interval, on a real-time basis. These factors are then discriminated through a probabilistic neural network and allocated as machine operation commands. The user can thus voluntarily operate various machines using finger tapping movements. Experimental results showed that the prototype system developed can learn and classify user movements with a high degree of accuracy (average rates: 98.56plusmn1.15 [%]), and that it can be used to smoothly control appliances and game machines. The possibility of the system supporting finger movement training was also confirmed with three subjects through game operation.

This paper proposes a novel phoneme classification method using facial electromyography (EMG) signals. This method makes use of differential EMG signals between muscles for phoneme classification, which enables a speech synthesizer to be constructed using fewer electrodes. The EMG signal is derived as a differential between monopolar electrodes attached to two different muscles, unlike conventional methods in which the EMG signal is derived as a differential between bipolar electrodes attached to the same muscle. Frequency-based feature patterns are then extracted using a filter bank, and the phonemes are classified using a probabilistic neural network, called a reduced-dimensional log-linearized Gaussian mixture network (RD-LLGMN). Since RD-LLGMN merges feature extraction and pattern classification processes into a single network structure, a lower-dimensional feature set that is consistent with classification purposes can be extracted; consequently, classification performance can be improved. Experimental results indicate that the proposed method with a fewer number of electrodes can achieve a considerably high classification accuracy.

This paper discusses an optimum design approach for robotic hands by considering the characteristics of visco-elasticity of food. "Norimaki-sushi" is taken as an example for food. We first show that the dynamic characteristics of such food can be expressed by utilizing the Maxwell model with two layers. Based on dynamic parameters obtained by experiments, we show the relationship among the total working time, the plastic deformation of food after the grasping motion, the hand stiffness, and the operating velocity of the hand. We newly found an interesting behavior of food that allows to find an optimum set of the design parameters for achieving the minimum plastic deformation of food.

A human can control dynamic properties of his/her own body naturally and effectively according to tasks by utilizing the perceived information of environmental characteristics. If dynamic properties of human movements depending on environmental characteristics can be described quantitatively, there would be expected to design and develop a novel human-machine system in which an operator can manipulate more comfortably. This paper discusses a design methodology of human-machine systems integrating human motor characteristics. A vehicle interface system manipulated by the foot is focused on, and mechanical impedance properties of human lower extremities during maintained leg posture are investigated according to the leg posture and the foot force

In order to survive in complex natural environments, living organisms have been genetically acquiring various algorithms. Paramecium, for example, exhibits an avoiding reaction when it senses repellent chemicals on the anterior part of the cell. Also, on sensing attractants, it accelerates its swimming velocity and remains in the area. Such a chemotactic response is called chemotaxis. In this paper, we proposes the comuputer model of Paramecium based on biological knowledge. And, we report the simulation experiments that the computer model can reproduce the characteristics of the actual organism.

This paper presents an analysis tool of kicking motion in soccer based on robotic techniques. The developed tool can calculate kinematics, dynamics, and manipulability of trainee's movements with considerations of human physical characteristics from the trainee's postures measured by a motion capture system, and can visually provide the quantitative analysis to users in the main window of the tool. The effectiveness of the proposed approach is verified through basic experiments with skilled and unskilled subjects in soccer.

This paper focuses on a shift operation system of an automobile as an example of human-machine systems, and develops a virtual shift system using a variable-impedance controlled robot that can provide various types of shift dynamics. A series of operational experiments is carried out with different viscoelastic patterns installed in the virtual shift system by changing the relative position between the shift lever and the operator. Then, the relationship between operational feeling and human arm movements is investigated by using the force manipulability considering human joint-torque characteristics.

Human-robotic systems that include interaction between human operators and robots should be designed with careful consideration for the dynamic property and control ability of a human operator. This paper performs manual tracking control tests on a human-robotic system using an impedance-controlled robot, and investigates control characteristics of a human operator according to the robot impedance properties. Experimental results demonstrate that humans try to maintain dynamic properties of an overall system as constant as possible by adjusting their own impedance properties. Then, a new training system using a neural network for operating a human-robotic system is constructed on the basis of the experimental findings in the human tracking control properties.

This paper proposes a virtual EMG-prosthetic hand system using a neural network available in an fMRI room. A subject attaches surface electrodes on his forearm, and manipulates the virtual EMG-prosthetic hand visualized with 3D computer graphics by using visual biofeedback projected on a screen in the scan room. The preliminary experiments demonstrate that the intended hand motion by the subject can be determined with high discrimination rates over 96% without decaying fMRI images. Finally, in order to show the validity of the proposed system, we report the neurological experiments that were carried out to analyze human brain functions.

