• Journal of Institute of Control, Robotics and Systems
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• v.7 no.11
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• pp.933-938
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• 2001

In this paper, we describe a method for the mobile robot using images of a moving object. This method combines the observed position from dead-reckoning sensors and the estimated position from the images captured by a fixed camera to localize a mobile robot. Using the a priori known path of a moving object in the world coordinates and a perspective camera model, we derive the geometric constraint equations which represent the relation between image frame coordinates for a moving object and the estimated robot`s position. Since the equations are based on the estimated position, the measurement error may exist all the time. The proposed method utilizes the error between the observed and estimated image coordinates to localize the mobile robot. The Kalman filter scheme is applied to this method. Effectiveness of the proposed method is demonstrated by the simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1667-1670
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• 1997

We consider the probelem of identifying and underwater vehicle. It is assumed that a priori information about the parameteric model structure and values of the hydrodynamic coefficients is available from some other schemes. The concept of relative esnsitivity is introduced to plan and efficinet identification procedure. An analysis of the sensitivity of the overall system to a particular hydrodynamic coefficinet provides a tool to evaluate the relative importance of the same coefficient in a particular maneuver. Then it can be made possible to reduce the filter size by selecting some dominatn hydrodynamic coefficients as parameters to be estimated for a given maneuver, and this fact may be used for establishing a gradual identification scheme. The main merit of a gradual identification is substantially reduced computer burden with increased nimerical stability. An illustrative simualtion result is given.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.11
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• pp.116-122
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• 1998

We propose a spatially adaptive image interpolation algorithm, which can restore high frequency details in the original high resolution image. In order to apply the regularization approach to the interpolation procedure, we first present a two-dimensional separable image degradation model for a low resolution imaging system. According to the model, we propose a regularized spatially adaptive interpolation algorithm by using five different constraints. We also analyze convergence of the proposed algorithm, and provide some experimental results to compare the proposed algorithm with its nonadaptive version.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.12
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• pp.1306-1316
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• 1990

A controller using a multilayer neural network is proposed to the dynamic control of a PUMA 560 robot arm. This controller is developed based on an error back-propagation (BP) neural network. Since the neural network can model an arbitrary nonlinear mapping, it is used as a commanded feedforward torque generator. A Proportional Derivative (PD) feedback controller is used in parallel with the feedforward neural network to train the system. The neural network was trained by the current state of the manipulator as well as the PD feedback error torque. No a priori knowledge on system dynamics is needed and this information is rather implicitly stored in the interconnection weights of the neural network. In another experiment, the neural network was trained with the current, past and future positions only without any use of velocity sensors. Form this thim window of position values, BP network implicitly filters out the velocity and acceleration components for each joint. Computer simulation demonstrates such powerful characteristics of the neurocontroller as adaptation to changing environments, robustness to sensor noise, and continuous performance improvement with self-learning.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.5
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• pp.420-427
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• 2010

This paper describes a localization method based on Monte Carlo Localization approach for a mobile robot. The method uses range data which are measured from ultrasound transmitting beacons whose locations are given a priori. The ultrasound receiver on-board a robot detects the range from the beacons. The method requires several beacons, theoretically over three. The method proposes a sensor model for the range sensing based on statistical analysis of the sensor output. The experiment uses commercialized beacons and detector which are used for trilateration localization. The performance of the proposed method is verified through real implementation. Especially, it is shown that the performance of the localization degrades as the sensor update rate decreases compared with the MCL algorithm update rate. Though the method requires exact location of the beacons, it doesn't require geometrical map information of the environment. Also, it is applicable to estimation of the location of both the beacons and robot simultaneously.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.9-14
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• 2006

