Ji-Wook Kwon

Hierarchical Formation Control Based on a Vector Field Method for Wheeled Mobile Robots

This paper proposes a hierarchical formation control using a target tracking control law based on the vector field method such that a decentralized and flexible formation control can be achieved without additional motion planning. Previously, many researchers have dealt with the control laws for the rigid formation, where the line of sight toward the leader is controlled for the leader-follower formation control. However, a width change or a collision of the formation can occur since a limited motion of the rigid formation can occur when the formation control maintains the line of sight. Therefore, the formation of multiple mobile robots is required to be flexible, keeping the width and curvature of the formation. To this end, a formation control law based on a vector field method is proposed, and a hierarchical formation structure is introduced in such a way that it consists of a line formation and a column formation based on the leader-follower formation strategy. First, a subgroup, which consists of several robots, is generated using the line formation, and then, the overall formation structure is constructed from several subgroups using the column formation. Finally, we show the stability of the whole formation. The stability analysis and simulation results of the proposed hierarchical formation control using this vector field method are included to demonstrate the practical applicability of the proposed method.

Polynomial fuzzy modeling and tracking control of wheeled mobile robots via sum of squares approach

This paper proposes the polynomial fuzzy modeling and tracking control methods for wheeled mobile robots by using sum of squares (SOS) approach, which is developed as SOSTOOL under the Matlab environment. Due to the polynomial fuzzy modeling, we can obtain the linearized tracking error dynamics such that both LMI (Linear Matrix Inequality) and SOS approaches can be applied. Since SOS approach handles more nonlinear system characteristics than LMI, we can obtain the better tracking performance, which is demonstrated in the numerical simulations. The proposed method has advantages in that the control structure can be simplified and it can be further extended to accommodate the input saturation, disturbance compensation, etc., which is well developed for LMI control methods.

Vector field trajectory tracking control for wheeled mobile robots

This paper proposes a novel vector field control method based on nonlinear control algorithm for wheeled-mobile robots. A new vector field control method is proposed for wheeled-mobile robots. In the proposed method, the controller is designed to asymptotically stabilize the tracking errors in both position and orientation angle. By the proposed controller, trajectory tracking can be achieved for arbitrary reference trajectory even with large initial tracking errors and bounded disturbances. The performance analysis is performed and also simulations are included to confirm the effectiveness of the proposed scheme.

Experimental Study on Camera Calibration and Pose Estimation for the Application to Vehicle's Wheel Alignment

In this paper, we discuss about the experimental results of camera calibration and pose estimation for the application to vehicles wheel alignment system using single camera. At first, we define a camera model for the camera system considered. For this model which consists of a linear part (ideal pinhole camera model) and a nonlinear part (lens distortion model), we drive a systematic and iterative calibration algorithm and pose estimation algorithm. In addition, we develop a coordinate matching algorithm which calculates matched sequence of order in both extracted image coordinate and object coordinate for non-interactive calibration and pose estimation. Based on the results of calibration, we also develop a camera simulator to confirm the results of calibration and compare the results of simulations with those of experiments in pose estimation

Target Tracking of the Wheeled Mobile Robot using the Combined Visual Servo Control Method

This paper proposes a target tracking algorithm for wheeled mobile robots using in various fields. For the stable tracking, we apply a vision system to a mobile robot which can extract targets through image processing algorithms. Furthermore, this paper presents an algorithm to position the mobile robot at the desired location from the target by estimating its relative position and attitude. We show the problem in the tracking method using the Position-Based Visual Servo(PBVS) control, and propose a tracking method, which can achieve the stable tracking performance by combining the PBVS control with Image-Based Visual Servo(IBVS) control. When the target is located around the outskirt of the camera image, the target can disappear from the field of view. Thus the proposed algorithm combines the control inputs with of the hyperbolic form the switching function to solve this problem. Through both simulations and experiments for the mobile robot we have confirmed that the proposed visual servo control method is able to enhance the stability compared to of the method using only either PBVS or IBVS control method.

Localization of the mobile agent using indirect Kalman filter in distributed sensor networks

This paper proposes a localization algorithm for mobile agents in sensor networks using the data fusion method based on indirect Kalman filter. The fusion method of an inertial navigation sensor and sensor nodes data is used to acquire the correct position information. Since indirect Kalman filter is used for estimation of the mobile agent position, the position of the mobile agent can be estimated even when the sensor node cannot work or data acquired from the network are not reliable. Because measurement errors are used for estimation, its implementation is easy. Finally, a sequence of simulations is carried out to confirm the performance of the proposed localization algorithm.

Localization of Outdoor Wheeled Mobile Robots using Indirect Kalman Filter Based Sensor fusion

This paper presents a localization algorithm of the outdoor wheeled mobile robot using the sensor fusion method based on indirect Kalman filter(IKF). The wheeled mobile robot considered with in this paper is approximated to the two wheeled mobile robot. The mobile robot has the IMU and encoder sensor for inertia positioning system and GPS. Because the IMU and encoder sensor have bias errors, divergence of the estimated position from the measured data can occur when the mobile robot moves for a long time. Because of many natural and artificial conditions (i.e. atmosphere or GPS body itself), GPS has the maximum error about when the mobile robot moves for a short time. Thus, the fusion algorithm of IMU, encoder sensor and GPS is needed. For the sensor fusion algorithm, we use IKF that estimates the errors of the position of the mobile robot. IKF proposed in this paper can be used other autonomous agents (i.e. UAV, UGV) because IKF in this paper use the position errors of the mobile robot. We can show the stability of the proposed sensor fusion method, using the fact that the covariance of error state of the IKF is bounded. To evaluate the performance of proposed algorithm, simulation and experimental results of IKF for the position(x-axis position, y-axis position, and yaw angle) of the outdoor wheeled mobile robot are presented.

Vision-based corridor line detection using k-means algorithm

We propose a corridor lines extraction algorithm. The algorithm is based on a vision-system. It uses the patterns of slopes between two points, K-means, and least-square method. The proposed algorithm offers intersection lines between the wall and the floor. The proposed method consists of image preprocessing, noise filtering, and line extraction algorithm stage. The method has been tested on a large number of real corridor images. The experimental results are included to demonstrate the validity of the proposed algorithm.

Object detection of mobile robot using data-mining algorithm

This paper proposes a data-mining algorithm for mobile robots in an object detection. It is important that is used to localization and map building. The proposed method uses three algorithms to divide sections. To this end, the laser scanner is used to recognized objects for mobile robots. To control the mobile robots in the researches of collision avoidance, we can recognize approximation of objects. By using data-mining algorithm, the processing speed is faster and more correct. Simulation results are included to demonstrate the validity of the proposed algorithm.

Wavefront Method-Based Local-Path Planning for a Mobile Robot with a Vision System

This paper proposes a local path planning algorithm for a mobile robot with a vision system. Based on projective geometry and the wavefront-based skeleton method, the proposed algorithm performs obstacle extraction, 2D-3D transformation, and local path planning. The effectiveness of the proposed method is illustrated by experiments where a vision-equipped mobile robot navigates a maze using the proposed path planning algorithm