• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.5
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• pp.765-773
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• 2011

This paper proposes a control method for a differential robot to track and follow a moving object based on ultrasonic sensor. To track a target object, the method uses a transmitter and two receivers to get distances from the object. The method derives translational and rotational error by the distances and then it uses the errors to calculate control values based on PID control method. The control values are used to control the robot to follow moving object. The authors do some experimentations to analyze some characteristics such as influence of PID gain, influence of translational and rotational gain. This method not only can be applied for following moving object problem but also can be done group unit control problems.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.113-114
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• 2023

가정용 생활 쓰레기 수거 작업은 야간이나 이른 새벽에 이루어지고 있어 환경미화원의 안전사고와 수거 차량으로 인한 소음 문제가 빈번하게 발생한다. 본 논문에서는 딥러닝 기반의 영상 인식을 활용하여 종량제 봉투를 인식하고 수거가 가능한 생활 쓰레기 수거 로봇의 설계를 제시한다. 제시하는 생활 쓰레기 수거 로봇은 지정 구역을 자율주행하며 로봇에 장착된 카메라를 이용해 학습된 모델을 기반으로 가정용 쓰레기 종량제 봉투를 검출한다. 이를 통해 처리 대상으로 지정된 종량제 봉투와 로봇 팔 사이의 거리를 카메라를 활용하여 얻은 깊이 정보와 2차원 좌표를 토대로 목표 위치를 예측해 로봇 팔의 관절을 제어하여 봉투를 수거한다. 해당 로봇은 생활 쓰레기 수거 작업 과정에서 환경미화원을 보조하여 미화원의 안전 확보와 소음 저감을 위한 기기로 활용될 수 있다.

• Journal of KIISE:Software and Applications
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• v.34 no.8
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• pp.731-742
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• 2007

Self-growing software is a software system that has the capability of evolving its functionalities and configurations by itself based on dynamically monitored situations. Self-growing software is especially necessary for intelligent service robots, which must have the capability to monitor their surrounding environments and provide appropriate behaviors for human users. However, it is hard to anticipate all situations that robots face with, and it is hard to make robots have all functionalities for various environments. In addition, robots have limited internal capacity. To support self-growing software for intelligent service robots, we are developing a cell-based distributed repository system that allows robots and developers transparently to share robot functionalities. To accomplish the creation of evolutionary repositories, we invented the concept of a cell, which is a logical group of distributed repositories based upon the functionalities of components. In addition, a cell can be used as a unit for the evolutionary growth of the components within the repositories. In this paper, we describe the requirements and architecture of the cell-based repository system for self-growing software. We also present a prototype implementation and experiment of the repository system. Through the cell-based repositories, we achieve improved performance of self-growing actions for robots and efficient sharing of components among robots and developers.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.12
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• pp.491-498
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• 2019

Digital twin is a technology that virtualizes physical objects of the real world on a computer. It is used by collecting sensor data through IoT, and using the collected data to connect physical objects and virtual objects in both directions. It has an advantage of minimizing risk by tuning an operation of virtual model through simulation and responding to varying environment by exploiting experiments in advance. Recently, artificial intelligence and machine learning technologies have been attracting attention, so that tendency to virtualize a behavior of physical objects, observe virtual models, and apply various scenarios is increasing. In particular, recognition of each robot's motion is needed to build digital twin for co-robot which is a heart of industry 4.0 factory automation. Compared with modeling based research for recognizing motion of co-robot, there are few attempts to predict motion based on sensor data. Therefore, in this paper, an experimental environment for collecting current and inertia data in co-robot to detect the motion of the robot is built, and a motion prediction model based on the collected sensor data is proposed. The proposed method classifies the co-robot's motion commands into 9 types based on joint position and uses current and inertial sensor values to predict them by accumulated learning. The data used for accumulating learning is the sensor values that are collected when the co-robot operates with margin in input parameters of the motion commands. Through this, the model is constructed to predict not only the nine movements along the same path but also the movements along the similar path. As a result of learning using SVM, the accuracy, precision, and recall factors of the model were evaluated as 97% on average.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.375-378
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• 2007

This paper describes indoor location estimation intelligent robot. It is loaded indoor location estimation function using RSSI based indoor location estimation system and wireless sensor networks. Spartan III(Xilinx, U.S.A.) is used as a main control device in the mobile robot and the current direction data is collected in the indoor location estimation system. The data is transferred to the wireless sensor network node attached to the mobile robot through Zigbee/IEEE 802.15.4, a wireless communication. After receiving it, with the data of magnetic compass the node is aware of and senses the direction the robot head for and the robot moves to its destination. Indoor location estimation intelligent robot is can be moved efficiently and actively without obstacle on flat ground to the appointment position by user.

