Electromyogram (EMG) signal-based gait phase recognition for walking-assist devices warrants much attention in human-centered system design as it well exemplifies human-in-the-loop control where the system's prediction directly affects subsequent walking motion. Since walking motion poses considerable variations in electrode placement, performance reliability of such systems is contingent on a combination of electrode montage and a feature extraction method that takes into account underlying physiological factors of peripheral muscles where electrodes are placed. In many practical applications, however, proper consideration of effects of the electrode location variation on performance reliability of the system has received scant empirical attention. Here, based on a user-centered design principle, we establish a gait phase recognition system that is capable of rigidly controlling ill effects due to this covariate by carrying out a large-scale analysis that combines statistical, model-based, and empirical approaches. In doing so, we have developed a special sensing suit for the control of electrode placement and a reliable data acquisition. We then have conducted a nonparametric statistical analysis on class separability values of thirty types of EMG feature sets, followed by a model-based analysis to address the tradeoff between class separability and dimensionality. To further address the issue of how these results generalize to independent systems and data sets, we have carried out an empirical performance assessment over six classification methods. First, the two feature types, Integral of Absolute Value and Histogram, and a combination of the two are shown to be robust against electrode location variations while providing a firm performance guarantee. Second, system organization scenarios are presented on a case-by-case basis, allowing us to trade off system complexity for on-line adaptation capability. Collectively, our integrated analysis lends itself to formulating a guideline for design of highly reliable EMG signal-based walking assistant systems in a variety of smart home scenarios.

In designing autonomous service systems such as assistive robots for the aged and the disabled, discovery and prediction of human actions are important and often crucial. Patterns of human behavior, however, involve ambiguity, uncertainty, complexity, and inconsistency caused by physical, logical, and emotional factors, and thus their modeling and recognition are known to be difficult. In this paper, a nonsupervised learning framework of human behavior patterns is suggested in consideration of human behavioral characteristics. Our approach consists of two steps. In the first step, a meaningful structure of data is discovered by using Agglomerative Iterative Bayesian Fuzzy Clustering (AIBFC) with a newly proposed cluster validity index. In the second step, the sequence of actions is learned on the basis of the structure discovered in the first step and by utilizing the proposed Fuzzy-state Q--learning (FSQL) process. These two learning steps are incorporated in an amalgamated framework, AIBFC-FSQL, which is capable of learning human behavior patterns in a nonsupervised manner and predicting subsequent human actions. Through a number of simulations with typical benchmark data sets, we show that the proposed learning method outperforms several well-known methods. We further conduct experiments with two challenging real-world databases to demonstrate its usefulness from a practical perspective.

In this brief, we consider kernel methods for classification (Shawe-Taylor and Cristianini, 2004) from a separability point of view and provide a representation of the Fisher criterion function in a kernel feature space. We then show that the value of the Fisher function can be simply computed by using averages of diagonal and off-diagonal blocks of a kernel matrix. This result further serves to reveal that the ideal kernel matrix is a global solution to the problem of maximizing the Fisher criterion function. Its relation to an empirical kernel target alignment is then reported. To demonstrate the usefulness of these theories, we provide an application study for classification of prostate cancer based on microarray data sets. The results show that the parameter of a kernel function can be readily optimized.

Memory impaired people, e.g., dementia people, requires careful social support. Dementia people are getting increased with very high rate especially. It has been reported that regular daily life can alleviate the symptom of the memory loss. Accordingly, human behavior suggestion is highly expected to help memory impaired people live regularly. In this paper, we propose a human behavior suggestion system based on fuzzy-state Q-learning for memory impaired person, and show its possible application in intelligent sweet home. Specifically, we claim that an averaged frequency feature is an important factor. In order to evaluate the validity of the proposed human behavior suggestion system, we conduct experiments with a real world data set, INT DB. The experimental results show that the proposed system with the averaged frequency feature outperforms the existing system.