Impedance control is one of the most effective methods for controlling the interaction between a manipulator and a task environment. In conventional impedance control methods, however, the manipulator cannot be controlled until the end-effector contacts task environments. A noncontact impedance control method has been proposed to resolve such a problem. This method on only can regulate the end-point impedance, but also the virtual impedance that works between the manipulator and the environment by using visual information. This paper proposes a learning method using neural networks to regulate the virtual impedance parameters according to a given task. The validity of the proposed method was verified through computer simulations and experiments with a multijoint robotic manipulator.

This paper proposes a new endoscope system with an air injection based contact probe capable of measuring the local impedance of the inner surface of stomach. By utilizing air pressure, we can produce an equivalent contact force, which is really advantageous for avoiding any damage for stomach during an examination. A big issue is that there is no way to obtain the applied force to stomach exactly. The key for solving it is the specially designed solenoid valve with the response for more than 500 Hz. The solenoid valve with such a high response allows us to estimate each impedance parameter, even with a reference value. We show experimental system with a couple of interesting results.

Probabilistic neural networks (PNNs) that incorporate the Bayesian decision rule and statistical models have been widely used for pattern classification. Efficient estimation of the PNN's weights, however, is still a major problem. In this paper, we propose a new training scheme based on a discriminative criterion, maximum mutual information (MMI), and apply this method to the log-linearized Gaussian mixture network (LLGMN) which is one of the PNNs. The MMI training achieves a consistent estimator of network weights, and includes the conventional maximum likelihood (ML) algorithm as a special case. Also, the dynamics of terminal attractor (TA) is introduced for iteration control of the MMI training. Finally, the classification ability of the proposed method is examined with a pattern classification problem of the electromyogram (EMG) signals, and found that the MMI training results in better classification than the conventional ML algorithm.

This paper proposes a new EMG-controlled pointing device using a novel statistical neural network. This device can be used as an interface tool for wearable computers since it does not restrict the operator to being in front of computer devices such as a keyboard or a mouse. The distinctive feature of this device is that we adopt a statistical neural network, which includes a continuous density hidden Markov model, to model the relationship between EMG signals and directions of a pointer movement. The operator can move a pointer in any direction throughout 360 degrees. We also introduced a physical model, such as a mass in a viscous space, into our system to realize a smooth pointer movement corresponding to the operator's force sense. In the experiments, omnidirectional pointer control is achieved using the proposed method and the applicability of our method is confirmed.

Context in time series is one of the most useful and interesting characteristics for machine learning. In some cases, the dynamic characteristic would be the only basis for achieving a possible classification. A novel neural network, which is named "a recurrent log-linearized Gaussian mixture network (R-LLGMN)," is proposed in this paper for classification of time series. The structure of this network is based on a hidden Markov model (HMM), which has been well developed in the area of speech recognition. R-LLGMN can as well be interpreted as an extension of a probabilistic neural network using a log-linearized Gaussian mixture model, in which recurrent connections have been incorporated to make temporal information in use. Some simulation experiments are carried out to compare R-LLGMN with the traditional estimator of HMM as classifiers, and finally, pattern classification experiments for EEG signals are conducted. It is indicated from these experiments that R-LLGMN can successfully classify not only artificial data but real biological data such as EEG signals.

This paper proposes a human-assisting manipulator teleoperated by electromyographic (EMG) signals and arm motions. The proposed method can realize a new master-slave manipulator system that uses no mechanical master controller. A person whose forearm has been amputated can use this manipulator as a personal assistant for desktop work. The control system consists of a hand and wrist control part and an arm control part. The hand and wrist control part selects an active joint in the manipulator's end-effector and controls it based on EMG pattern discrimination. The arm control part measures the position of the operator's wrist joint or the amputated part using a three-dimensional position sensor, and the joint angles of the manipulator's arm, except for the end-effector part, are controlled according to this position, which, in turn, corresponds to the position of the manipulator's joint. These control parts enable the operator to control the manipulator intuitively. The distinctive feature of our system is to use a novel statistical neural network for EMG pattern discrimination. The system can adapt itself to changes of the EMG patterns according to the differences among individuals, different locations of the electrodes, and time variation caused by fatigue or sweat. Our experiments have shown that the developed system could learn and estimate the operator's intended motions with a high degree of accuracy using the EMG signals, and that the manipulator could be controlled smoothly. We also confirmed that our system could assist the amputee in performing desktop work.

This paper newly proposes dynamic friction closure, under which a moving object is completely stopped within a predetermined length through the work due to Coulomb friction between an object and a gripper. There are two conditions for achieving it: The geometrical condition, ensuring the robot motion to meet with an object at an appropriate catching point, and frictional stopping capability, ensuring the moving distance of object is less than the predetermined distance under a limited grasping force. Through the simple analysis on catching a 2D circular object by a parallel jaw gripper, we newly found that even under the same total energy of object, the distance changes depending upon the ratio between initial rotational and translational velocity. For a 3D sphere object, the maximum moving distance is provided with the initial kinetic energy.

