• Journal of the Ergonomics Society of Korea
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• v.10 no.1
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• pp.3-10
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• 1991

The teach pendant and keyboard have been used as an input device of control command in human-robot sustem. But, many problems occur in case that the usef is a novice. So, speech recognition system is required to communicate between a human and the robot. In this study, Korean voice commands, eitht robot commands, and ten digits based on the broad phonetic analysis are described. Applying broad phonetic analysis, phonemes of voice commands are divided into phoneme groups, such as plosive, fricative, affricative, nasal, and glide sound, having similar features. And then, the feature parameters and their ranges to detect phoneme groups are found by minimax method. Classification rules are consisted of combination of the feature parameters, such as zero corssing rate(ZCR), log engery(LE), up and down(UD), formant frequency, and their ranges. Voice commands were recognized by the classification rules. The recognition rate was over 90 percent in this experiment. Also, this experiment showed that the recognition rate about digits was better than that about robot commands.

• The Journal of Korea Robotics Society
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• v.2 no.2
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• pp.191-195
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• 2007

Abstract MOCIE(Ministry of Commerce, Industry and Energy) handles 6 Projects for Intelligent Robot, whose budget is around 40 Million dollars per year. In this paper we have tried to analyze the state of robot technology of the projects. Each sub-projects has been divided according to the technological classification. Two major projects of Next Generation Growth Engine and 21C Frontier show different state each other. The former is focused on the product while the latter on the technology. Output of 21C Frontier should be linked to the Next Generation Growth Engine, otherwise, it will fail to advance. The project management handles only the quantitative performance such as business results, number of prototype, and number of patents and papers. Technological Capability is essential and it should be managed. This paper proposes efficient classification of robot technology and technology index.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1738-1741
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• 1997

This paper addresses the issue fo target classification and localization with a SONAR for mobiler robot indoor navigation. In particular, multiple refetions of SONAR sound are used actively and interntionally. As for the SONAR sensor, the multiple reflection has been generally considered as one of the noisy phenomena, which is inevitable in the indoor environments. However, these multiple reflections can be a clue for classifying and localizing targets in the indoor environment if those can be controlled and used well. This paper develops a new SONAR sensor module with a reflection plane which can actively create the multiple refection. This paper also intends to suggest a new target classification emthod which uses the multiple refectiions. We approximate the world as being two dimensional and assume that the targets consisting of the indoor environment are pland, corner, and edge. Multiple reflection paths of an acoustic bean by a SONAR are analyzed, by simulations and the patterns of the TOPs (Time Of Flight) and angles of multiple reflections from each target are also analyzed. In addition, a new algorithm for target classification and localization is proposed.

• ETRI Journal
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• v.45 no.5
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• pp.847-861
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• 2023

Dynamic object detection is essential for ensuring safe and reliable autonomous driving. Recently, light detection and ranging (LiDAR)-based object detection has been introduced and shown excellent performance on various benchmarks. Although LiDAR sensors have excellent accuracy in estimating distance, they lack texture or color information and have a lower resolution than conventional cameras. In addition, performance degradation occurs when a LiDAR-based object detection model is applied to different driving environments or when sensors from different LiDAR manufacturers are utilized owing to the domain gap phenomenon. To address these issues, a sensor-fusion-based object detection and classification method is proposed. The proposed method operates in real time, making it suitable for integration into autonomous vehicles. It performs well on our custom dataset and on publicly available datasets, demonstrating its effectiveness in real-world road environments. In addition, we will make available a novel three-dimensional moving object detection dataset called ETRI 3D MOD.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1413-1415
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• 2004

This paper proposes a SVM(Support Vector Machine) training algorithm to control a service robot with voice command. The service robot with a stereo vision system and dual manipulators of four degrees of freedom implements a User-Dependent Voice Control System. The training of SVM algorithm that is one of the statistical learning theories leads to a QP(quadratic programming) problem. In this paper, we present an efficient SVM speech recognition scheme especially based on less learning data comparing with conventional approaches. SVM discriminator decides rejection or acceptance of user's extracted voice features by the MFCC(Mel Frequency Cepstrum Coefficient). Among several SVM kernels, the exponential RBF function gives the best classification and the accurate user recognition. The numerical simulation and the experiment verified the usefulness of the proposed algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.9
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• pp.755-762
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• 2004

In this research, EOG(Electrooculogram) signal was analyzed to predict the subject's intention using a fuzzy classifier. The fuzzy classifier is built automatically using the EOG data and evolutionary algorithms. An assistant robot manipulator in redundant configuration has been developed, which operates according to the EOG signal classification results. For automatic fuzzy model construction without any experts' knowledge, an evolutionary algorithm with the new representation scheme, design of adequate fitness function and evolutionary operators, is proposed. The proposed evolutionary algorithm can optimize the number of fuzzy rules, the number of fuzzy membership functions, parameter values for the each membership functions, and parameter values for the consequent parts. It is shown that the fuzzy classifier built by the proposed algorithm can classify the EOG data efficiently. Intelligent motion planner that consists of several neural networks are used for control of robot manipulator based upon EOG classification results.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.123-126
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• 2002

Recently. the effort to make a robot alike human is increasing. The robot helps house work or make a service in restaurant is not a dream. In this paper, we studied home robot serves a beverage. Each 4 beverage has different color. (And robot can serve a drink to master with its color.) It can be applicable to other works need to recognize target object with color. Perhaps, This looks simple but it's idea can be a basis for more complex work.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.276-282
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• 2015

This paper proposes a SMD (surface mounting device) classification method for the PCB assembly inspection machines. The package types of SMD components should be classified to create the job program of the inspection machine. In order to reduce the creation time of job program, we developed the automatic classification algorithm for the SMD packages. We identified the chip-type packages by color and edge distribution of the images. The input images are transformed into the HSI color model, and the binarized histroms are extracted for H and S spaces. Also the edges are extracted from the binarized image, and quantized histograms are obtained for horizontal and vertical direction. The neural network is then applied to classify the package types from the histogram inputs. The experimental results are presented to verify the usefulness of the proposed method.

• Korean Journal of Agricultural Science
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• v.49 no.4
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• pp.785-793
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• 2022

In this study, tomato maturity was estimated based on deep learning for a harvesting robot. Tomato images were obtained using a RGB camera installed on a monitoring robot, which was developed previously, and the samples were cropped to 128 × 128 size images to generate a dataset for training the classification model. The classification model was constructed based on convolutional neural networks, and the mean-variance loss was used to learn implicitly the distribution of the data features by class. In the test stage, the tomato maturity was estimated as a continuous index, which has a range of 0 to 1, by calculating the expected class value. The results show that the F1-score of the classification was approximately 0.94, and the performance was similar to that of a deep learning-based classification task in the agriculture field. In addition, it was possible to estimate the distribution in each maturity stage. From the results, it was found that our approach can not only classify the discrete maturation stages of the tomatoes but also can estimate the continuous maturity.