The natural signal of human such as voice or gesture has been applied to the system for assisting disabled and the elderly people. As an example of such kind of system, the soft remote control system has been developed by HWRS-ERC in KAIST. This system is a vision-based hand gesture recognition system for controlling home appliances such as television, lamp and curtain. One of the most important technologies of the system is the hand gesture recognition algorithm. The frequently occurred problems which lower the recognition rate of hand gesture are inter-person variation and wrong recognition of similar gestures. In this paper, we propose multivariate fuzzy decision tree (MFDT) learning and classification algorithm for hand gesture recognition. The similar meaningless gestures are rejected using fuzzy garbage model. To recognize hand gesture of a new user, the most proper recognition model among several well trained models is selected using model selection algorithm and incrementally adapted to the user's hand gesture. For the general performance of MFDT as a classifier, we show classification rate using the benchmark data of the UCI repository. The experimental results show the classification and user adaptation performance of proposed algorithm is better than traditional fuzzy decision tree. Also the meaningless gestures are well rejected.

The independence of people who need help with daily activities will become of vital importance to all societies in the future. This paper addresses the problem of controlling the assistive home environment and emphasizes human-friendly human-machine interactions in an approach designed to achieve independence. To provide residents with an accessible, convenient, and cost-effective environment for independent living, we introduce a new service robot, categorized as a steward robot, as an intermediate agent between residents and their complex smart house environment. The learning capability and emotional interaction of the robot can make it more human-friendly in various tasks. A learning system enables the robot to provide customized services by accumulating knowledge of the user's behavioral patterns in daily activities. An emotional interaction system generates facial expressions to communicate with the user in a human-friendly manner. We have developed two types of a steward robot: a software type, which can be used everywhere via personal computing devices such as a PDA and a cellular phone, and a hardware type, which provides tangible services with physical interaction via two robotic arms and a mobile base.

We propose a design method of personalized classifier with soft computing techniques for automatic facial expression recognition. Motivated by the fact that even though human facial expressions of emotion are often ambiguous and inconsistent, humans are, in general, very good at classifying such complex images. In consideration of individual characteristics, we adopt a similar strategy of building a personalized classifier to enhance the recognition performance. For realization, we use a soft computing technique of neurofuzzy approach. Specifically, two core steps-ldquomodel building/modificationrdquo and ldquofeature selectionrdquo-are applied to build a ldquopersonalizedrdquo classification structure. The proposed scheme of classifier construction achieves a higher classification rate, minimal network parameters, easy-to-extend structure, and faster computation time, among others. Four sets of facial expression data are chosen and image features are extracted from each of them to show effectiveness of the proposed method, which confirms considerable enhancement of the whole performance.

To deal with data patterns with linguistic ambiguity and with probabilistic uncertainty in a single framework, we construct an interpretable probabilistic fuzzy rule-based system that requires less human intervention and less prior knowledge than other state of the art methods. Specifically, we present a new iterative fuzzy clustering algorithm that incorporates a supervisory scheme into an unsupervised fuzzy clustering process. The learning process starts in a fully unsupervised manner using fuzzy c-means (FCM) clustering algorithm and a cluster validity criterion, and then gradually constructs meaningful fuzzy partitions over the input space. The corresponding fuzzy rules with probabilities are obtained through an iterative learning process of selecting clusters with supervisory guidance based on the notions of cluster-pureness and class-separability. The proposed algorithm is tested first with synthetic data sets and benchmark data sets from the UCI Repository of Machine Learning Database and then, with real facial expression data and TV viewing data.

In the procedure of steady-state hierarchical optimization with feedback for a large-scale industrial process, it is usual that a sequence of step set-point changes is carried out and used by the decision-making units while searching the eventual optimum. In this case, the real process experiences a form of disturbances around its operating set-point. In order to improve the dynamic performance of transient responses for such a large-scale system driven by the set-point changes, an open-loop proportional integral derivative-type iterative learning control (ILC) strategy is explored in this paper by considering the different magnitudes of the controller's step set-point change sequence. Utilizing the Hausdorff–Young inequality of convolution integral, the convergence of the algorithm is derived in the sense of Lebesgue-P norm. Furthermore, the extended higher order ILC rule is developed, and the convergence is analyzed. Simulation results illustrate that the proposed ILC strategies can remarkably improve the dynamic performance such as decreasing the overshoot, accelerating the transient response, shortening the settling time, etc.

