• Proceedings of the KSME Conference
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• 2000.04a
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• pp.523-528
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• 2000

In this paper, the new type biped walking robot which is composed of the minimum number or links just for walking and its appropriate gaits are proposed. The proposed new gaits for this robot are four-crossing, crawling, standing and turning gait. In designing the biped robot we propose the Performance Index which means the needed torque per a moving distance and generate foot trajectories by $3^{rd}$ order spline Interpolation. Among those, numerically we find the optimal conditions which minimize the Performance Index. Dynamically stable walking of the biped robot is realized by satisfying the stability condition of ZMP(zero moment point), which is related to maintaining the ZMP within the region of the supporting foot during the s1n91e leg support phase. We determine the region of mass center from the stability condition of ZMP and plan references which track the mass conte. trajectory of constant velocity. Finally we implement the gaits statically tracking the planned trajectories using PD control method.

• Journal of Drive and Control
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• v.15 no.3
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• pp.1-7
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• 2018

Humanoid robots have fascinated many researchers since they appeared decades ago. For the requirement of both accurate tracking control and the safety of physical human-robot interaction, torque control is basically desirable for humanoid robots. Humanoid robots are highly nonlinear, coupled, complex systems, accordingly the calculation of robot model is difficult and even impossible if precise model of the humanoid robots are unknown. Therefore, it is difficult to control using traditional model-based techniques. To realize model-free torque control, time-delay control (TDC) for humanoid robot was proposed with time-delay estimation technique. Using optimal walking trajectory obtained by particle swarm optimization, TDC with proposed scheme is implemented on whole body of a humanoid, not on biped legs even though it is performed by a virtual humanoid robot. The simulation results show the validity of the proposed TDC for humanoid robots.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.5
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• pp.249-257
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• 2023

This paper discusses the increasing need for autonomous delivery robots due to the current growth in the delivery market, rising delivery fees, high costs of hiring delivery personnel, and the need for contactless services. Additionally, the cost of hardware and complex software systems required to build and operate autonomous delivery robots is high. To provide a low-cost alternative to this, this paper proposes a autonomous delivery robot platform using a low-cost sensor combination of 2D LIDAR, depth camera and tracking camera to replace the existing expensive 3D LIDAR. The proposed robot was developed using the RTAB-Map SLAM open source package for 2D mapping and overcomes the limitations of low-cost sensors by using the convex hull algorithm. The paper details the hardware and software configuration of the robot and presents the results of driving experiments. The proposed platform has significant potential for various industries, including the delivery and other industries.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1055-1058
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• 2003

The robustness and reliability of vision algorithms is the key issue in robotic research and industrial applications. In this paper robust real time visual tracking in complex scene is considered. A common approach to increase robustness of a tracking system is the use of different model (CAD model etc.) known a priori. Also fusion or multiple features facilitates robust detection and tracking of objects in scenes of realistic complexity. Voting-based fusion of cues is adapted. In voting. a very simple or no model is used for fusion. The approach for this algorithm is tested in a 3D Cartesian robot which tracks a toy vehicle moving along 3D rail, and the Kalman filter is used to estimate the motion parameters. namely the system state vector of moving object with unknown dynamics. Experimental results show that fusion of cues and motion estimation in a tracking system has a robust performance.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.46-51
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• 2005

Intelligent Space(ISpace) is the space where many intelligent devices, such as computers and sensors, are distributed. According to the cooperation of many intelligent devices, the environment, it is very important that the system knows the location information to offer the useful services. In order to achieve these goals, we present a method for representing, tracking and human following by fusing distributed multiple vision systems in ISpace, with application to pedestrian tracking in a crowd. And the article presents the integration of color distributions into particle filtering. Particle filters provide a robust tracking framework under ambiguity conditions. We propose to track the moving objects by generating hypotheses not in the image plan but on the top-view reconstruction of the scene. Comparative results on real video sequences show the advantage of our method for multi-object tracking. Simulations are carried out to evaluate the proposed performance. Also, the method is applied to the intelligent environment and its performance is verified by the experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.2
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• pp.168-177
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• 2008

