• Journal of the Korean Society for Precision Engineering
• /
• v.23 no.5 s.182
• /
• pp.77-84
• /
• 2006

This paper describes the design and implementation of a robot's gripper control system. In order to safely grasp an unknown object using the robot's gripper, the gripper should detect the force of gripping direction and the force of gravity direction, and should perform the force control using the detected forces and the robot's gripper control system. In this paper, the robot's gripper control system is designed and manufactured using DSP(Digital Signal Processor), and the gripper is composed of two 6-axis force/moment sensors which measures the Fx force(force of x-direction), Fy force, Fz force, and the Mx moment(moment of x-direction), My moment, Mz moment at the same time. The response characteristic test of the system is performed to determine the proportional gain Kp and the integral gain Ki of PI controller. As a result, it is shown that the developed robot's gripper control system grasps an unknown object safely.

• Journal of Sensor Science and Technology
• /
• v.5 no.4
• /
• pp.57-70
• /
• 1996

Motion can be estimated continuously from each sensor through the analysis of the instantaneous states of an object. This paper is aimed to introduce a method to estimate the general 3D motion of a planar object from the instantaneous states of an object using multi-sensor data fusion. The instantaneous states of an object is estimated using the linear feedback estimation algorithm. The motion estimated from each sensor is fused to provide more accurate and reliable information about the motion of an unknown planar object. We present a fusion algorithm which combines averaging and deciding. With the assumption that the motion is smooth, the approach can handle the data sequences from multiple sensors with different sampling times. Simulation results show proposed algorithm is advantageous in terms of accuracy, speed, and versatility.

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.6
• /
• pp.175-182
• /
• 1998

An advanced visual tracking and stable grasping algorithm for a moving object is proposed. The stable grasping points for a moving 2D polygonal object are obtained through the visual tracking system with the Kalman filter and image prediction technique. The accuracy and efficiency are improved more than any other prediction algorithms for the tracking of an object. In the processing of a visual tracking. the shape predictors construct the parameterized family and grasp planner find the grasping points of unknown object through the geometric properties of the parameterized family. This algorithm conducts a process of ‘stable grasping and real time tracking’.

• 한국전산유체공학회:학술대회논문집
• /
• 2008.03b
• /
• pp.576-579
• /
• 2008

Electrical impedance(EIT) for the multi-phase flow visualization is an imaging modality in which the resistivity distribution of the unknown object is estimated based on the known sets of injected currents and measured voltages on the surface of the object. In this paper, an EIT reconstruction algorithm based on the extended Kalman filter(EKF) is proposed. The EIT reconstruction problem is formulated as a dynamic model which is composed of the state equation and the observation equation, and the unknown resistivity distribution is estimated recursively with the aid of the EKF. To verify the reconstruction performance of the proposed algorithm, experiments with simulated multi-phase flow are performed.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1044-1047
• /
• 2005

In this paper, we present the strategy of object search for distributed autonomous robotic systems (DARS). The DARS are the systems that consist of multiple autonomous robotic agents to whom required functions are distributed. For instance, the agents should recognize their surrounding at where they are located and generate some rules to act upon by themselves. In this paper, we introduce the strategy for multiple DARS robots to search a hidden object at the unknown area. First, we present an area-based action making process to determine the direction change of the robots during their maneuvers. Second, we also present Q learning adaptation to enhance the area-based action making process. Third, we introduce the coordinate system to represent a robot's current location. In the end of this paper, we show experimental results using hexagon-based Q learning to find the hidden object.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.17 no.9
• /
• pp.1040-1049
• /
• 1992

When the model of control object is not described correctly, ambiguity is often expressed by unknown parameter, In a case that this ambiguity satisfies a certain condition of limit, if robust control method is used, even if model is not correctly discribed, control system can be composed. The characteristic of control based on the variable structure theory is that the influence by ambiguity of system eradicates high-gain feedback. Therefore in this paper, VSS indentifier is proposed. Transformation of control input producing control system in sliding mode actually reflects influence of ambiguity unknown parameter of control object. If useful information is out from transformation input by a few times of operation, proper identify mechanism is selected and this information is used, to decide the unknown parameter is possible. So more effective controller was composed by addition of the proposed identifier to the unknown parameter identifier of robot manipulator.