Recently, in service robotics area, increasing attention is being paid on interaction capability of robot for human-being as well as its task performing capability. In this paper, in particular, an automatic gesture generation methodology for conversational interaction of service robots is presented toward more human-friendly human-robot interaction. From the survey on the results in the psychology field, we first categorized the target gestures into the three types of gestures, which are basic, supplementary/emphasizing, and finishing/interconnective gestures with their corresponding unit gesture components. Then, a set of mapping rules have been extracted for gesture generation based on observing human behavioral patterns during conversation, by means of morpheme decomposition and analysis. From the given text input in Korean, the proposed system tries to generate robotic gestures, which consist of the head and the arm motions, by gesture selection and motion scheduling schemes. Finally, we discuss on the effectiveness of the proposed system with the simulated motions of robot as an initial attempt to apply in a practical system.

In this paper minimal representation of voiced speech based on decomposition into AM-FM components is proposed for generation of emotion speech. For the decomposition, firstly time-frequency boundaries of AM-FM components are estimated and secondary each AM-FM component is extracted by using the variable bandwidth filter adaptive to the estimated time-frequency boundaries. Finally, two parameters, that is, instantaneous frequency and instantaneous amplitude of each AM-FM component are estimated. The set composed of instantaneous amplitudes and instantaneous frequencies is the minimal representation of voiced speech signals. The minimal representation is optimal feature set since the set describes effectively the biomechanical characteristics of the vocal codes and the vocal track. Raw speech signals are modified by changing the parameters for generation of emotion speech.

This paper conveys Bayesian interpretation of improved integrated adaptive fuzzy clustering(IAFC), which is one of the adaptive fuzzy neural network models and suggests upper bound of vigilance parameter, which gives us a guideline to endow IAFC with flexibility within the framework of minimum risk classifier. Besides, we proposed the off-line and on-line learning strategy of IAFC. The proposed techniques are applied to construct facial expression recognition system dealing with neutral, happy, sad, and angry. We empirically show that proposed methods are able to outperform the conventional IAFC.

A linear time-varying filter with the characteristic of variable bandwidth is proposed for noise reduction of signals with time-varying bandwidth and decomposition of multicomponent signals into components. The proposed filters are composed of constant coefficients and differential operators with a time-varying coefficient. Frequency analysis of the proposed filter is given. Also, the compensation of time-varying delay due to linear phase is discussed

In this paper, we propose a new service agent, called a steward robot, which provides inhabitants with accessible, convenient, and cost effective interfaces as an intermediate agent between the user and a smart home environment. To implement more subtle emotional reaction of the agent, we adopt a novel emotional cue, sentiment relation, and address a problem of modeling the intensity and transition of emotion words, while previous researches have mainly focused on the selection of discrete emotion words from psychological point of view. We also discuss some issues of the proposed emotional model applying a virtual scenario to our Intelligent Sweet Home

The facial expression recognition (FER) is one of the biosignal-based recognition techniques which attract a lot of attention recently. To deal with its complex characteristics effectively, we adopt the soft computing techniques (SCT) such as fuzzy logic, neural networks, genetic algorithm and/or rough set technique. In this paper, we overview the state-of-the-art reports on FER in view of SCT, and introduce some interesting works done by our group on the SCT-based facial emotional expression recognition. Specifically, 1) fuzzy observer-based approach, 2) personalized FER system based on fuzzy neural networks, and 3) Gabor wavelet neural networks are briefly discussed

In this paper, we describe the "soft remote control system" that provides easy control of home installed appliances to improve the inhabitant's comfort. This system is feasible to control various home appliances naturally by recognizing the user's hand gestures in the "intelligent sweet home". The user selects the device that he/her wants to control by pointing it with his/her hand. Then, the user can command operation of the desired function via a predefined hand gesture. The approach complements the inconvenience of the conventional remote controllers giving additional freedom to persons with movement deficits and people without disabilities.

This paper deals with a method to recognize hand-postures in Softremocon system. The system uses multiple cameras to recognize the user's hand-posture. It is hard to recognize hand-posture since a human-hand is the object with high degree of freedom and there follows the self-occlusion problem, the well-known problem in vision-based recognition area. It can be a possible solution to use multiple cameras. However, when we use multiple images, another problem is arisen, that is, the composition methods of multiple recognition results from each camera to get the final decision. We show 2-staged recognition process. The first stage gets the results which represent each camera viewpoint. And the second one results out the final decision. This structure gives robustness to the hand-posture recognition against view-point variation.