Path finding is a key element in the navigation of a mobile robot. To find a path, robot should know their position exactly, since the position error exposes a robot to many dangerous conditions. It could make a robot move to a wrong direction so that it may have damage by collision by the surrounding obstacles. We propose a method obtaining an accurate robot position. The localization of a mobile robot in its working environment performs by using a vision system and Virtual Reality Modeling Language(VRML). The robot identifies landmarks located in the environment. An image processing and neural network pattern matching techniques have been applied to find location of the robot. After the self-positioning procedure, the 2-D scene of the vision is overlaid onto a VRML scene. This paper describes how to realize the self-positioning, and shows the overlay between the 2-D and VRML scenes. The suggested method defines a robot's path successfully. An experiment using the suggested algorithm apply to a mobile robot has been performed and the result shows a good path tracking.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.285-288
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• 2005

Neural networks are known as kinds of intelligent strategies since they have learning capability. There are various their applications from intelligent control fields; however, their applications have limits from the point that the stability of the intelligent control systems is not usually guaranteed. In this paper we propose a neuro-adaptive controller for robot manipulators using the radial basis function network(RBFN) that is a kind of a neural network. Adaptation laws for parameters of the RBFN are developed based on the Lyapunov stability theory to guarantee the stability of the overall control scheme. Filtered tracking errors between the actual outputs and desired outputs are discussed in the sense of the uniformly ultimately boundedness(UUB). Additionally, it is also shown that the parameters of the RBFN are bounded. Experimental results for a SCARA-type robot manipulator show that the proposed neuro-adaptive controller is adaptable to the environment changes and is more robust than the conventional PID controller and the neuro-controller based on the multilayer perceptron.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.8
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• pp.1811-1817
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• 2014

In this paper, a PID controller with interpolated gains by use of neural networks is proposed for the formation control problem that following robots track a leading robot with constant distances and angles when there are changes in the mass of the following robot. The whole control system is composed of a kinematic controller and a dynamic controller considering the robot dynamics. The dynamic controller is the PID controller with varying gains, and the proper gains are obtained for some representative masses of the follower robot by the genetic algorithm. Neural networks is trained using the genetic algorithm with the gain data obtained in the previous step. The trained neural network determines optimal PID gains for a random mass of following robot. Simulation studies show that for arbitrary masses of the tracking robot, the PID controller with interpolated gains by the trained neural network has better tracking performance than that of the PID controller with fixed gains.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.6C
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• pp.593-603
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• 2009

In this paper, we propose a new integrated computer vision system designed to track multiple human beings and extract their silhouette with an active stereo camera. The proposed system consists of three modules: detection, tracking and silhouette extraction. Detection was performed by camera ego-motion compensation and disparity segmentation. For tracking, we present an efficient mean shift based tracking method in which the tracking objects are characterized as disparity weighted color histograms. The silhouette was obtained by two-step segmentation. A trimap is estimated in advance and then this was effectively incorporated into the graph cut framework for fine segmentation. The proposed system was evaluated with respect to ground truth data and it was shown to detect and track multiple people very well and also produce high quality silhouettes. The proposed system can assist in gesture and gait recognition in field of Human-Robot Interaction (HRI).

• Journal of the Korea Society for Simulation
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• v.18 no.2
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• pp.77-89
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• 2009

Visual tracking technique is one of the essential things which are very important in the major fields of modern society. While contour tracking is especially necessary technique in the aspect of its fast performance with target's external contour information, it sometimes fails to track target motion because it is affected by the surrounding edges around target and weak egdes on the target boundary. To overcome these weak points, in this research it is suggested that PDMs can be obtained by generating the virtual 6-DOF motions of the mobile robot with a CCD camera and the image tracking system which is robust to the local minima around the target can be configured by constructing Active Shape Model in modular base. To show the effectiveness of the proposed method, the experiment is performed on the image stream obtained by a real mobile robot and the better performance is confirmed by comparing the experimental results with the ones of other major tracking techniques.