• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.49.1-49
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• 2001

The skin color model is a very important concept in face detection, face recognition and face tracking. Usually, this model is obtained by estimating a probability density function of skin color distribution. In many cases, it is assumed that the underlying density function follows a Gaussian distribution. In this paper, a new method for non-parametric estimation of the probability density function, by using feed-forward neural network, is used to estimate the underlying skin color model. By using this method, the resulting skin color model is better than the Gaussian estimation and substantially approaches the real distribution. Applications to face detection and face ...

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.88.2-88
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• 2001

In this paper Author introduce a new field named Artificial Life and its main directions of research. That is the research of evolutionary robot and artificial brain. Then author explored the advanced scientific thought hidden in them. Furthermore, the author tries intuitively to show a new type of control that is heuristically raised from artificial life research. It could be named as evolutionary control. This type of control is more like human body´s structure, and it is self-organized.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.2
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• pp.481-491
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• 2015

Since the crack in the surface of the concrete acts as the main reason influencing the life span of the structure, regular inspections and maintenance are required. The sealing required for maintenance of the concrete surface is a method of repairing the crack in the surface in the beginning, and is effective in preventing additional cracks and expansion that occurs with time. However, sealing on large sized structures such as tall buildings or bottom parts of bridges are difficult to ensure safety of the workers due to inadequate working environments. Due to this reason, the importance of the need for sealing automation for the maintenance of large sized concrete structures is emerging. This study proposes two control methods to apply robot systems to the sealing of cracks on the bottom parts of concrete bridges. First is the method of automatically tracking the trajectory of cracks. The robot gets the trajectory of the cracks using video information obtained from cameras. Comparing the previous several points and new point, the next point can be estimated. Thus, the trajectory of the crack can be tracked automatically. The other method is sealing by maintaining steady force to the contacting surface. The concrete surface exposed to an external environment for a long time gets an irregular roughness. If robots are able to carry out sealing while maintaining a steady contact force on these rough surfaces, complete equal sealing can be maintained. In order to maintain this equal force, a force control method using impedance is proposed. This paper introduces two developed control methods to apply to sealing robots, and conducts a Lab Test and Field Test after applying to a robot. Based on the test results, opinions on the possibilities of field application of the robot applied with the control methods are presented.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.126-133
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• 2023

In this study, electromyography was obtained in the six muscle areas that move the joints of the two legs, and by analyzing it, an exercise robot system capable of gait rehabilitation was proposed in consideration of the individual's muscle state. Through this, the system was constructed to prevent the effect of exercise from decreasing because the patient's will was not reflected when walking exercise was simply provided automatically. As a result of the evaluation of the developed system, it was confirmed that the pedestrian rehabilitation robot system manufactured through this study had performance suitable for the design requirements, and it was also confirmed that the usability evaluation was comprehensively satisfactory. The results of this study are thought to be of great help to patients who are having difficulty in gait rehabilitation, and are believed to be helpful in the development of electromyography signal-based gait robot systems.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.53-69
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• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.

• The Journal of the Korea Contents Association
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• v.9 no.4
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• pp.81-94
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• 2009

In ubiquitous environments, the real-time features are necessary to insure the QoS of the intelligent service robots. In this paper, we design and implement a real-time framework for intelligent service robots to support real-time features. The real-time framework to support real-time scheduling services is implemented on the general operating systems. We solve the problem that the scheduler of a general operating system can not support real-time features. This paper also proposes realtime scheduling services to guarantee the QoS of real-time robot applications. We implemented the proposed real-time framework on the Windows operating system and conducted some performance experiments. The experimental results show that the proposed real-time framework can improve thread response times and it has slight performance overhead of $62{\mu}s$.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.890-903
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• 2017

The problem of determining a smooth and collision-free path with maximum possible speed for a Mobile Robot (MR) which is chasing a moving target in a dynamic environment is addressed in this paper. Genetic Network Programming with Reinforcement Learning (GNP-RL) has several important features over other evolutionary algorithms such as it combines offline and online learning on the one hand, and it combines diversified and intensified search on the other hand, but it was used in solving the problem of MR navigation in static environment only. This paper presents GNP-RL based on predicting collision positions as a first attempt to apply it for MR navigation in dynamic environment. The combination between features of the proposed collision prediction and that of GNP-RL provides safe navigation (effective obstacle avoidance) in dynamic environment, smooth movement, and reducing the obstacle avoidance latency time. Simulation in dynamic environment is used to evaluate the performance of collision prediction based GNP-RL compared with that of two state-of-the art navigation approaches, namely, Q-Learning (QL) and Artificial Potential Field (APF). The simulation results show that the proposed GNP-RL outperforms both QL and APF in terms of smooth movement and safer navigation. In addition, it outperforms APF in terms of preserving maximum possible speed during obstacle avoidance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.4
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• pp.872-878
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• 2012

In this paper, an arm motion sensing system using potentiometer is developed. Most motion sensing systems use optical method for the quality of motion data. The optical method needs much cost for manufacturing capture system and takes much time for correcting the captured data. And mechanical method entails relativity low cost, but it uses the wires and takes much time for correcting the data like the optical method. For solving the problems, in this paper, an arm motion sensing system is newly developed using low cost potentiometer and based on the suggested simple calculation method for the joint angles and the angular velocities. For the verification of the performance of the developed system, practical experiments were executed using real human arm motion and a robot arm. The experimental results showed that the motion of the robot arm controlled by the output of the developed motion sensing system is much similar with the motion of human arm.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.7
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• pp.134-141
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• 2009

Recently, many researchers have tried to develop wearable robots for various fields such as medical and military purposes. We have been studying robotic exoskeletons to assist the motion of persons who have problems with their muscle function in daily activities and rehabilitation. The upper-limb motions (shoulder, elbow and wrist motion) are especially important for such persons to perform daily activities. Generally for shoulder motion 300F is needed to describe its motion(extension/flexion, abduction/adduction, internal/external rotation) but we have used a redundant actuator thus making a 4 DOF system. In this paper, we proposed the mechanism design of the exoskeleton which consists of 4-DOF for shoulder and 1-DOF for elbow robotic exoskeleton to assist upper-limb motion. Then we compared the new mechanism design and prototype mechanism design. Here we also analyze the proposed system kinematically to find out and to avoid the singular point. This research will ensure that the proposed wearable robot system make human's motion more powerfully and more easily.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.89-92
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• 2005

Using natural speech commands for controlling a human-robot is an interesting topic in the field of robotics. In this paper, our main focus is on the verification of a speaker who gives a command to decide whether he/she is an authorized person for commanding. Among possible dynamic features of natural speech, pitch period is one of the most important ones for characterizing speech signals and it differs usually from person to person. However, current techniques of pitch detection are still not to a desired level of accuracy and robustness. When the signal is noisy or there are multiple pitch streams, the performance of most techniques degrades. In this paper, we propose a two-level approach for pitch detection which in compare with standard pitch detection algorithms, not only increases accuracy, but also makes the performance more robust to noise. In the first level of the proposed approach we discriminate voiced from unvoiced signals based on a neural classifier that utilizes cepstrum sequences of speech as an input feature set. Voiced signals are then further processed in the second level using a modified standard AMDF-based pitch detection algorithm to determine their pitch periods precisely. The experimental results show that the accuracy of the proposed system is better than those of conventional pitch detection algorithms for speech signals in clean and noisy environments.