This paper discusses the design of a capturing system with an extremely high response. The key for achieving a quick response is the arm/gripper coupling mechanism, where the potential energy initially accumulated in the arm is transferred to the kinetic energy of the arm and, continuously, to the kinetic energy for closing the gripper at the capturing point without any time lag. The experimental results show the maximum acceleration of 91G corresponding to almost two times higher than the conventional world record, and the total capturing time of 0.03 sec. Experiments on capturing a ball are also shown.

Electromyogram (EMG) has often been used as a control signal for a prosthetic arm, which includes information on the operator's motor intentions and the mechanical impedance of joints. Most previous research adopted the control methods of the prosthetic arms based on the EMG pattern discrimination and/or the force estimation from the EMG signals, and did not utilize any knowledge on tasks performed by amputees such as object grasping and soup-spooning tasks. In this paper, a new myoelectric control method is proposed using a statistically organized neural network and an event-driven task model. The task model is represented using a Petri net to describe the task dependent knowledge, which is used to modify the neural network's output. Experimental results show that the use of the task model significantly improves the accuracy of the EMG pattern discrimination.

This paper discusses a sufficient condition for the manipulation of the Envelope Family, where multiple contacts are allowed between object and chains (or the environment). All chains are assigned to either the position controlled chain (P-chain) or the torque controlled chain (T-chain). Although the object motion under multiple contacts can not be uniquely specified only by the T-chains, we are able to provide a sufficient condition ensuring that a given set of torque commands for the T-chains always moves the object within the designated direction along the surface of a P-chain (or a fixed environment). Experiments as well as simulations are shown to verify this basic idea.

In this paper, a bio-mimetic trajectory of robots for manipulating a nonholonomic car is generated with a time base generator (TBG). In order to reveal what kind of trajectories the robots should generate for the given task, experiments with human subjects were performed. It has been shown that a human generates the trajectory with a single- or double-peaked velocity profile according to the geometrical conditions of the car. Then, bio-mimetic trajectories were generated by modeling the observed primitive profiles with the TBG and also compared with the human trajectories.

This paper proposes a new trajectory generation method that allows full control of transient behavior, namely, time-to-target and velocity profile, based on the artificial potential field approach for a real-time robot motion planning problem. Little attention, in fact, has been paid to the temporal aspects of this class of path planning methods. The ability to control the motion time to the target as well as the velocity profile of the generated trajectories, however, is of great interest in real-life applications. In the paper, we argue that such transient behavior should be taken into account within the framework of the artificial potential field approach.

This paper proposes the tactile differentiator that can enhance the edge of polygonal surface, through tracing motion. It is composed of a flexible beam anchored at the base with a moment sensor, and an actuator for moving the whole system. When the beam tip passes over the edge where two different planes are intersected with an arbitrary angle, the tactile differentiator provides a step change of output, as if it were just like a differentiator. The edge enhancement factor of sensor is introduced by the shape of beam, the intersection angle of environment, and the contact friction. By using the factor, we discuss the design of the sensor. Experimental results are also shown to verify the basic idea.

Through group experiments by human, we found an interesting grasping pattern, where the human can easily captures a small object placed on a table by changing the finger posture from upright to curved ones after each finger making contact with the object. A series of this motion is called as detaching assist motion (DAM). This paper discusses a condition for achieving DAM by taking the idea of self-posture changing motion. A sufficient condition for lifting up an object from the table is discussed. Some experiments are also shown to verify the condition.

In this paper, a new training method for robotic rehabilitation aids is proposed, in which only one neural network (NN) is simultaneously used in order to identify the dynamic properties of a human-robot system and give an assist to the trainee. The model used for identification of the dynamics of the human-robot system consists of the NN and a reference model which represents the main characteristic of a skilful operator. This paper explains the working principle of the training method and shows the validity of the proposed method through experiments by non-skilled operators.

Discusses the manipulation of multiple objects under rolling contacts. For manipulating multiple objects, there are two key issues which do not arise in the manipulation, of a single object, (1) each object's motion is restricted by the other objects, and (2) the contact force among objects is not controlled directly. As for (1), we first formulate the motion constraint for the whole grasp system, and then provide a necessary condition for manipulating multiple objects uniquely. As for (2), we provide a condition for determining the contact forces among objects uniquely. We further show a sufficient condition for manipulating multiple objects within the object motion constraint. Under this sufficient condition, we propose a control scheme for object motion by taking the motion constraint into account. An experimental result is provided to confirm our idea.

This paper discusses a sufficient condition for manipulation of envelope family, where multiple contacts are allowed between object and chains (or environment). All chains are assigned by either position controlled chain (P-chain) or torque controlled chain (T-chain). While the object motion under multiple contacts can not be uniquely specified by T-chains only, we show a sufficient condition ensuring that a given set of torque commands for T-chains always move the object toward the goal along the surface of P-chain (or a fixed environment). Experiments as well as simulations are also shown to verify the basic idea.

