• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.1
• /
• pp.34-39
• /
• 2015

In this paper, a finite impulse response (FIR) filter is proposed for discrete-time nonlinear systems. The proposed filter is designed by combining the estimate of the perturbation state and nominal state. The perturbation state is estimated by adapting the optimal time-varying FIR filter for the linearized perturbation model and the nominal state is directly obtained from the nonlinear nominal trajectory model. Since the FIR structured estimators use the finite horizon information on the most recent time interval, the proposed extended FIR filter satisfies the bounded input/bounded output (BIBO) stability, which can't be obtained from infinite impulse response (IIR) estimators. Thus, it can be expected that the proposed extended FIR filter is more robust than IIR structured estimators such as an extended Kalman filter for the round-of errors and the uncertainties from unknown initial states and uncertain system model parameters. The simulation results show that the proposed filter has better performance than the extended Kalman filter (EKF) in both robustness and fast convergency.

• Journal of KIISE:Software and Applications
• /
• v.29 no.5
• /
• pp.319-325
• /
• 2002

Tracking moving objects is one of the most important techniques in motion analysis and understanding, and it has many difficult problems to solve. Especially, estimating and identifying moving objects, when the background and moving objects vary dynamically, are very difficult. It is possible under such a complex environment that targets may disappear totally or partially due to occlusion by other objects. The Kalman filter has been used to estimate motion information and use the information in predicting the appearance of targets in succeeding frames. In this paper, we propose another version of the Kalman filter, to be called structured Kalman filter, which can successfully work its role of estimating motion information under a deteriorating condition such as occlusion. Experimental results show that the suggested approach is very effective in estimating and tracking non-rigid moving objects reliably.

• 제어로봇시스템학회:학술대회논문집
• /
• 1987.10b
• /
• pp.556-561
• /
• 1987

This paper presents a suboptimal Kalman filter design method for the problem of tracking a maneuvering target. The design method is essentially based on linear target dynamics and linear-like structured measurements called pseudomeasurements. The pseudomeasurements are obtained by manipulating the original nonlinear measurements algebraically. The resulting filter has computational advantages over other filters with similar performance. Monte Carlo computer simulation results are included to demonstrate the effectiveness of the proposed suboptimal filter associated with the target acceleration model.

• Journal of Institute of Control, Robotics and Systems
• /
• v.11 no.12
• /
• pp.1068-1076
• /
• 2005

In order to increase the autonomous navigation capability of a mobile robot, it is very crucial to develop a method for the robot to be able to recognize a priori hon structured environmental characteristics. This paper proposes an ultrasonic sensor based real-time method for recognizing a priori known structured indoor environmental characteristics like a wall and comer Unlike the methods reported in the literature the information obtained from the sensor can be processed in real-time by extended Kalman filter to update estimations of the position and orientation of robot with respect to known environmental characteristics.

• The Journal of Korea Robotics Society
• /
• v.6 no.2
• /
• pp.182-188
• /
• 2011

This paper describes a method of road tracking by using a vision and laser with extracting road boundary (road lane and curb) for navigation of intelligent transport robot in structured road environments. Road boundary information plays a major role in developing such intelligent robot. For global navigation, we use a global positioning system achieved by means of a global planner and local navigation accomplished with recognizing road lane and curb which is road boundary on the road and estimating the location of lane and curb from the current robot with EKF(Extended Kalman Filter) algorithm in the road assumed that it has prior information. The complete system has been tested on the electronic vehicles which is equipped with cameras, lasers, GPS. Experimental results are presented to demonstrate the effectiveness of the combined laser and vision system by our approach for detecting the curb of road and lane boundary detection.

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.9
• /
• pp.807-810
• /
• 2015

In this paper, a finite impulse response(FIR) fixed-lag smoother is proposed for discrete-time nonlinear systems. If the actual state trajectory is sufficiently close to the nominal state trajectory, the nonlinear system model can be divided into two parts: The error-state model and the nominal model. The error state can be estimated by adapting the optimal time-varying FIR smoother to the error-state model, and the nominal state can be obtained directly from the nominal trajectory model. Moreover, in order to obtain more robust estimates, the linearization errors are considered as a linear function of the estimation errors. Since the proposed estimator has an FIR structure, the proposed smoother can be expected to have better estimation performance than the IIR-structured estimators in terms of robustness and fast convergence. Additionally the proposed method can give a more general solution than the optimal FIR filtering approach, since the optimal FIR smoother is reduced to the optimal FIR filter by setting the fixed-lag size as zero. To illustrate the performance of the proposed method, simulation results are presented by comparing the method with an optimal FIR filtering approach and linearized Kalman filter.

