• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.47-49
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• 2022

Robot navigation has seen a major improvement since the the rediscovery of the potential of Artificial Intelligence (AI) and the attention it has garnered in research circles. A notable achievement in the area was Deep Learning (DL) application in computer vision with outstanding daily life applications such as face-recognition, object detection, and more. However, robotics in general still depend on human inputs in certain areas such as localization, navigation, etc. In this paper, we propose a study case of robot navigation based on deep reinforcement technology. We look into the benefits of switching from traditional ROS-based navigation algorithms towards machine learning approaches and methods. We describe the state-of-the-art technology by introducing the concepts of Reinforcement Learning (RL), Deep Learning (DL) and DRL before before focusing on visual navigation based on DRL. The case study preludes further real life deployment in which mobile navigational agent learns to navigate unbeknownst areas.

• Journal of the Institute of Convergence Signal Processing
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• v.25 no.2
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• pp.58-66
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• 2024

Robotics is a convergence technology with significant ripple effects across various industries, including manufacturing and services. Its importance has been increasingly underscored by advancements in artificial intelligence. As a crucial industry for addressing the challenges posed by a declining and aging production workforce and for enhancing manufacturing competitiveness, the training of robotics experts is now critically important. This paper examines the case of the Robot Job Innovation Center in Gumi City and proposes a strategy for training robotics experts and specialists in robot/AI-based signal processing. A core curriculum was carefully selected and implemented in actual educational settings, with key components necessary for developing a comprehensive educational framework detailed. The convergence of AI-based data signal processing and robotics represents a significant technological advancement poised to impact a wide array of industries. By proposing the comprehensive educational framework outlined in this paper, it is anticipated that related organizations will be able to effectively utilize these foundational elements to train experts in the field.

• Journal of Practical Engineering Education
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• v.12 no.2
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• pp.339-351
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• 2020

In response to changes in Fourth Industrial Revolution in recent years, the field of education has focused on development of the human resources in the areas of artificial intelligence (AI: Artificial Intelligence) and industrial robot. Due to particular interest in these areas, the importance of developing integrated human resources equipped with artificial intelligence technology is emphasized in higher education and vocational competence development. In regards to rapid changing environment, this study created a program "Fostering personalized AI integrated human resource" and established an operational model correspond to latest personalized education trend. The established operational model was conducted twice using Delphi survey with experts in AI and innovative education in order to verify the suitability of program's basic structure, training process, and the sub-components of the operational strategy. The final training model was applied to the online vocational training platform (STEP) and a plan was proposed to establish a personalized training model to foster an AI integrated competent individual.

• Convergence Security Journal
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• v.23 no.5
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• pp.151-156
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• 2023

Currently, ICT is widely used in caring for the elderly living alone and preventing the disappearance of the elderly with dementia. Therefore, in this study, based on the government policy direction for the 4th industrial revolution, the use of AI technology-based care services, which are gradually increasing in community care, was sought to explore the current status and prospects for utilization and activation.AI speakers and caring robots, services that can be used for community care, help solve various problems experienced by the elderly, and are also used to relieve lack of conversation or loneliness by adding emotional functions. In order to activate community care using AI technology in the future: First, there is a need for continuous education to familiarize the elderly with AI devices and 'user experience (UX) design' for the elderly. Second, it is necessary to use human-centered technology that has a complementary relationship and enables emotional mutual relationships rather than using function-oriented technology. Third, it is necessary to solve ethical problems such as guaranteeing the user's right to self-determination and protecting privacy.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1797-1801
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• 2005

SLAM(Simultaneous localization and mapping) and AI(Artificial intelligence) have been active research areas in robotics for two decades. In particular, localization is one of the most important issues in mobile robot research. Until now expensive sensors like a laser sensor have been used for the mobile robot's localization. Currently, as the RFID reader devices like antennas and RFID tags become increasingly smaller and cheaper, the proliferation of RFID technology is advancing rapidly. So, in this paper, the smart floor using passive RFID tags is proposed and, passive RFID tags are mainly used to identify the mobile robot's location on the smart floor. We discuss a number of challenges related to this approach, such as RFID tag distribution (density and structure), typing and clustering. In the smart floor using RFID tags, because the reader just can senses whether a RFID tag is in its sensing area, the localization error occurs as much as the sensing area of the RFID reader. And, until now, there is no study to estimate the pose of mobile robot using RFID tags. So, in this paper, two algorithms are suggested to. We use the Markov localization algorithm to reduce the location(X,Y) error and the Kalman Filter algorithm to estimate the pose(q) of a mobile robot. We applied these algorithms in our experiment with our personal robot CMR-P3. And we show the possibility of our probability approach using the cheap sensors like odometers and RFID tags for the mobile robot's localization on the smart floor.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.197-202
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• 2019

Society advances through technology, and technology has changed many lifestyles. The need for food is varying, but the availability of food is constantly changing as trends in production change. Combining the food industry and technology, a robot that delivers food and also cooks it has been developed. The time has come for a combination of food content and technology to advance the restaurant industry. This study discusses the application of a recommended food content media providing system using a curation engine that recommends contents according to individual tastes and preferences for the convenience of those who use food contents, using artificial intelligence speakers. We discuss the technologies required to develop video contents optimized for AI speakers with screens and shapes, combined with inset top boxes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.527-529
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• 2021

This paper focuses on the implementation of machine learning model for Recycling bot, which is a platform service of recycling education. The recycling bot applied with a AI model using collected image set. The experiment confirms that classified by the model result are accurate.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.838-839
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• 2023

배달량 확산과 자율 주행 로봇의 실내외 주행이 가능해지면서 로봇을 활용할 주문 및 배달 서비스가 활발해지고 있다. 따라서 기존의 자율 주행 배달 로봇의 문제점을 개선하여 보다 나은 배달 서비스를 제공하고자 자율 주행 배달 로봇을 연구하였다. 배달 로봇에 AI를 적용하여 장애물 탐지, 최적코스 탐색을 통해 목적지까지 최적 경로로 이동하며, 배달 물품 안전까지 보장한다.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.17-18
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• 2023

This study aims to compare the hardness value development characteristics according to the water-cement ratio during a series of experiments to develop a setting estimator for an AI-based concrete finishing automation system. For the test variables, water-cement ratios are varied with 30, 40 and 50%. Proctor penetration test and surface hardness test by setting time estimator are conducted to estimate the setting time. For the effect of water-cement ratios, they did not affect the surface hardness either, while initial set time and final set time are not constant with water-cement ratios.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.11
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• pp.522-531
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• 2001

Recently conventional artificial intelligence(AI) approaches have been employed to build action selectors for the autonomous mobile robot(AMR). However, in these approaches, the decision making process to choose an action from multiple competence modules is still an open question. Many researches have been focused on the reactive planning systems such as the biological immune system. In this paper, we attempt to construct an action selector for an AMR based on the artificial immune network and internet. The information from vision sensors is used for antibody. We propose a learning method for artificial immune network using evolutionary algorithm to produce antibody automatically. The internet environment for an AMR action selector shows the usefulness of the proposed learning artificial immune network application.