Discusses a hugging walk where multiple contacts are allowed between each leg and environment during locomotion. Since an external force can be supported by multiple contact points, legged robots in this style can be expected to be even more robust against a disturbance (including gravitational force) than those in conventional gaits based on foot contact. The alternative two-legs-support gait which never exists for conventional gaits based on static balance is introduced. Two indices for stable locomotion are introduced. Experiments are also shown to confirm the basic motion of the proposed gait.

This paper discusses a whisker sensor composed of a flexible beam anchored at the base with torque sensor, and an actuator for moving it. We consider a sufficient condition for detecting the surface shape by using a whisker sensor. We show that a straight-lined whisker sensor with one axis torque sensor can achieve the requirement irrespective of contact friction, while a curved whisker can not do it. We experimentally show that a straight-lined whisker sensor can detect the surface irregularity with pretty high sensitivity (/spl les/5 /spl mu/m), even under different contact frictions.

This paper proposes a new control method of an externally powered prosthetic hand based on a skeletal muscle model. This method consists of two steps: in the first step, a joint of a prosthetic hand to be driven is selected based on the EMG pattern discrimination using a neural network. Then, muscular contraction levels of flexors and extensors are estimated using other neural networks and used for an impedance control of the prosthetic hand in the second step. In the experiments, natural feeling of the prosthetic control similar to that of a human arm is realized using the neural networks and the impedance control.

This paper discusses the manipulation of multiple objects under rolling contacts. For manipulating multiple objects, the following two key issues do not arise in the manipulation of a single object: 1) each object's motion is restricted by the other objects; and 2) the contact force among objects is not controlled directly. As for (1), we first formulate the motion constraint for the whole grasp system, and then provide a necessary condition for manipulating multiple objects uniquely. As for (2), we provide a condition for determining the contact forces among objects uniquely. We further show a sufficient condition for manipulating multiple objects within the object motion constraint. Under this sufficient condition, we propose a control scheme for object motion by taking the motion constraint into account. Simulation and experimental results are provided to confirm our idea.

The paper discusses the scale-dependent grasp. Suppose that a human approaches an object initially placed on a table and finally achieves an enveloping grasp. Under such initial and final conditions, he (or she) unconsciously changes the grasp strategy according to the size of objects, even though they have similar geometry. We call the grasp planning the scale-dependent grasp. We find that grasp patterns are also changed according to the surface friction and the geometry of cross section in addition to the scale of object. Focusing on column objects, we first classify the grasp patterns and extract the essential motions so that we can construct grasp strategies applicable to multifingered robot hands. The grasp strategies constructed for robot hands are verified by experiments. We also consider how a robot hand can recognize the failure mode and how it can switch from one to another.

Discusses an EMG based control method of a robotic manipulator as an adaptive human supporting system, which consists of an arm control part, a hand and wrist control part and a graphical feedback display. The arm control part controls joint angles of the arm according to the position of the operator's wrist joint measured by a 3D position sensor. The hand and wrist control part selects an active joint out of four joint degrees of freedom and controls if using an impedance model based on the EMG signals. A distinctive feature of our method is to use a statistical neural network for EMG pattern discrimination. This network can adapt to changes of the EMG patterns according to differences among individuals different locations of the electrodes, time variation caused by fatigue or sweat, and so on. It is shown from the experiments that the hand and wrist motions can be controlled based on the EMG signals sufficiently. It may be useful as an assistive device for a handicapped person.

The human-like trajectory of robots for manipulating a nonholonomic car is generated. In order to reveal what kind of trajectories the robots should generate on the task, experiments with subjects were performed. It is shown that a human generates the trajectories with a single-peaked and a double-peaked velocity profile. By modeling these primitive profiles with a time base generator (TBG), the human-like trajectory for the robots is generated with the TBG based trajectory generation method. Finally, simulation results are shown and compared with the trajectories.

This paper proposes a training system based on EMG signals for prosthetic control and the development of its prototype. This system aims to enhance three kinds of control ability: muscular contraction, cooperation among several muscles, and the timing of EMG generation. For EMG signal processing a statistical neural network is used which can adapt itself to changing EMG patterns according to the differences among individuals, the different locations of the electrodes, the time variation caused by fatigue or sweat, and so on. During training, EMG signal information is displayed in the feedback monitor. The experiments have been conducted using the prototype system. The subject is a 51 year old man who had his forearm amputated when he was 18 years old. After training for five days, the ability to manipulate the EMG signal of the subject has been enhanced and the effectiveness of this system is shown.

Proposes a new probabilistic neural network (NN) that can estimate the a-posteriori probability for a pattern classification problem. The structure of the proposed network is based on a statistical model composed by a mixture of log-linearized Gaussian components. However, the forward calculation and the backward learning rule can be defined in the same manner as the error backpropagation NN. In this paper, the proposed network is applied to the electroencephalogram (EEG) pattern classification problem. In the experiments described, two types of a photic stimulation, which are caused by eye opening/closing and artificial light, are used to collect the data to be classified. It is shown that the EEG signals can be classified successfully and that the classification rates change depending on the amount of training data and the dimension of the feature vectors.