We discuss about a design process of 'personalized' classifier with soft computing techniques. Based on a human's way of thinking, a construction methodology for personalized classifier is mentioned. Here, two fuzzy similarity measures and ensemble of classifiers are effectively used. A feature selection method for the fuzzy neural networks plays also a key role for construction of personalized classifier. As one of the possible applications, facial expression recognition problem is discussed. The numerical result shows that the proposed method can enhance the classification rate for 22 persons from 81.0% to 90.4%.

The hand-posture-based user interface has been considered as an alternative to human-computer interaction (HCI), or for human-robot interaction, but many difficulties remain to be resolved. For natural recognition of hand posture by a vision device, we find that view-point-in variance is one of the most required characteristics. This paper presents a hand-posture recognition method that is robust against various viewpoints. Observing that when the viewing direction is changed, the same appearance feature does not necessarily match the 3D structure, and vice versa, we propose a 2-layered hand-posture database structure in which the 2D appearance-based method and the 3D-based method are complementarity combined. We have tested the approach in an experiment for 15 different viewing directions.

Many diverse methods have been developed in the field of biometric identification as a greater emphasis is placed on human friendliness in the area of intelligent systems. One emerging method is the use of footprint shape. However, in previous research, there were some limitations resulting from the spatial resolution of sensors. One possible method to overcome this limitation is through the use of additional and independent information such as gait information during walking. In this study, we suggest a new person-recognition scheme based on the center of pressure (COP) trajectory in the dynamic footprint. To make an efficient and automated footprint-based person recognition method using the COP trajectory, we use a hidden Markov model and a neural network. Finally, we demonstrate the usefulness of the suggested method, obtaining an approximately 80% recognition rate using only the COP trajectory in our experiment with 11 people. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 1127–1141, 2004.

A bandpass variable-bandwidth filter (BPVBF) is proposed for the reconstruction of the signal whose boundaries are known in time-frequency domain (TFD). The behaviors of the ideal BPVBF are investigated in TFD. Simulation results show that the ideal BPVBF rejects noise in the outside of a region of interest in TFD and produces the reliable reconstruction of signals.

Human's intention plays a key role in human-machine interaction as in the case of a robot serving for a handicapped person. The quality of a service robot will be much enhanced if the robot can infer the human's intension during the interaction process. In this paper, we propose a soft computing-based technique to read a user's intention using some multisensors-based approach. We have tested the technique by a scenario of 'serving a drink to the user'. With such force/torque or vision sensor, the robot can effectively infer the user's intention to drink the beverage or not to drink. As an application, this intention technique is employed for building a rehabilitation robot, called KARES II, to perform various human-friendly human-robot interaction.

Facial expression recognition is very important in many human-robot/human-computer interaction systems. Although so many researches are done, it is hard to find a practical applications in the real world due to its underestimate about individual differences among people. Thus, as a solution for such problem, we introduce a 'personalized' facial expression recognition system. Many previous works on facial expression recognition focus on the well-known six universal facial expressions (happy, sad, fear, angry, surprise and disgust) under usage of unified (or non-separated) classification approach. However, for ordinary people, it is a very difficult task to make such facial expressions without much effort and training. Instead of universal facial expressions, many people show 'personalized' or 'individualized' facial expressions typically. Thus, for dealing with such personalities, we propose a method to construct a personalized classifier based on novel feature selection method. Specifically, feature selection is done by histogram-based approach in the frame of fuzzy neural networks(FNN). Besides, we also use an integrated approach for facial expression recognition. Actual experiments/simulations show that the proposed method is effective not only in view of facial expression recognition but also in view of pattern classifier itself.

This paper presents a novel application of fuzzy logic in the field of rehabilitation robots. This soft computing-based approach is utilized for the task of serving a drink to a person, to determine whether contact between the person's mouth and the drinking glass has occurred. The decision is based on force sensor data, which are pre-processed before being applied for decision-making. The design or decisionmaking is accomplished by means of force information as well as the first and second differential of the force. The results demonstrate that the detection algorithm is of high reliability and robustness under various environmental conditions.

In this paper is proposed a robust higher-order iterative learning control (ILC) algorithm for discrete-time systems. In contrast to conventional discrete-time learning methods, the proposed learning algorithm is constructed based on both time-domain performance and iteration-domain performance. Also, the proposed learning algorithm use more than one past error in the iteration-domain. It is proved that the proposed method has robustness in the presence of external disturbances and, in absence of all disturbances, the convergence of the proposed learning algorithm is guaranteed. A numerical example is given to show the robustness in the presence of state disturbance and convergence property according to parameters change.