• The Journal of Korea Robotics Society
• /
• v.8 no.1
• /
• pp.37-42
• /
• 2013

In this paper, a localization algorithm and an autonomous controller for PETASUS system II which is an underwater vehicle-manipulator system, are proposed. To estimate its position and to identify manipulation targets in a structured environment, a multi-rate extended Kalman filter is developed, where map information and data from inertial sensors, sonar sensors, and vision sensors are used. In addition, a three layered control structure is proposed as a controller for autonomy. By this controller, PETASUS system II is able to generate waypoints and make decisions on its own behaviors. Experiment results are provided for verifying proposed algorithms.

• Smart Structures and Systems
• /
• v.9 no.3
• /
• pp.273-286
• /
• 2012

As civil structures are exposed to various external loads, it is essential to assess the structural condition, especially the structural displacement, in every moment. Therefore, a visually servoed paired structured light system was proposed in the previous study. The proposed system is composed of two screens facing with each other, each with a camera, a screen, and one or two lasers controlled by a 2-DOF manipulator. The 6-DOF displacement can be calculated from the positions of three projected laser beams and the rotation angles of the manipulators. In the estimation process, one of well-known iterative methods such as Newton-Raphson or extended Kalman filter (EKF) was used for each measurement. Although the proposed system with the aforementioned algorithms estimates the displacement with high accuracy, it takes relatively long computation time. Therefore, an incremental displacement estimation (IDE) algorithm which updates the previously estimated displacement based on the difference between the previous and the current observed data is newly proposed. To validate the performance of the proposed algorithm, simulations and experiments are performed. The results show that the proposed algorithm significantly reduces the computation time with the same level of accuracy compared to the EKF with multiple iterations.

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.6
• /
• pp.579-583
• /
• 2012

The purpose of this paper is to suggest IDE (Incremental Displacement Estimation) algorithm for the previously proposed visually servoed paired structured light system. The system is composed of two sides facing with each other, each with one or two lasers with a 2-DOF manipulator, a camera, and a screen. The 6-DOF displacement between two sides can be estimated by calculating the positions of the projected laser beams and rotation angles of the manipulators. In the previous study, Newton-Raphson or EKF (Extended Kalman Filter) has been used as an estimation algorithm. Although the various experimental tests have validated the performance of the system and estimation algorithms, the computation time is relatively long since aforementioned algorithms are iterative methods. Therefore, in this paper, a non-iterative incremental displacement estimation algorithm which updates the previously estimated displacement with a difference of the previous and the current observed data is introduced. To verify the performance of the algorithm, experimental tests have been performed. The results show that the proposed non-iterative algorithm estimates the displacement with the same level of accuracy compared to the EKF with multiple iterations with significantly less computation time.

• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.5
• /
• pp.434-443
• /
• 2007

Presently, the exploration of an unknown environment is an important task for thee new generation of mobile service robots and mobile robots are navigated by means of a number of methods, using navigating systems such as the sonar-sensing system or the visual-sensing system. To fully utilize the strengths of both the sonar and visual sensing systems. This paper presents a technique for localization of a mobile robot using fusion data of multi-ultrasonic sensors and vision system. The mobile robot is designed for operating in a well-structured environment that can be represented by planes, edges, comers and cylinders in the view of structural features. In the case of ultrasonic sensors, these features have the range information in the form of the arc of a circle that is generally named as RCD(Region of Constant Depth). Localization is the continual provision of a knowledge of position which is deduced from it's a priori position estimation. The environment of a robot is modeled into a two dimensional grid map. we defines a vision-based environment recognition, phisically-based sonar sensor model and employs an extended Kalman filter to estimate position of the robot. The performance and simplicity of the approach is demonstrated with the results produced by sets of experiments using a mobile robot.