• The Journal of Korean Association of Computer Education
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• v.18 no.2
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• pp.21-33
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• 2015

This paper aims at developing IT convergence robot education contents using open hardware-based GoGo Board and presenting three cases that were applied into educational settings with elementary and middle school students. Several types of data for their activities were collected: photos, work output, survey data, video data and interview with robot teacher and students. Each student experienced building up a GoGo Bumper Car with touch sensors attached at front and back sides and figuring out the principle of digital board control and operating of electronic devices by sensing. The participants, in the following phases, conducted domino chain-reaction with GoGo Bumper Cars and acquiring GoGo Driving Licence by driving test on three different road maps. Students in a gifted education program creatively implemented their own ideas as part of robotic art. The result of case analysis showed that the proposed project provides students not only intimacy for technology, fun, concentration but her own empowerment for developing ideas and creative implementation.

• Journal of Advanced Navigation Technology
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• v.10 no.2
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• pp.113-120
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• 2006

In this paper, a vision-based robust controller for tracking the desired trajectory a robot manipulator is proposed. The trajectory is generated to move the feature point into the desired position which the robot follows to reach to the desired position. To compensate the parametric uncertainties of the robot manipulator which contain in the control input, the robust controller is proposed. In addition, if there are uncertainties in the Jacobian, to compensate it, a vision-based robust controller which has control input is proposed as well in this paper. The stability of the closed-loop system is shown by Lyapunov method. The performance of the proposed method is demonstrated by simulations and experiments on a two degree of freedom 5-link robot manipulators.

• The Journal of the Korea Contents Association
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• v.19 no.2
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• pp.469-476
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• 2019

Robotic motion is designed by the rotation and the translation of multiple joint coordinates in a three-dimensional space. Joint coordinates are generally modeled by homogeneous transform matrix. However, the complexity of three dimensional motions prefers the visualization methods based on simulation environments in which models and generated motions work properly. Many simulation environments have the limitations of usability and functional extension from platform dependency and interpretation of predefined commands. This paper proposes the web-based three dimensional simulation environment toward high user accessibility. Also, it covers the small size web server that is linked with Python script. The non linearities of robot control apply to verify the computing efficiency, the process management, and the extendability of user scripts.

• Journal of IKEEE
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• v.25 no.2
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• pp.337-343
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• 2021

Deep reinforcement learning is an artificial intelligence algorithm that enables learners to select optimal behavior based on raw and, high-dimensional input data. A lot of research using this is being conducted to create an optimal movement path of a mobile robot in an environment in which obstacles exist. In this paper, we selected the Dueling Double DQN (D3QN) algorithm that uses the prioritized experience replay to create the moving path of mobile robot from the image of the complex surrounding environment. The virtual environment is implemented using Webots, a robot simulator, and through simulation, it is confirmed that the mobile robot grasped the position of the obstacle in real time and avoided it to reach the destination.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.15-17
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• 2013

재난방지 및 구호에 사용되는 로봇의 주된 목적은 인간이 직접적으로 접근하기 곤란한 지역에 대한 올바른 상황 정보를 얻기 위함이다. 하지만, 재난지역에서는 통신이 원활하게 접속되지 않거나, 육안을 벗어나는 경우, 원격조정에 의한 통신을 통한 로봇들이 업무지시를 수행해야 하는데 상당한 어려움이 있다. 더군다나 재난지역의 범위가 공간적으로 방대하여 자율적이고, 협동할 수 있으며, 함께 행동할 수 있는 지능적인 로봇의 필요성이 대두되고 있다. 따라서 본 논문에서는 이전 연구에서 개발한 모듈러 기반의 생체로봇을 이용하여 재난지역에서 원활한 업무수행을 할 수 있도록 모듈러 로봇간의 상호인식방법을 연구하고자 한다. 특히, 서로의 위치를 인식하기 위한 방법으로 LED-RGB 센서를 이용한 상호위치인식 방법을 연구하고자 한다.