• Proceedings of the Korean Society for Agricultural Machinery Conference
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• v.10 no.2
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• pp.400-403
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• 2005

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.8
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• pp.726-734
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• 2007

Generally, a mobile robot is moved by original input programs. However, it is very hard for a non-expert to change the program generating the moving path of a mobile robot, because he doesn't know almost the teaching command and operating method for driving the robot. Therefore, the teaching method with speech command for a handicapped person without hands or a non-expert without an expert knowledge to generate the path is required gradually. In this study, for easily teaching the moving path of the autonomous mobile robot, the autonomous mobile robot with the function of speech recognition is developed. The use of human voice as the teaching method provides more convenient user-interface for mobile robot. To implement the teaching function, the designed robot system is composed of three separated control modules, which are speech preprocessing module, DC servo motor control module, and main control module. In this study, we design and implement a speaker dependent isolated word recognition system for creating moving path of an autonomous mobile robot in the unknown environment. The system uses word-level Hidden Markov Models(HMM) for designated command vocabularies to control a mobile robot, and it has postprocessing by neural network according to the condition based on confidence score. As the spectral analysis method, we use a filter-bank analysis model to extract of features of the voice. The proposed word recognition system is tested using 33 Korean words for control of the mobile robot navigation, and we also evaluate the performance of navigation of a mobile robot using only voice command.

• Journal of Sensor Science and Technology
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• v.10 no.2
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• pp.134-141
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• 2001

This paper describes a method to steer an autonomous electric vehicle using magnetic fields. Magnets are embeded along the center of the road and a magneto-resistive sensor is mounted beneath the front bumper of the vehicle. As the vehicle moves along the road neural network controller controls the vehicle using measured magnetic field variation. Based on a single magnets modeling equation, we analyzed three dimensional magnetic field distributions of embeded magnets in series on the center of the road and performed a computer simulation using this results. In simulation study, straight and curved road was configured. The steering controller for the vehicle was designed using neural network and experiment was performed on the real embeded magnets using real autonomous electric vehicle. At the experiment we compensated the earth's magnetic fields and showed a good result driving an autonomous vehicle using proposed method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.71-77
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• 2021

Recently, there is a demand for efficient program development of an IoT application support frameworks considering heterogeneous hardware characteristics. In addition, the scope of hardware support is expanding with the development of neuromorphic architecture that mimics the human brain to learn on their own and enables autonomous computing. However, most existing IoT IDE(Integrated Development Environment), it is difficult to support AI(Artificial Intelligence) or to support services combined with various hardware such as neuromorphic architectures. In this paper, we design an AI component abstract model that supports the second-generation ANN(Artificial Neural Network) and the third-generation SNN(Spiking Neural Network), and implemented an autonomous IoT IDE based on the proposed model. IoT developers can automatically create AI components through the proposed technique without knowledge of AI and SNN. The proposed technique is flexible in code conversion according to runtime, so development productivity is high. Through experimentation of the proposed method, it was confirmed that the conversion delay time due to the VCL(Virtual Component Layer) may occur, but the difference is not significant.

• ETRI Journal
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• v.29 no.2
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• pp.249-251
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• 2007

A novel idea is presented to locate intersections in a video sequence captured from a moving vehicle. More specifically, we propose a Bayesian network approach to combine evidence extracted from a video sequence and evidence from a database, maximizing evidence from various sensors in a systematic manner and locating intersections robustly.

• Annual Conference of KIPS
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• 2003.11b
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• pp.639-642
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• 2003

본 논문에서는 청소로봇이 전 청소 영역을 CCPP(Complete Coverage Path Planning)를 이용해 경로를 생성한 후 재 경로계획 시 장애물의 미소한 변화로도 기존에 생성한 전 경로패턴을 바꾸지 않고 수정 할 수 있는 CD(Cell Decomposition)와 FN(Flow Network)을 이용한 CCPP 방식을 제안 하였다. 그리고 제안된 경로 계획에 대해 시뮬레이션으로 결과를 제시하였다.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.124-127
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• 1997

In this paper, we propose a method of cooperative control based on immune system in distributed autonomous robotic system(DARS). Immune system is living body's self-protection and self-maintenance system. Thus these features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For the purpose of applying immune system to DARS, a robot is regarded as a B lymphocyte(B cell), each environmental condition as an antigen, and a behavior strategy as an antibody respectively. The executing process of proposed method is as follows. When the environmental condition changes, a robot selects an appropriate behavior strategy. And its behavior strategy is simulated and suppressed by other robot using communication. Finally much simulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and idiotopic network hypothesis. And it is used for decision making of optimal swarm strategy.

• Journal of Satellite, Information and Communications
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• v.12 no.3
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• pp.69-73
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• 2017

In this paper, we developed a real-time image recognition algorithm based on machine learning and tested the performance of the algorithm. The real-time image recognition algorithm recognizes the input image in real-time based on the machine-learned image data. In order to test the performance of the real-time image recognition algorithm, we applied the real-time image recognition algorithm to the autonomous vehicle and showed the performance of the real-time image recognition algorithm through the application of the autonomous vehicle.

• Proceedings of the Korean Institute of Industrial Safety Conference
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• 2003.10a
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• pp.3-16
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• 2003

After a not-widely-known fact is revealed that Japan is a leading country in production and use of industrial unmanned helicopters, a kind of UAV. The voice command system and the autonomous flight control system with a variety of control algorithms including neural network, robust and adaptive control that have been developed in collaboration between Kyoto University and Yamaha Motor Co., and funded by the Ministry of Education and Science of Japan are described in some detail. Both already-proven and promising future applications of the autonomous unmanned helicopters are given.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.2
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• pp.180-188
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• 1999

The objective of this paper is, based upon the principles of artificial life, to induce emergent behaviors of multiple autonomous mobile robots which complex global intelligence form from simple local interactions. Here, we propose an architecture of neural network learning with reinforcement signals which perceives the neighborhood information and decides the direction and the velocity of movement as mobile robots navigate in a group. As the results of the simulations, the optimum weight is obtained in real time, which not only prevent the collisions between agents and obstacles in the dynamic environment, but also have the mobile robots move and keep in various patterns.