This paper proposes an impedance control method for redundant manipulators, which can control not only the end-point impedance using one of the conventional impedance control methods, but the joint impedance which has no effects on the end-point impedance. First, a sufficient condition for the joint impedance controller is derived. Then, the optimal controller for a given desired joint impedance is designed using the least squares method. Finally, computer simulations and experiments using a planar direct-drive robot are performed in order to confirm the validity of the proposed method.

Proposes an impedance control method that can regulate a virtual impedance between a robot manipulator and external objects using visual information. The conventional impedance control method is not useful in some cases where no interaction force between the arm and its environment exists, although it is one of the most effective control methods for manipulators in contact with the environment. Using the proposed method, we can control the manipulator motion based on the virtual impedance before contact with the objects. The validity of the proposed method is verified through computer simulations and experiments using a direct-drive robot.

This paper proposes the concept of a human-robot interface as rehabilitation aid and develops the prototype system. The prototype system aims to be used as a controller for the robotic manipulator and as rehabilitation system for the handicapped person. In order to adapt the system to the characteristics of the operator's electromyogram (EMG) signal, the EMG pattern discrimination method using the neural network is utilized as an essential technique of our system. In the experiments, it can be seen that the robotic manipulator can be controlled with high accuracy using the operator's EMG signal, and that the adaptive learning of the neural network improves the discrimination ability of the EMG signal. The rehabilitation program and biofeedback are also discussed.

This paper discusses the transition stability in sliding an object enveloped by a multi-fingered robot hand whose joints are under constant torque command. We provide a new concept on transition stability, where a transition is called stable if the object is guaranteed to reach the goal section without moving away from a virtual cylinder defined in hand working space. To evaluate the transition stability, we introduce the force-flow-diagram enabling us to confirm whether the object moves to the designated direction or not. By using the diagram, we discuss the transition stability in lifting up an object to the palm under gravitational force. Simulation results show that the transition phase for a column object with concave surface is stable, while that having convex surface tends to be unstable. We also show experimental results to confirm the basic behaviours during the transition phase.

This paper discusses how many fingers are necessary and sufficient for capturing a pyramidal-like object placed on a table under the gravitational field. Allowing that the contact friction is small enough to ensure that any direct grasp may fail in achieving an equilibrium grasp, we prove that a planar two-fingered hand are necessary and sufficient for achieving the task for an idealized 2D triangle object placed vertically on a table. We also consider 3D pyramidal-like objects, and prove that a spatial two-fingered hand are necessary and sufficient for achieving the task.

A trajectory generation method for the dynamic control of robots is proposed. The method is developed by introducing the combination of a time scale transformation with a time base generator into the artificial potential field approach. This method can actively control the dynamic behavior of the robot without any change of the form of the designed controller itself. Effectiveness of the proposed method is verified through computer simulations with an omnidirectional mobile robot.

This paper proposes a new active sensor system (active antenna) that can detect not only the contact location between an insensitive flexible beam and an environment but also the information of the environment's surface where the beam makes contact. The active antenna is simply composed of a flexible beam, actuators to move the beam, position sensors to measure the rotational angle of the beam, and a moment sensor. We first show that the contact distance under no lateral slip is proportional to the rotational compliance that the beam can sense at the rotational center. The lateral slip, which possibly occurs according to the pushing direction and the environment's geometry, overestimates the rotational compliance, and as a result, brings a large sensing error for the localizing contact point. The goal of this paper is to find the contact location under such conditions. We explore how to detect a lateral slip and how to determine the new pushing direction to avoid it. We show an algorithm that can search for the pushing direction which can avoid any lateral slip. The convergence of this algorithm is shown and a practical utilization of this algorithm is also discussed with a trade-off between the number of trials and the sensing accuracy.

This paper proposes a new neural adaptive control method that can perform adaptive control and identification for a class of controlled plants with linear and nonlinear uncertainties. This method uses a single neural network for both control and identification, and a sufficient condition of the local asymptotic stability is derived. Then, in order to illustrate the applicability of the proposed method, it is applied to the torque control of a flexible beam that includes linear and nonlinear structural uncertainties.

This paper discusses the 3D vision based active antenna (3D-VBAA) that can detect the contact force and the contact point between an insensitive elastic antenna and a 3D environment. The 3D-VBAA is composed of an insensitive flexible antenna, two actuators, two position sensors, a camera, and a one-axis moment sensor. By utilizing the antenna's shape mapped into the calibration, plane C, the vision sensor can provide both the contact distance and a force component on C. The moment sensor output allows us to evaluate the force component normal to C. The 3D-VBAA can work even under a compliant object, while the 3D active antenna cannot. We show that the coupling effect between vision and moment sensor depends on the implementation angle of the CCD camera. We verify our idea experimentally.

