• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.181-184
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• 1998

CAM-Brain is a model to develop neural networks based in cellular automata by evolution, and finally aims at a model as and artificial brain,. In order to show the feasibility of evolutionary engineering to develop an artificial brain we have attempted to evolve a module of CAM-Brain for the problem to control a mobile robot, In this paper, we present some recent results obtained by analyzing the behaviors of the evolved neural module. Several experiments reveal a couple of problems that should be solved when CAM-Brain evolves to control a mobile robot. so that some modification of the original model is proposed to solve them. The modified CAM-Brain has evolved to behave well in a simulated environment, and a thorough analysis proves the power of evolution.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.109.1-109
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• 2001

This paper deals with the path following problem of car-like intelligent mobile robot. A robust sliding mode control law based on time-varying state feedback is performed via Lyapunov method for path tracking of nonholonomic mobile robot with uncertainties. At first, A sliding control law is designed by combing the natural algebraic structure of the chained form system with ideas from sliding mode theory. Then, a robust control law is proposed to impose robustness against bounded uncertainties in path tracking. The problem of estimating the asymptotic stability region and the sliding domain of uncertain sliding mode system with bounded control input is also discussed. The proposed sliding mode control law can ensure the global reaching condition of the uncertain control system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.219-222
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• 1996

During the navigation of mobile robot, one of the essential task is to determination the absolute location of mobile robot. In this paper, we proposed a method to determine the position of the camera from a landmark through the visual image of a quadrangle typed landmark using neural network. In determining the position of the camera on the world coordinate, there is difference between real value and calculated value because of uncertainty in pixels, incorrect camera calibration and lens distortion etc. This paper describes the solution of the above problem using BPN(Back Propagation Network). The experimental results show the superiority of the proposed method in comparison to conventional method in the performance of determining camera position.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.29-47
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• 2004

최근 로봇에 지능을 부여하기 fl한 다양한 연구가 진행되어 지고 있으며, 생물학-곤충들의 행동과 지능에 기초를 둔 로봇 지능-행동 연구가 중요한 연구 줄거리를 형성하게 되었다. 이 글은 로봇의 제어구조-숙고형, 반사행동형-에 대한 소개와 각각의 장.단점에 대한 고찰, 생물학-곤충들의 행동과 지능-에 기초를 둔 로봇'지능-행동 연구 분야의 최근의 연구 성과들을 고찰하고 소개한다. 그리고 지능을 부여하기 위한 학습 방법으로서의 강화학습(reinforcement learning)의 연구주제를 소개하고 향후의 로봇 연구 방향에 대하여 고찰한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.332-340
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• 2013

This paper reports a programming toolkit for implementing localization and navigation of a mobile robot both in real world and simulation. Many of the previous function libraries are difficult to use because of their complexity or lack of usability. The proposed toolkit consist of functions for dead reckoning, motion model, measurement model, and operations on directions or heading angles. The dead reckoning and motion model deals with differential drive robot and bicycle type robot driven by front wheel or rear wheel. The functions can be used for navigation in both real environment and simulation. To prove the feasibility of the toolkit, simulation results are shown along with the results in real environment. It is expected the proposed toolkit is used for test of algorithms for mobile robot navigation such as localization, map building, and obstacle avoidance.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2024-2029
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• 2005

In this paper, it is shown how a mobile robot can navigate with high speed in dynamic real environment. In order to achieve high speed and safe navigation, a robot collects environmental information. A robot empirically memorizes locations of high risk due to the abrupt appearance of dynamic obstacles. After collecting sufficient data, a robot navigates in high speed in safe regions. This fact implies that the robot accumulates location dependent environmental information and the robot exploits its experiences in order to improve its navigation performance. This paper proposes a computational scheme how a robot can distinguish regions of high risk. Then, we focus on velocity control in order to achieve high speed navigation. The proposed scheme is experimentally tested in real office building. The experimental results clearly show that the proposed scheme is useful for improving a performance of autonomous navigation. Although the scope of this paper is limited to the velocity control in order to deal with unexpected obstacles, this paper points out a new direction towards the intelligent behavior control of autonomous robots based on the robot's experience.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1195-1200
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• 2007

This paper describes indoor location estimation intelligent robot. Indoor location estimation function using RSSI based indoor location estimation system and wireless sensor networks were implemented in the robot. Spartan III(Xilinx, U.S.A.) was used as a main control device in the mobile robot and the current direction data was collected in the indoor location estimation system. The data was transferred to the wireless sensor network node attached to the mobile robot through Zigbee/IEEE 802.15.4, a wireless communication. After receiving it, with the data of magnetic compass the node is aware of and senses the direction the robot head for and the robot moves to its destination. Indoor location estimation intelligent robot is can be moved efficiently and actively without obstacle on flat ground to the appointment position by user.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1184-1189
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• 2003

Map building and position estimation capabilities are practically indispensable for a mobile robot to execute its given tasks in its working environments. An autonomous map building method and a smart localization method is proposed in our previous works. The experimental verifications are carried out in this paper. We applied the proposed algorithms to mobile service robots in large-scale indoor buildings. Experimental results show that our strategy is reliable and feasible in tough conditions like non-polygonal and dynamic environments. The advantages of the algorithms are well-illustrated through real experiments.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.18-23
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• 2003

This paper presents new algorithms of path planning and obstacle avoidance for an autonomous mobile robot to navigate under unknown environments in the real time. Temporary targets are set up by distance variation method and then the algorithms of trajectory planning and obstacle avoidance are designed using fuzzy rules. It is shown by computer simulation that these algorithms are working well. Furthermore, an autonomous mobile robot was constructed to implement and test these algorithms in the real field. The experimental results are also satisfactory just like those of computer simulation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.259-263
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• 2008

The dynamics of mobile robot is nonlinear. To cope with this nonlinearity, many advanced control schemes have been proposed recently. Generally, the advanced control schemes are complicated and not good for the practical real-time control when they are implemented as control programs. So, in this paper, a relatively simple PI controller is proposed and applied to the position control of mobile robot with the adoption of reference trajectory calculation method used for the AUV(Autonomous Underwater Vehicle) control. The proposed PI controller is programmed using LabVIEW which is popular for its graphical programming characteristics. The simulation and experimental results show the feasibility and effectiveness of the proposed PI controller.