A multiobjective control problem has been handled in many different ways such as fuzzy, neural network and reinforcement learning, etc. Among them, a reinforcement learning method solves a multiobjective control problem without any prior knowledge. In this article, a new reinforcement learning method for a multiobjective control problem is proposed in consideration of its convergence. The proposed method, in which objective eligibility is considered for handling multirewards, reformulates a multiobjective control problem in a form of a reinforcement learning problem under non-Markov environment. Using a similar relation to eligibility, the proposed method dealt with the previous research results of eligibility and was implemented with the concept of a decoupled fuzzy sliding mode control (DFSMC). © 2003 Wiley Periodicals, Inc.

Reports some early results of our study on continuous Korean sign language (KSL) recognition using color vision. In recognizing gesture words such as sign language, it is very difficult to segment a continuous sign into individual sign words since the patterns are very complicated and diverse. To solve this problem, we disassemble the KSL into 18 hand motion classes according to their patterns and represent the sign words as some combination of hand motions. Observing the speed and the change of speed of hand motion and using fuzzy partitioning and state automata, we reject unintentional gesture motions such as preparatory motion and meaningless movement between sign words. To recognize 18 hand motion classes we adopt the hidden Markov model. Using these methods, we recognize 15 KSL sentences and obtain 94% recognition ratio.

Human-robot interaction technologies are very important components in the human centered system. Among many human-robot interaction technologies, the intention reading of a user plays an important role in human centered systems. In the paper, we focus on the use of intention reading in rehabilitation robots, and implement its capability for the wheelchair-based robotic arm system, called KARES (KAIST Rehabilitation Engineering Service System) II. For effective intention reading, visual servoing by using an image sequence of user's face or control by using EMG signals of user's arm are applied to KARES II. Specially, it is shown that soft computing techniques are very effective for intention reading.

Intention reading is one of the essential functions of human-friendly rehabilitation robots. First, the overall structure of a new wheelchair-based robotic arm system, KARES II, and its human-robot interaction technologies are presented. Among those technologies, we concentrate on visual servoing that allows this robotic arm to operate autonomously via visual feedback. Effective intention reading, such as recognizing the positive and negative meaning of the user, is performed on the basis of changes of the facial expression around lips that is strongly related to the user's intention while serving the user. For the efficient visual information processing, log-polar mapped images are used to control the stereo camera head that is located in the end-effector of the robotic arm. The visual servoing with effective intention reading was successfully applied to serve a beverage for the user.

The authors present a real time hand gesture recognition system that controls the motion of a human avatar based on predefined dynamic hand gestures in a virtual environment. First, we note that recognition of a distinction between the start and end of a motion is needed and a lot of time is spent on learning in conventional recognition systems. To resolve this problem, we propose a recognition method using intelligent techniques. Then, we also present an obstacle-free path generation method to ensure that an avatar can navigate, avoiding obstacles in a virtual environment.

We provide an introduction to the overall structure of a new wheelchair-based robotic arm system, KARES II, and its human-robot interaction technologies which assist independent life of the elderly and the disabled that have disadvantages in sensory and motor functions of their limbs. First of all, the tasks of a robotic arm are specified. Next, several human-robot interaction technologies are presented to perform the defined tasks in comfortable and safe ways. In addition, the input/output relation of several interaction technologies, such as an eye-mouse, a haptic suit, bio-signal recognition and visual servoing, is described under two essential factors: one is intention reading of the user, and the other is the autonomous capability of the robot. Among those technologies, we concentrate on visual servoing that is a good solution for reducing the burden of a user in a wheelchair with a robotic arm. We mention difficulties of visual servoing in the wheelchair-based robotic arm. It is found that visual servoing using space variant vision has several merits in an eye-in-hand configuration. Specially, a nearby target is handled efficiently through a modified log-polar mapping.