A practical procedure for achieving an, enveloping grasp is presented especially for cylindrical objects. It is composed of three phases, the approach phase, the lifting phase and the grasping phase. For the grasping phase, we introduce the natural computation mode in which a preset torque is commanded to each joint, so that both the equilibrium position and the contact forces may be determined automatically. A sufficient condition for achieving the enveloping grasps is shown for the given procedure. Experimental results are also shown to verify the effectiveness of the proposed grasping procedure.

An algorithm for detecting the shape of a 2D concave surface by utilizing a tactile probe is proposed. Pulling a tactile probe whose tip lies on an object's surface can be easily achieved, while pushing it is more difficult due to stick-slip or blocking up with an irregular surface. To cope with the difficulty of pushing motion on a frictional surface, the proposed sensing algorithm makes use of the pulling motion of a tactile probe from a local concave point to an outer direction. The algorithm is composed of three phases, local concave point search, tracing motion planning, and infinite loop escape. The proposed algorithm runs until the tactile probe detects every surface which it can reach and touch. We show some computer simulations and experimental results obtained with the proposed algorithm.

The paper deals with the rotational stability of a rigid body under the constant internal forces. For this problem, first, the stiffness tensor is constructed and its basic properties are analyzed. The internal force parameterization is done with the use of the virtual linkage/spring model. Within this parameterization, necessary and sufficient conditions of stability are obtained in the analytical form. In the space of the internal forces they form a region given by intersection of a plane and a singular quadric. Since the stability conditions guarantee only positive definiteness of the stiffness tensor, the contact friction is taken into account separately. In this paper analysis of the unilateral constraints is done under a study case, where achieving stable grasp of a convex object, with the stretching internal forces created by friction, is studied in an analytical example.

Impedance control is one of the most effective control methods for a manipulator in contact with its environment. In this method however, the manipulator does not move until an external force is exerted. The present paper proposes a non-contact impedance control which can regulate a virtual impedance between an arm and an external object using visual information. Validity of the proposed method is verified through experiments using a direct-drive robot.

A trajectory generation method for multi-arm robots through cooperative and competitive interactions among multiple end-effecters is proposed. The method can generate the trajectories of the multiple arms in a distributed manner based on a concept of a virtual interaction force which represents an interaction between an end-effector and an environment. It is shown that the method is effective not only for simple cooperative tasks such as positioning a common object, but also for more complicated tasks including relative motions among arms.

Autonomous distributed control (ADC) is one of the most attractive approaches for more versatile and autonomous robot systems. The paper proposes a parallel and distributed trajectory generation method for redundant manipulators through cooperative and competitive interactions among subsystems composing the ADC that is based on a concept of virtual arms. The virtual arm has the same kinematic structure as the manipulator except that its end point is located on a joint or link of the manipulator. Then the redundant manipulator can be represented by a set of the virtual arms. Trajectory generation and point to point control of the redundant manipulator are discussed, and it is shown that the kinematic redundancy of the manipulator can be utilized positively in the generated trajectories by using the virtual arms.

This paper proposes the vision based active antenna (VBAA) that can detect the contact force, the stiffness of the environment, and the contact location between an insensitive elastic antenna and an environment, through the observation of the antenna's shape by a camera. We show that both the contact location and the contact force can be estimated by measuring two arbitrary points on the antenna after a pushing motion; even though the exact contact point is hidden by occlusion. By considering the geometrical relationship between. The virtual (without environment) and the real (with environment) displacements of the contact point, the stiffness of the environment can also be estimated, which our conventional active antenna can not do. We present the basic working principle of the VBAA and give experimental verification.

This paper discusses scale-dependent grasp. Human beings unconsciously change their grasp strategy according to the size of objects, even though those objects have similar geometry. We first observe the grasping strategy of the human hand in wrapping around a cylindrical object placed on a table. We show that the human grasp planning can be roughly classified into three patterns according to the object's size. For a large cylindrical object, the human hand wraps around it directly without any regrasping process. As the diameter of the object decreases, a human begins to slip the object along the finger, so that the object can finally make contact with the palm. For a further smaller diameter, a human first picks it up by his/her finger tips and then makes a transition from a finger tip to a wrapping grasp. We also extract the essential motions of human grasping so that we can implement them in multifingered robot hands.

A dynamic active antenna sensor for locating a contact point is considered in this paper. In its simplest realization the sensor can be implemented in the form of a flexible beam rotating in plane. The main feature of the sensor is the use of the frequency-contact point curve which, for some regions, may be a multivalued function. One way of "improving" the curve could be concerned with the nonuniform mass and stiffness distribution of the beam which would lead to a rather complicated sensor design. Another way to remedy the situation is to change the sensing strategy by adding a proper control action at the joint of the beam. To study the sensor's response to the control, a dynamic model of the beam in contact with the external environment is developed. Analysis of the frequency equation shows how a simple proportional control law changes the sensing curve. It is found that in some limiting, but practically attainable cases the sensing curve becomes a single-valued function. Thus, the solution proposed can significantly simplify the identification procedure.