• Journal of Mechanical Science and Technology
• /
• v.17 no.12
• /
• pp.1876-1885
• /
• 2003

The artificial potential field (APF) methods provide simple and efficient motion planners for practical purposes. However, these methods have a local minimum problem, which can trap an object before reaching its goal. The local minimum problem is sometimes inevitable when an object moves in unknown environments, because the object cannot predict local minima before it detects obstacles forming the local minima. The avoidance of local minima has been an active research topic in the potential field based path planing. In this study, we propose a new concept using a virtual obstacle to escape local minima that occur in local path planning. A virtual obstacle is located around local minima to repel an object from local minima. We also propose the discrete modeling method for the modeling of arbitrary shaped objects used in this approach. This modeling method is adaptable for real-time path planning because it is reliable and provides lower complexity.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.712-715
• /
• 1997

In image jacobian based visual servoing, generally, inverse jacobian should be calculated by complicated coordinate transformations. These are required excessive computation and the singularity of the image jacobian should be considered. This paper presents a visual servoing to control the pose of the robotic manipulator for tracking and grasping 3-D moving object whose pose and motion parameters are unknown. Because the object is in motion tracking and grasping must be done on-line and the controller must have continuous learning ability. In order to estimate parameters of a moving object we use the kalman filter. And for tracking and grasping a moving object we use a fuzzy inference based reinforcement learning algorithm of dynamic recurrent neural networks. Computer simulation results are presented to demonstrate the performance of this visual servoing

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2009.01a
• /
• pp.547-550
• /
• 2009

The object recognition mechanism of human being is not well understood yet. On research of animal experiment using an ape, however, neurons that respond to simple shape (e.g. circle, triangle, square and so on) were found. And Hypothesis has been set up as human being may recognize object as combination of such simple shapes. That mechanism is called Figure Alphabet Hypothesis, and those simple shapes are called Figure Alphabet. As one way to research object recognition algorithm, we focused attention to this Figure Alphabet Hypothesis. Getting idea from it, we proposed the feature extraction algorithm for object recognition. In this paper, we described recognition of binarized images of multifont alphabet characters by the recognition model which combined three-layered neural network in the feature extraction algorithm. First of all, we calculated the difference between the learning image data set and the template by the feature extraction algorithm. The computed finite difference is a feature quantity of the feature extraction algorithm. We had it input the feature quantity to the neural network model and learn by backpropagation (BP method). We had the recognition model recognize the unknown image data set and found the correct answer rate. To estimate the performance of the contriving recognition model, we had the unknown image data set recognized by a conventional neural network. As a result, the contriving recognition model showed a higher correct answer rate than a conventional neural network model. Therefore the validity of the contriving recognition model could be proved. We'll plan the research a recognition of natural image by the contriving recognition model in the future.

• Journal of KIISE:Software and Applications
• /
• v.31 no.4
• /
• pp.411-419
• /
• 2004

When the imaging object rotates in image plane during MRI scan, its rotation causes phase error and non-uniform sampling to MRI signal. The model of the problem including phase error non-uniform sampling of MRI signal showed that the MRI signals corrupted by rotations about an arbitrary center and the origin in image plane are different in their phases. Therefore the following methods are presented to improve the quality of the MR image which includes the artifact. The first, assuming that the angle of 2-D rotational motion is already known and the position of 2-D rotational center is unknown, an algorithm to correct the artifact which is based on the phase correction is presented. The second, in case of 2-D rotational motion with unknown rotational center and unknown rotational angle, an algorithm is presented to correct the MRI artifact. At this case, the energy of an ideal MR image is minimum outside the boundary of the imaging object to estimate unknown motion parameters and the measured energy increases when the imaging object has an rotation. By using this property, an evaluation function is defined to estimate unknown values of rotational angle at each phase encoding step. Finally, the effectiveness of this presented techniques is shown by using a phantom image with simulated motion and a real image with 2-D translational shift and rotation.