In this paper, we propose a vision-based vehicle detection system. We use a method composed of a hypothesis generation (HG) step and a hypothesis verification (HV) step, following the general approach to vision-based vehicle detection systems. In the HG step, the system extracts hypotheses using shadow regions that appear under vehicles. In the HV step, the system classifies feature vectors extracted from hypotheses to determine whether those hypotheses are vehicles. Along with the histogram of oriented gradients (HOG), we propose and implement a new type of feature vector, i.e., HOG symmetry vectors, in this paper. We also propose a new classification method that uses data importance in the HV step. The data importance value is based on the locations of hypotheses to prioritize hypotheses that have greater risks of accident. Experimental results show the strong performance of our proposed system.

For the development of for mental model of intelligent robot, in this paper we deal with a cognitive modeling system based on the cognitive process of human being, using psychoanalysis and two kinds of emotion theories, artificial intelligence, neural network and fuzzy Petri net. Based on the psychoanalysis of Freud, we graph the psychiatric structure named 'mind sketch' by KNBN(Kohonen Network based on Belief Network). Within the mind sketch, 'emotion' is used as the base for making relative data. It is based on the emotion theory of Plutchick and the cognitive emotion theory of Ortony et al. To combine these emotional theories, a fuzzy Petri net algorithm has been proposed. And a cognitive modeling system has been developed.

This paper presents an adaptive approach for synchronization of Takagi–Sugeno (T–S) fuzzy chaotic systems. T–S fuzzy model can represent a general class of nonlinear system and we employ it for fuzzy modeling of the chaotic drive system. Since the output of the drive system is only available for synchronization, the response system is designed based on fuzzy adaptive observer for uncertain parameters and parameter mismatch cases. We analyze the stability of the overall fuzzy synchronization system by applying Lyapunov stability theory and derive stability conditions by solving linear matrix inequalities (LMIs) problem. The adaptive law is derived to estimate the uncertain parameters or parameter mismatch. Numerical examples are given to demonstrate the validity of the proposed fuzzy adaptive synchronization approach.

We propose an effective facial features detection method for human– robot interaction (HRI) with indoor mobile robot. In case of human-robot interaction, its vision system has to manage such difficult problems as pose variations, illumination changes, and complex backgrounds, which is caused by the mobility of robot. In this paper, in order to overcome such difficult problems, we suggest a facial feature detection method based on local image area and direct pixel-intensity distributions, in which we propose two novel concepts; the directional template for evaluating intensity distributions and the edge-like blob map with multiple strength intensity. Using this proposed blob map, we show that the locations of major facial features – two eyes and a mouth – can be successfully estimated. Without the information of facial area boundary, final candidate face region is determined by both obtained locations of facial features and weighted correlations with stored facial templates. Our approach is also a flexible algorithm that can be applicable to both color and gray image. In case of color image, faster detection of both facial features and face is feasible by using the chromatic property of facial color. Experimental results from many color images and well-known gray level face database images prove the usefulness of proposed algorithm in human– robot interaction applications.

A new robust TSK fuzzy modeling algorithm is proposed. The proposed algorithm is the modified version of noise clustering algorithm. Various robust approaches to deal with the data containing noise or outliers in real applications were proposed, but most algorithms process clustering of data first and then conduct fuzzy regression. We propose the algorithm that parameters of the premise part and the consequent part are obtained simultaneously. The proposed algorithm shows good performance against noise or outliers. Without adaptation of parameters, the proposed algorithm shows the superior performance over other approaches.

In this paper, we propose an energy-based stability control of a passive haptic device equipped with passive actuators such as an electric brake. Unstable behavior is observed even in the passive haptic system due to time delay mainly arising from the slow update rate of the virtual environment. The force approximation, one of the major limitations of the passive haptic device, is explained in the passive FME analysis. The ideal force display of passive haptic device is investigated, and it is shown that the passive haptic system frequently obtains the pullback capability due to the force approximation. The indirect force control scheme is proposed in that the change rate of passivity is controlled to simulate the ideal force display. To avoid the increase of the passivity due to the rate controller, which is not correlative to stability, passivity feedback is established. An experiment was conducted with a 2-link passive haptic device equipped with two electric brakes. From the experiment, the proposed control method showed satisfactory performance in displaying a plain virtual wall.