This paper is an extended version of the vision based active antenna (VBAA) that can detect the contact force, the stiffness of the environment, and the contact location between an insensitive elastic antenna and an environment, through the observation of the antenna's shape by a camera. If measured points are on the antenna, two arbitrary points are sufficient for computing the contact location and the contact force uniquely. In a practical application of VBAA, however, the measured points are not always on the antenna. In this paper, we show the condition for uniquely obtaining both contact location and contact force under the situation that the measured points are not always on the antenna. We also show a new compensation method to improve the sensing accuracy. We further verify our idea experimentally.

This paper presents a new trajectory generation method of the artificial potential field approach to a real-time motion planning problem. In the artificial potential field approach, the goal is represented by an attractive artificial potential and the obstacles are represented by repulsive ones, so that the robot reaches the goal without colliding with obstacles by using a gradient vector of the potential field. Although this approach is simple and computationally much less expensive than other methods based on the global information on the task space, little attention has been paid to temporal behavior of the generated trajectories such as a movement time from the initial position to the goal and the velocity profile of the end-effector motion. We argue that temporal behavior of the generated arm trajectory should be taken into account within the framework of the artificial potential field approach and introduce a time base generator that determines a dynamic behavior of the arm trajectory as a part of a feedback controller. By synchronizing a time course of the potential function used as an artificial potential field with the time base generator, the temporal behavior of the generated arm trajectory can be adjusted through the time base generator without any change of the potential function to be used.

This paper proposes an adaptive regulator using neural network. For a controlled object with linear and nonlinear uncertainties, the conventional optimal regulator is designed based on a known linear part of the controlled object and the uncertainties included in the controlled object are identified using the neural network. At the same time, the neural network adaptively compensates a control input from the predesigned optimal regulator. In this paper, we show how the output of the neural network compensates the control input based on the Riccati equation, and a sufficient condition of the local asymptotic stability is derived using the Lyapunov stability technique. Then, the proposed regulator is applied to the torque control of a flexible beam. Experimental results under the proposed regulator are compared with the conventional optimal regulator in order to illustrate the effectiveness and applicability of the proposed method.

Impedance control is one of the most effective control methods for the manipulators in contact with their environments. The characteristics of force and motion control, however, is determined by a desired impedance parameter of a manipulator's end-effector that should be carefully designed according to a given task and an environment. The present paper proposes a new method to regulate the impedance parameter of the end-effector through learning of neural networks. Three kinds of the feed-forward networks are prepared corresponding to position, velocity and force control loops of the end-effector before learning. First, the neural networks for position and velocity control are trained using iterative learning of the manipulator during free movements. Then, the neural network for force control is trained for contact movements. During learning of contact movements, a virtual trajectory is also modified to reduce control error. The method can regulate not only stiffness and viscosity but also inertia and virtual trajectory of the end-effector. Computer simulations show that a smooth transition from free to contact movements can be realized by regulating impedance parameters before a contact.

This paper proposes an impedance control method called the multi-point impedance control (MPIC) for redundant manipulators. The method can not only control end-effector impedance, but also regulate impedances of several points on the links of the manipulator, which are called virtual end-point impedances, utilizing arm redundancy. Two approaches for realizing the MPIC are presented. In the first approach, controlling the end-effector impedance and the virtual end-point impedances are considered as the tasks with the same level, and the joint control law developed in this approach can realize the closest impedances of the multiple points, including the end-effector and the virtual end-points to the desired ones in the least squared sense. On the other hand, in the second approach, controlling the end-effector impedance is considered the most important task, and regulating the impedances of the virtual end-points is considered as a sub-task. Under the second approach, the desired end-effector impedance can be always realized since the joint control torque for the regulation of the virtual end-point impedances is designed in such a way that it has no effect on the end-effector motion of the manipulator. Simulation experiments are performed to confirm the validity and to show the advantages of the proposed method.

This paper discusses a 3-D Active Antenna that can detect the contact location between an insensitive flexible beam and a 3-D environment through the measurement of the rotational compliance of the beam in contact with the environment. The lateral slip, which possibly occurs for the 3-D Active Antenna, overestimates the rotational compliance, and as a result, brings a large sensing error for the localizing contact point. The goal of this paper is to find the contact point under such conditions. In the first step, we push the antenna to the environment. If no lateral slip is detected during this motion, the contact point can be obtained through a similar method used for the 2-D model. If a lateral slip is confirmed, the pushing direction is changed continuously until development of any lateral slip is finally avoided. The authors explore how to detect a lateral slip and how to determine the new pushing direction to avoid it. The authors show an algorithm which can search not only the contact distance but also the normal direction of the environment's surface, even under the appearance of a lateral slip during the first step. The basic idea is also verified by experiments.

