• The Journal of Korea Robotics Society
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• v.15 no.4
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• pp.404-412
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• 2020

Typically everyday human life tasks involve at least two people moving objects such as tables and beds, and the balancing of such object changes based on one person's action. However, many studies in previous work performed their tasks solely on robots without factoring human cooperation. Therefore, in this paper, we propose cooperative robot for table balancing using Q-learning that enables cooperative work between human and robot. The human's action is recognized in order to balance the table by the proposed robot whose camera takes the image of the table's state, and it performs the table-balancing action according to the recognized human action without high performance equipment. The classification of human action uses a deep learning technology, specifically AlexNet, and has an accuracy of 96.9% over 10-fold cross-validation. The experiment of Q-learning was carried out over 2,000 episodes with 200 trials. The overall results of the proposed Q-learning show that the Q function stably converged at this number of episodes. This stable convergence determined Q-learning policies for the robot actions. Video of the robotic cooperation with human over the table balancing task using the proposed Q-Learning can be found at http://ibot.knu.ac.kr/videocooperation.html.

• 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.

• Journal of Korea Society of Digital Industry and Information Management
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• v.20 no.2
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• pp.37-47
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• 2024

The food tech market, which uses artificial intelligence robots for the restaurant industry, is gradually expanding. Among them, the robot barista, a representative food tech case for the restaurant industry, is characterized by increasing the efficiency of operators and providing things for visitors to see and enjoy through a 24-hour unmanned operation. This research was conducted through text mining analysis to examine trends related to robot baristas in the restaurant industry. The research results are as follows. First, keywords such as coffee, cafe, certification, ordering, taste, interest, people, robot cafe, coffee barista expert, free, course, unmanned, and wine sommelier were highly frequent. Second, time, variety, possibility, people, process, operation, service, and thought showed high closeness centrality. Third, as a result of CONCOR analysis, a total of 5 keyword clusters with high relevance to the restaurant industry were formed. In order to activate robot barista in the future, it is necessary to pay more attention to functional development that can strengthen its functions and features, as well as online promotion through various events and SNS in the robot barista cafe.

• 전자공학회논문지 IE
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• v.44 no.3
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• pp.35-41
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• 2007

In this paper we suggest a model that the virtual robot based on artificial intelligence performs learning with compensation signals and compare the leaning speed of the virtual robot according to the compensation method after applying it to three type environments. As a result our model has showed that positive compensation is superior to hybrid one mixed positive and negative if there are enough time for learning in case of more or less complicated environment with the numerous foods, obstacles and robots. Otherwise hybrid method is better than positive one.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.10
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• pp.878-886
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• 2004

Uncertainties are inevitable in robotic assembly in unstructured environment since it is difficult to construct fixtures to guide motions of robots. This paper proposes an augmented Petri net and an algorithm to adapt the assembly model on-line during actual assembly process. The augmented Petri net identifies events using force and position information simultaneously. Unmodeled contact states are identified and incorporated into the model on-line. The proposed method increases the level of intelligence of the robot system by enhancing the autonomy. The proposed method is evaluated by simulation and experiments with L-type peg-in-hole assembly using a two-arm robot system.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.501-506
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• 2000

Many research in Robot control has effectively proceeded on the development of Aritficial Intelligence Robot which is able to apply to the uncertain and monotonous operations which are repeated continuously in the industrial field. In this study, the precise shape recognition of base metal for welding was gained by mono CCD camera, and the gained data was transformed into Decimal code through Image Board in computer. And the Fuzzy Logic control system designed by use of Fuzzy rule was built to judge whether the base metals were precisely matched or not with Decimal code. Machanically manipulated Robot syst em was linked to Fuzzy Control system through image information, and ultimately, these systems will be able to apply for production system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.314-320
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• 2006

Navigation in environments that are densely cluttered with obstacles is still a challenge for Autonomous Ground Vehicles (AGVs), especially when the configuration of obstacles is not known a priori. Reactive local navigation schemes that tightly couple the robot actions to the sensor information have proved to be effective in these environments, and because of the environmental uncertainties, STSF(Space and Time Sensor Fusion)-based fuzzy behavior systems have been proposed. Realization of autonomous behavior in mobile robots, using STSF control based on spatial data fusion, requires formulation of rules which are collectively responsible for necessary levels of intelligence. This collection of rules can be conveniently decomposed and efficiently implemented as a hierarchy of fuzzy-behaviors. This paper describes how this can be done using a behavior-based architecture. The approach is motivated by ethological models which suggest hierarchical organizations of behavior. Experimental results show that the proposed method can smoothly and effectively guide a robot through cluttered environments such as dense forests.

• 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 Engineering Education Research
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• v.25 no.3
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• pp.35-44
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• 2022

Artificial intelligence is a core technology in the era of digital transformation, and as the technology level is advanced and used in various industries, its influence is growing in various fields, including social, ethical and legal issues. Therefore, it is time to raise social awareness on ethics of artificial intelligence as a prevention measure as well as improvement of laws and institutional systems related to artificial intelligence development. In this study, we analyzed unstructured data, typically text, such as online news articles and comments to confirm the degree of social awareness on ethics of artificial intelligence development. The analysis showed that the public intended to concentrate on specific issues such as "Human," "Robot," and "President" in 2018 to 2019, while the public has been interested in the use of personal information and gender conflics in 2020 to 2021.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1137-1144
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• 2022

In this study, an artificial intelligence(AI) was developed to help with facial expression practice in order to express emotions. The developed AI used multimodal inputs consisting of sentences and facial images for deep neural networks (DNNs). The DNNs calculated similarities between the emotions predicted by the sentences and the emotions predicted by facial images. The user practiced facial expressions based on the situation given by sentences, and the AI provided the user with numerical feedback based on the similarity between the emotion predicted by sentence and the emotion predicted by facial expression. ResNet34 structure was trained on FER2013 public data to predict emotions from facial images. To predict emotions in sentences, KoBERT model was trained in transfer learning manner using the conversational speech dataset for emotion classification opened to the public by AIHub. The DNN that predicts emotions from the facial images demonstrated 65% accuracy, which is comparable to human emotional classification ability. The DNN that predicts emotions from the sentences achieved 90% accuracy. The performance of the developed AI was evaluated through experiments with changing facial expressions in which an ordinary person was participated.