Galileo is the European Global Navigation Satellite System, under civilian control, and consists on a constellation of medium Earth orbit satellites and its associated ground infrastructure. Galileo will provide to their users highly accurate global positioning services and their associated integrity information. The elements in charge of the computation of Galileo navigation and integrity information are the OSPF (Orbit Synchronization Processing Facility) and IPF (Integrity Processing Facility), within the Galileo Ground Mission Segment (GMS). Navigation algorithms play a key role in the provision of the Galileo Mission, since they are responsible for computing the essential information the users need to calculate their position: the satellite ephemeris and clock offsets. Such information is generated in the Galileo Ground Mission Segment and broadcast by the satellites within the navigation signal, together with the expected a-priori accuracy (SISA: Signal-In-Space Accuracy), which is the parameter that in fault-free conditions makes the overbounding the predicted ephemeris and clock model errors for the Worst User Location. In parallel, the integrity algorithms of the GMS are responsible of providing a real-time monitoring of the satellite status with timely alarm messages in case of failures. The accuracy of the integrity monitoring system is characterized by the SISMA (Signal In Space Monitoring Accuracy), which is also broadcast to the users through the integrity message.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1589-1592
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• 1997

Among the prerequisite abilities (perception of environment, path planning and position estimation) of an autonomous mobile robot, position estimation has been seldom studied by mobile robot researchers. In most cases, conventional positioin estimation has been performed by placing landmarks or giving the entrire environmental information in advance. Unlikely to the conventional ones, the study addresses a new method that the robot itself can select distinctive features in the environment and save them as landmarks without any a priori knowledge, which can maximize the autonomous behavior of the robot. First, an orjentaion probaility model is applied to construct a lcoal map of robot's surrounding. The feature of the object in the map is then extracted and the map is saved as landmark. Also, presented is the position estimation method that utilizes the correspondence between landmarks and current local map. In dong this, the uncertainty of the robot's current positioin is estimated in order to select the corresponding landmark stored in the previous steps. The usefulness of all these approaches are illustrated with the results porduced by a real robot equipped with ultrasonic sensors.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.41.4-41
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• 2002

In this paper we deal with visual servoing that can control a robot arm with a camera using information of images only, without estimating 3D position and rotation of the robot arm. Here it is assumed that the robot arm is calibrated and the camera is uncalibrated. Here we consider two coordinate systems, the world coordinate system and the camera coordinate one and we use a pinhole camera model as the camera one. First of all, the essential notion can be show, that is, epipolar geometry, epipole, epipolar equation, and epipolar constrain. And these plays an important role in designing visual servoing in the later chapters. Statement of the problem is giver. Provided two a priori...

• The Journal of the Acoustical Society of Korea
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• v.24 no.3E
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• pp.87-89
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• 2005

The acoustical sources of intake and exhaust systems in fluid machines are often characterized by the source impedance and strength using linear frequency-domain modeling. In the case of the sources which are nonlinear and time-variant, however, the source parameters were sometimes incorrectly obtained. In this paper, the source model and direct measurement technique are modified in order to evaluate the effect due to nonlinear and periodically time-varying source character as well as the linear property of the reflectivity of in-duct fluid machine source. With a priori known kinematical information of the source, the types of nonlinear time-variant terms can be presumed by a simple physical model, in which there is practically no restriction on the form of the model. The concept of source impedance can be extendable by introducing the linear frequency response function for each nonlinear or time-variant input. Extending the conventional method and adapting the reverse MISO technique, it is possible to develop a direct method that can deal with the nonlinear time-variant source parameters. The proposed direct method has a novel feature that there is no restriction on the probability or spectral natures of the excited sound pressure data. The present method is verified by the simulated measurements for simplified fluid machines. It is thought that the proposed method would be useful in predicting the insertion loss or the radiated sound level from intake or exhaust systems.

• ETRI Journal
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• v.34 no.5
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• pp.684-689
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• 2012

A mode-singular-value-decomposition (SVD) maximum likelihood (ML) estimation procedure is proposed for the source localization problem under an additive measurement error model. In a practical situation, the noise variance is usually unknown. In this paper, we propose an algorithm that does not require the noise covariance matrix as a priori knowledge. In the proposed method, the weight is derived by the inverse of the noise magnitude square in the ML criterion. The performance of the proposed method outperforms that of the existing methods and approximates the Taylor-series ML and Cram$\acute{e}$r-Rao lower bound.