In this paper, design and integration of the ROBHAZ-DT3 are introduced which is a newly developed mobile robot system with double tracks. It is designed to curry out military and civilian missions in various hazardous environments. The rotational passive adaptation mechanism equipped between the front and rear body enables the ROBHAZ-DT3 to have good adaptability to uneven terrain including stairways. The passive adaptation mechanism reduces energy consumption in moving on uneven terrain as well as it offers simplicity in design and teleoperation. Based on this new design concept, dynamic simulation was conducted to determine the significant parameters such as optimal track size and allowable attack angle. Also dynamic effects in vehicle turning are investigated to assess proper load torque. The ROBHAZ-DT3 system developed was successfully experimented in stair climbing case.

A limit-cycle is the phenomenon that can be observed in systems composed of nonlinear elements. The phenomenon is of fundamental importance in nonlinear systems and, as far as the design of a nonlinear system is concerned, it should be considered along with the stability analysis. In the paper, the limit-cycle of a system controlled by a fuzzy logic controller (FLC) is addressed via some of the classical control techniques used to analyze nonlinear systems in the frequency domain. First, reasonable assumptions are made on the structure of the FLC by using fuzzy basis functions (FBFs) and the describing function of the FLC is derived to analyze and predict the existence of the limit-cycle of the closed-loop system including the FLC. Finally computer simulation is performed to show how the analysis given in the paper is used to predict the existence of the limit-cycle of the fuzzy control system.

A direct model reference adaptive fuzzy control (MRAFC) scheme is developed for the plant model whose structure is represented by the Takagi-Sugeno model. The MRAFC scheme is proposed to provide asymptotic tracking of a reference signal for the systems with uncertain or slowly time-varying parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal. The suggested design technique is applied to tracking control of a two-link robot manipulator.

For robot teleoperation, a lot of research has been done to control the slave robot from a remote site using a masterarm. The concept of a distributed controller architecture for the masterarm is introduced in the paper. To reduce possible non-uniform time delay and to have higher position command update rate, the host controller with distributed satellite controllers is developed. Each satellite controller measures the corresponding joint angle, while the host controller performs initial calibration, inverse kinematics calculation, and position commands generation. The host controller and the satellite controllers are networked via a SPI (serial peripheral interface) protocol. For force feedback, the host controller solves the joint torque relations based on the kinematics of the masterarm and the satellite controller controls each joint's torque. This distributed controller greatly reduces calculation and control loads and makes the electrical wiring very simple. In order to measure finger movement of the operator, the master hand is developed using infrared light. A similar slave satellite controller for this master hand is interfaced to the host controller via a SPI network. For teleoperation of the KIST humanoid robot which has two arms and two hands, the masterarm controller with one host controller and 16 satellite controllers is implemented. Force feedback with less than 2 msec update rate is achieved. This masterarm/hand is also integrated with a graphic model instead of using the slave robot. The experimental results show the performance of the distributed controller.

In a variable structure control algorithm, sliding mode makes the closed loop system insensitive to modelling uncertainties and external disturbances. However due to imperfections in switching, the system trajectory chatters, which is very undesirable, and the insensitivity property of a sliding mode controller is present only when the system is in the sliding mode. To overcome this shortcoming, a sliding mode control algorithm using time-varying sliding surface and PID-like structure is proposed. This controller guarantees robustness during the whole period of control and removes chattering and also reduces steady state error of the system. To demonstrate its performance, the proposed control algorithm is applied to a two link robot manipulator.

This paper proposes a numerical stability analysis methodology for the singleton-type linguistic fuzzy control systems based on optimization techniques. First, it demonstrates that a singleton-type linguistic fuzzy logic controller (FLC) can be converted into a region-wise sector-bounded controller or, more generally, a polytopic system by quadratic programming (QP). Next, the convex optimization technique called linear matrix inequalities (LMI) is used to analyze the closed loop of the converted polytopic system. Finally, the applicability of the suggested methodology is highlighted via simulation results.