In this paper, a new fuzzy basis function vector (FBFV) approach for the adaptive control of multivariable nonlinear systems is presented. With this method, the nonlinear plant is first linearized. The linearized bias and uncertainties as well as disturbances are assumed to be included in the model structure and their upper bound will be adaptively learned by the FBFV method. The output of the FBFV is used as the parameters of the robust controller in the sense that both the robustness and the asymptotic error convergence can be obtained for the multivariable nonlinear system. The effectiveness of the proposed analysis and design method is illustrated with a simulated example.

Many practical control problems for the complex, uncertain or large-scale plants, need to simultaneously achieve a number of objectives, which may conflict or compete with each other. If the conventional optimization methods are applied to solve these control problems, the solution process may be time-consuming and the resulting solution would often lose its original meaning of optimality. Nevertheless, the human operators usually perform satisfactory results based on their qualitative and heuristic knowledge. In this paper, we investigate the control strategies of the human operators, and propose a fuzzy model-based multi-objective satisfactory controller. We also apply it to the automatic train operation (ATO) system for the magnetically levitated vehicles (MAGLEV).

In this presentation we propose an algorithm for the adaptive gait control by which a quadruped walking robot can walk against external disturbances. The proposed gait mechanism makes it possible for a quadruped walking robot to change its gait pattern in response to external disturbances from various external forces acting on the body of the walking robot. More specifically, we propose a new criterion for the stability margin of a walking robot under unknown external forces by using the effective mass center based on the zero moment point. And for the solution of the adaptive gait algorithm against external disturbances, a method of altitude control and reflexive direction control is suggested. Based on the algorithmic search method, an optimal stride of the quadruped walking robot that walks from any leg configuration to the standard configuration is also proposed. To verify the efficiency of the proposed approach, some simulation results are provided.

This paper presents a systematic adaptive control strategy which can accomplish a variety of control objectives (position control, internal force control, constraints,and optimizations) for the generalized high-dimensional robotic systems (GHDRS) without restriction on target systems. Based on the concept of virtual decomposition by which a GHDRS is virtually decomposed into several objects and base-floating open chains, the motion control problem of the original system is converted into that of each object and that of each open chain, individually, while the internal force control as well as the constraint force control may be performed with respect to each object only. This feature makes it possible to implement the control algorithm of each subsystem in modularly structured hardware which can be integrated to form any specific robot controller dedicated to a specific application. In the sense of Lyapunov, it is declared that the dynamic coupling between every two physically connected subsystems can be completely represented by the so-called virtual power flows (VPFs) at the cutting points between them. Asymptotic stability of the complete system can be ensured by choosing the system Lyapunov function as the sum of all nonnegative accompanying functions assigned for the subsystems. Some possible applications based on the proposed approach are discussed. Finally, computer simulations of two PUMA 560 arms transporting a common object along a prespecified trajectory are carried out to verify the stability and robustness issues of the system.

The sign language is a method of communication for the deaf-mute. Articulated gestures and postures of hands and fingers are commonly used for the sign language. This paper presents a system which recognizes the Korean sign language (KSL) and translates into a normal Korean text. A pair of data-gloves are used as the sensing device for detecting motions of hands and fingers. For efficient recognition of gestures and postures, a technique of efficient classification of motions is proposed and a fuzzy min-max neural network is adopted for on-line pattern recognition.

It is known that the self-organizing fuzzy logic controller proposed by Procyk is sensitive to the external signals such as set-point changes and/or disturbances. Also, this difficulty may be encountered in other fuzzy learning controllers that have a learning algorithm to minimize the cost function of the error. To solve this problem, a new robust self-learning fuzzy logic controller is proposed based on the principle of sliding mode control. Computer simulation shows that the proposed method is robust to the set-point changes and the disturbances. Also, to show the applicability to the tracking control of MIMO systems, at is applied to a two-link robot manipulator.

An inference network is proposed as a tool for bidirectional approximate reasoning. The inference network can be designed directly from the given fuzzy data (knowledge). If a fuzzy input is given for the inference network, then the network renders a reasonable fuzzy output after performing approximate reasoning based on an equality measure. Conversely, due to the bidirectional structure, the network can yield its corresponding reasonable fuzzy input for a given fuzzy output. This property makes it possible to perform forward and backward reasoning in the knowledge base system.>