This paper discusses a new control method for an ultrasonic motor (USM). An USM has the capability of generating a resistant torque against an external one even under power-off which is well known as self-locking characteristic and conveniently utilized to save energy while maintaining rotor position. The goal of the paper is to relax the self-locking characteristic and to widely change it between lock and free states. In order to achieve this, we newly propose the two d.o.f. PWM control, in which OFF command is included in addition to CW and CCW commands. By applying this, we can also control the torque of USM in open loop without using any complicated circuit. In this paper, we first demonstrate the basic structure of the two d.o.f PWM control, and then show several experimental results to verify the effectiveness of the scheme proposed.

The present paper proposes a new feedback control strategy of mobile robots with nonholonomic constraints. This method introduces a function generator called a time base generator (TBG) as a time varying feedback gain, and by synchronizing linear and angular velocities of the mobile robot with a scalar variable generated by the TBG, the mobile robot can be positioned to the origin in the state space for any initial condition. Not only the configuration of the robot but also the time behavior of the generated trajectory such as the velocity profile and the movement time from an initial to a target position can be regulated through adjusting the parameters of the TBG.

This paper discusses the sensing accuracy of the active antenna that can detect the contact location between an insensitive flexible beam and an environment through the measurement of the rotational compliance of the beam in contact with the environment. During pushing motion of antenna, the contact point generally shifts on the environment's surface, which either underestimates or overestimates the rotational compliance and eventually brings a sensing error for localizing the contact point. The goal of this paper is to consider the effect of the environment's curvature on the rotational compliance. We show that the rotational compliance of the antenna being in contact with a curved environment depends on not only the contact distance but also on a new nondimensional parameter including the pushing angle and the environment's curvature where the antenna makes contact. We further show experimental results to support the theoretical analysis for various environments with different curvature.

This paper addresses a new active sensor system (Active Antenna) motivated by insect's antenna. The antenna can be characterized by its "flexibility" and "active motions". Based on these observations on insect's antenna, the authors' discuss an active sensor system composing of only four components, an insensitive flexible antenna, a position sensor, a torque sensor, and an actuator. The authors first consider the conditions which allow localisation of any contact point between the insensitive flexible antenna and environment by applying an active motion to the antenna. The authors show that the use of a straight antenna always makes it possible to localise the contact point irrespective of friction at the point of contact. The authors also show that contact point sensing by the use of a circular shaped antenna is weakly influenced by the friction at the point of contact.>

The present paper examines spatial characteristics of human hand impedance in multi-joint arm movements. While a subject maintains a hand location, small external disturbance to his hand is applied by a manipulandum. Time changes of the hand displacements and forces caused by the disturbance are measured and the hand impedance is estimated using a second-order linear model. The estimated hand impedance in different subjects and hand locations are summarized as follows: (1) spatial variations of the estimated hand impedance ellipses approximately agree with the experimental results of other researchers (Mussa-Ivaldi et at, 1985: Dolan et al., 1993), (2) the human hand inertia characteristics can be explained from basic biomechanics of the passive inertial effects (3) the grip force of the subject increases the size of the stiffness and viscosity ellipses, and (4) spatial features of the orientation. And the shape characteristics of the stiffness and viscosity ellipses are mostly explained from the kinematics point of view of the human arm.>

A distributed feedback control for redundant manipulators based on virtual arms is proposed. The virtual arm has the same kinematic structure as the manipulator except that its end-point is located on the joint or link of the manipulator. Therefore, the configuration of the redundant manipulator can be represented as a set of virtual arms. Dynamic behavior of each subsystem is based on an end-point feedback control of the virtual arm on the operational space. Position control of the manipulator utilizing kinematic redundancy can be realized through cooperative and competitive interactions between subsystems, and the dynamic behavior of the whole system is always stable. The advantages of the method are discussed.>

One of the basic characteristics of the dual-arm robots which manipulate a common object is the redundancy of joint degrees of freedom The present paper proposes a compliance control method utilizing kinematic redundancy of the dual-arm robot. The method presented here can regulate the compliance or stiffness of several points on the dual arm- robot as well as the object compliance through regulation of the joint stiffness.

Proposes a new conventional method to reconstruct 3D motion of rigid polyhedral objects from a sequence of monocular images. In general the problem is ill-posed, therefore additional information is required to recover depth. The authors utilize line correspondence between sequential images and existence of parallel line segments in the scene. The relation between the coordinates of points can be described only by a rotation matrix, if it is formulated relatively. Then if there exists a pair of parallel line segments, the matrix can be solved by using linear equations. After that the translation vector is computed. It is necessary that there exists at least one pair of parallel line segments in the scene in order to obtain motion parameters. They also propose the method to extract pairs of parallel line segments in the image. Finally some experimental results for simulated data are demonstrated. >