This paper proposes a new variable structure controller (VSC) for the accurate tracking control of a manipulator using the variable boundary layer. Up to now, VSC applying the variable boundary layer did not remove chattering from an arbitrary initial state of the system trajectory because VSC has used the fixed sliding surface. But, by using the linear time-varying sliding surfaces, the scheme has the robustness against chattering from all states. This scheme can be applied to the second-order nonlinear systems with parameter uncertainty and extraneous disturbances, and have better tracking performance than the conventional method. To demonstrate its performance, the proposed control algorithm is applied to a two-link manipulator.

A robust stability analysis and design method for a fuzzy feedback linearization regulator is presented. The well-known Takagi-Sugeno fuzzy model is used as the nonlinear plant model. Uncertainties and disturbance are assumed to be included in the model structure with known bounds. For these structured uncertainties, stability robustness of the closed system is analyzed in both input-output sense and Lyapunov sense. The robust stability conditions are proposed using multivariable circle criterion and the relationship between input-output stability and Lyapunov stability. Also, based on the stability analysis, a systematic design procedure for the fuzzy feedback linearization regulator is provided. The effectiveness of the proposed analysis and design method is illustrated by a simple example.

The objective of H/sub /spl infin// disturbance attenuation problem is to attenuate the effect of disturbance to the prescribed level and achieve performance robustness. Chen, Lee and Chang (Fuzzy Syst., vol.4, p.32-43, 1996) proposed an adaptive fuzzy H/sub /spl infin// disturbance attenuation algorithm. The fuzzy approximation error, which is influenced by the control input, is taken as the disturbance signal in the proposed algorithm. The authors argue that, because of the tradeoff between the attenuation level and the control input, performance robustness cannot be achieved by the proposed algorithm. The original authors agree that the approximation error will influence the performance robustness, especially in the case without external disturbance and with very small attenuation level. However, in practical control applications, very small attenuation levels are avoided in order to prevent high-gain control.

This paper presents an explanation of a fuzzy model considering the correlation among components of input data. Generally, fuzzy models have a capability of dividing an input space into several subspaces compared to a linear model. But hitherto suggested fuzzy modeling algorithms have not taken into consideration the correlation among components of sample data and have addressed them independently, which results in an ineffective partition of the input space. In order to solve this problem, this paper proposes a new fuzzy modeling algorithm, which partitions the input space more effectively than conventional fuzzy modeling algorithms by taking into consideration the correlation among components of sample data. As a way to use the correlation and divide the input space, the method of principal component is used. Finally, the results of the computer simulation are given to demonstrate the validity of this algorithm.

The authors state that in the above paper (Tanaka et al., 1996), the derived stability analysis cannot guarantee the norm requirement for necessity and the selection of the G matrix can affect the stability analysis. Therefore, the derived stability analysis cannot avoid conservative results. They illustrate this fact using a simple example and propose a modified stability theorem.

This paper proposes a new approach to fuzzy modeling. The suggested fuzzy model can express a given unknown system with a few fuzzy rules as well as Takagi and Sugeno's model (1985), because it has the same structure as that of Takagi and Sugeno's model. It is also as easy to implement as Sugeno and Yasukawa's model (1993) because its identification mimics the simple identification procedure of Sugeno and Yasukawa's model. The suggested algorithm is composed of two steps: coarse tuning and fine tuning. In coarse tuning, fuzzy C-regression model (FCRM) clustering is used, which is a modified version of fuzzy C-means (FCM). In fine tuning, gradient descent algorithm is used to precisely adjust parameters of the fuzzy model instead of nonlinear optimization methods used in other models. Finally, some examples are given to demonstrate the validity of this algorithm.

A new local path planning algorithm using DPH (distance profile histogram) is suggested in this paper. The proposed method makes a grid type world map using distance values acquired from multiple ultrasonic sensors and generates local points through which the mobile robot can avoid obstacles safely. The DPH (distance profile histogram) represents geometrical arrangement of obstacles around the robot in a local polar coordinate system which is assumed to be attached to the robot. To control the robot's navigation a three-layered control structure is adopted. The validity of the proposed method is demonstrated through several experiments.