The design of a multiprocessor-based automatic adjustment system for ITCs (integrated tube components) used in color picture tube fabrication is described. A rule-based hierarchical control approach is proposed to resolve the problem caused by uncertainties and nonlinearities in ITC processes. The electronic control system consists of three subsystems, each of which uses 32 bit microprocessors. Various image processing algorithms were incorporated into the control system for fast and reliable operation by using online processing units and a ral-time frame grabber. The general properties and difficulties of the ITC process are described, and the system configuration is outlined. Vision sensing and control algorithms are considered.>

An adaptive gait control problem for a quadruped is formalized and an adaptive gait algorithm for eliminating dead-lock positions is proposed. The proposed method is based on the concept of a neural optimization network. The desired footholds are taken as the output variables of the neural circuit, and differential equations which tend to minimize the energy of the network are derived. An efficient method to make the quadruped statically stable is addressed. Several conditions for satisfying the geometric limits of reachable volumes are introduced. The maximum stride which can be transversed in the current configuration is calculated. The algorithm is applicable to both even and rough terrain without gait transitions.>

In manufacturing CPT (Color Picture Tubes) used in TV sets and monitors, an ITC (Integrated Tube Components) adjustment process is involved. Automating the ITC adjustment process needs fast and robust visual sensing/measurement. Real time color purity and convergence measurement algorithms are described. Existing color purity measurement algorithms for ITC adjustment system often take too much time to detect color purity. Moreover, for convergence measurement, measurable data is not sufficient for accurate detection. To overcome these difficulties, real time vision system with 9 area cameras and many linear array cameras is developed. Area cameras are used for color purity measurements and linear array cameras are used for convergence measurements. To devise the purity measurement algorithm, the characteristics of CPT microscopic images are analyzed. To measure the exact color purity, several methods are selectively applied for calculating color purity depending on the conditions of CPT screen. For convergence measurement, special test CPT with no shadow mask and no black matrix is manufactured and a new measurement algorithm is developed using fuzzy inference and a priori knowledge taken from the specially manufactured CPT. The proposed algorithms are successfully applied as described in the experimental results.>

A trajectory planning method that ensures collision-free and time-optimal motions for two robotic manipulators with limited actuator torques and velocities is proposed. In terms of parameters defining the positions along the paths of two robots, a two-dimensional coordination space and a maximum velocity curve are constructed to detect collision regions and to plan a time-optimal velocity curve, respectively. An algorithm is then provided for the case of zero initial velocities and a single collision region. A numerical example is included.>

A method is presented for using image features in servoing a robot manipulator. Specifically, the concept of a feature is mathematically defined, and the differential relationship between the robot motion and feature vector is derived in terms of a feature Jacobian matrix and its generalized inverse. The feature-based PID (proportional-integral-derivative) controller is established with three scalar gains and an n*n matrix. By means of various examples, the method of feature-based servoing of a robot proposed is proved to be very effective for conducting object-oriented robotic tasks.>

A turning gait is proposed for a quadruped walking robot. The proposed scheme makes it possible to control the translation and orientation of the walking robot simultaneously. First, feasible leg sequences which guarantee positive longitudinal gait stability margin for each direction of movement are found. A method for finding the lifting time of each leg of a feasible leg sequence and selecting an optimal gait among feasible gaits is then suggested. The proposed gait can be applied to control the posture of walking robots and to generate an optimal gait for a desired movement of translation and rotation of the walking robot systematically.>

A trajectory planning method ensuring collision-free motion and minimum traveling time for two robots is proposed. The robot system under consideration is given specified geometric paths for two robots: then, by using traveled lengths along the paths, a two-dimensional coordination space is constructed for detecting collision. Also the robot dynamics is transformed as a function of the traveled lengths, and is described in the phase plane containing bounds on acceleration and velocity limits derived from a set of constraints on torques and maximally allowable velocities of actuators. Then the main algorithm is provided which simultaneously considers collision avoidance and time optimality on the coordination space and the phase plane, respectively. The proof for optimality of the proposed algorithm is given, and a simulation result is included to show the usefulness of the proposed method.>

An iterative learning control algorithm for a class of linear periodic systems is proposed in which parameter estimation is performed in the domain of an iterative sequence of operations with the time frozen. A sufficient condition for the convergence of the proposed algorithm is given. It is also shown that the proposed learning controller can be applied to the continuous path control of robot manipulators. It is noted that the proposed algorithm works reasonably well only for the case of small perturbations with respect to a nominal trajectory.>