This paper suggests a new fuzzy adaptive controller, which is able to solve the problems of classical adaptive controllers and conventional fuzzy adaptive controllers. It explains the architecture of a fuzzy adaptive controller using the robust property of a fuzzy controller. The basic idea of new adaptive control scheme is that an adaptive controller can be constructed with parallel combination of robust controllers. This new adaptive controller uses a multirule-base architecture which has several independent fuzzy controllers in parallel, each with different robust stability area. Out of several independent fuzzy controllers, the most suited one is selected by a system identifier which observes variations in the controlled system parameter. Here, we propose a design procedure which can be carried out mathematically and systematically from the model of a controlled system; related mathematical theorems and their proofs are also given. The performance of the proposed adaptive control algorithm is analyzed through a design example and a DC motor control simulation.

In this paper, we suggest a method of deciding the PD gains of a stiffness controller using an identification method based on the Takagi-Sugeno's fuzzy model. It is difficult to perform a compliance task due to the characteristics of the robot itself and an uncertain work environment. Therefore, in this paper, we identify a fuzzy model by dividing the relationship of input-output data into several piecewise-linear equations using the Hough transform, which is an image processing method, and by fine-tuning the parameters of the fuzzy model using the gradient-descent method. By using this fuzzy model, we propose a method of designing the PD gains of the stiffness controller. Finally we show the validity of this method by a surface tracking experiment using a paper box.

A fuzzy adaptive controller, which is able to solve the problems of classical adaptive controllers and conventional fuzzy adaptive controllers, is suggested in this paper. The suggested design method is applied to hybrid robot control. The architecture of a fuzzy adaptive controller using the robust property of a fuzzy controller is explained. A design procedure which can be carried out mathematically and systematically from the model of an objective system is suggested. This fuzzy adaptive controller is used in the control algorithm which is adaptive to the parametric uncertainty in hybrid force/position robot system, and its practicality is confirmed by an experiment on hybrid force/position robot system.

A fuzzy adaptive controller which is able to solve the problems of classical adaptive controllers and conventional fuzzy adaptive controller is suggested. The architecture of a fuzzy adaptive controller using the robust property of a fuzzy controller is explained. A design procedure which can be carried out mathematically and systematically from the model of an objective system is suggested, and related mathematical theorems and their proofs are also given. The performance of the proposed adaptive control algorithm is analyzed through a DC motor control simulation and experiments on a compliant robot system.

In recent years, lots of researches on autonomous mobile robot have been accomplished. However they focused on environment recognition and its processing to make a decision on the motion. And cooperative multy-robot, which must be able to avoid crash and to make mutual communication, has not been studied much. This paper deals whth cooperative motion of two robots, Meari I and Meari 2 made in our laboratory, based on communication between the two. Because there is an interference on communication occurring in cooperative motion of multi-robot, many restrictive conditions are required. Therefore, lie have designed these robot system so that communication between them is available and mutual interference is precluded, and lie used fuzzy interference to overcome unstability of sensor data.

Proposes a compliance robot control algorithm using fuzzy adaptive controller and fuzzy compliance vector generator. In the compliance robot control, one needs more adaptivity because the linear modeling in robot dynamics is getting more difficult by contact with external environment. Existing adaptive controllers have difficulty in realtime processing. So in order to overcome it, the authors adopt fuzzy adaptive controller and propose fuzzy compliance vector generator for flexible compliant motion. They analyze and confirm the proposed algorithm by surface processing experiment with a control system implemented by VME system.>

Presents a new shadow mask inspection method which can detect the very small hole defects reliably and rapidly; the shadow mask line image data is periodical. The inspection method mainly depends on the fact that a small defect can be significantly emphasized in terms of the comparison between the image pixels departed by the period. In this paper, this comparison is implemented as the digital filtering algorithms, which can be rapidly processed by a DSP chip. In terms of the high speed line image sensing and fast algorithm by DSP, the whole inspection scheme sufficiently supports the real time shadow mask handing. From the experimental results, this scheme shows a reliable inspection capability for all types of individual hole defects.>