• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.6
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• pp.990-997
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• 2010

The Mission environment of UGV(Unmanned Ground Vehicle) has a complexity and variety, and the status of system and sensor is dependent on the environment factors such as operation time, the weather and road type. It is necessary for UGV to cope adaptively with the various mission types, operation modes and operation environment as human operators do. To satisfy this necessity, we present an autonomy manager based on the autonomous architecture. In this paper, we design a path planning software architecture and LPP manager by using open autonomous architecture which is previously designed by ADD. Field test is conducted with UGV in order to verify the performance of LPP Manager based on the Autonomous Architecture with scenarios.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.11a
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• pp.9-10
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• 2012

Efficient operation of construction equipment has become more and more important in the competitive construction environment. Accurate measurement of performance rate of construction equipment is a critical factor for a construction project planning. However, it might be quite difficult to measure the performance rate due to diverse practical limitations such as continuously variable performance rate of construction equipment, considerable indirect cost, large construction field, and so on. Therefore, the purpose of this paper is to propose an automatic algorithm that measures a performance rate of construction equipment with a multipurpose composite sensor. It is expected that the algorithm compiles database on construction equipment and in advance, facilitates efficient operation of construction equipment.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.713-716
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• 1991

This paper describes a methodology of mobile robot navigation which is designed to carry heavy payloads at high speeds to be used in FMS(Flexible Manufacturing System) without human control. Intelligent control scheme using fuzzy logic is applied to the navigation control. It analyzes sensor readings from multi-sensor system, which is composed of ultrasonic sensors, infrared sensors and odometer, for environment learning, planning, landmark detecting and system control. And it is implemented on a physical robot, AGV(Autonomous Guided Vehicle) which is a two-wheeled, indoor robot. An on-board control software is composed of two subsystems, i.e., AGV control subsystem and Sensor control subsystem. The results show that the navigation of the AGV is robust and flexible, and a real-time control is possible.

• Journal of the Korean Society of Industry Convergence
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• v.21 no.3
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• pp.125-131
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• 2018

In this paper, we propose a homogeneous multisensor-based navigation algorithm for a mobile robot, which is intelligently searching the goal location in unknown dynamic environments with moving obstacles using multi-ultrasonic sensor. Instead of using "sensor fusion" method which generates the trajectory of a robot based upon the environment model and sensory data, "command fusion" method by fuzzy inference is used to govern the robot motions. The major factors for robot navigation are represented as a cost function. Using the data of the robot states and the environment, the weight value of each factor using fuzzy inference is determined for an optimal trajectory in dynamic environments. For the evaluation of the proposed algorithm, we performed simulations in PC as well as real experiments with mobile robot, AmigoBot. The results show that the proposed algorithm is apt to identify obstacles in unknown environments to guide the robot to the goal location safely.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.7-12
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• 1992

This paper proposes a sensor-based path planning method which utilizes fuzzy logic and neural network for obstacle avoidance of a mobile robot in uncertain environments. In order to acquire the information about the environment around the mobile robot, the ultrasonic sensors mounted on the front of mobile robot are used. The neural network, whose inputs are preprocessed by ultrasonic sensor readings, informs the mobile robot of the situation of environment in which mobile robot is at the present instant. Then, according to the situation class, the fuzzy rules are fired to make a decision on the mobile robot action. In addition, this method can be implemented real time since the number of fuzzy rules used to avoid the obstacle is small. Fuzzy rules are constructed based on the human reasoning and tuned by iterative simulations. The effective of the proposed avoidance method is verified by a series of simulations.

• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.18-21
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• 2009

2006년 발사되어 현재 운영 중인 아리랑위성 2호의 활용도를 높이기 위해서는 촬영 지역의 radiance 값에 따라 아리랑위성 2호의 TDI와 Vp gain 값을 조절해야만, 10bit인 radiometric resolution을 넓게 사용할 수가 있다. 촬영하고자 하는 지역의 radiance 값을 미리 예측하기 위해서, 한 픽셀이 $250km\times250km$인 월별 전 세계 radiance 평균값을 기본으로 촬영 날짜, 촬영 시각, 촬영 지역의 기후 조건 및 아리랑위성 2호의 ON-to-Radiance 변화 계수 값들을 사용하여서 최종 At-sensor radiance 값을 계산하는 알고리즘을 개발하였다. 실제 아리랑위성 2호의 자료수집계획 작성 시에 참고 값으로 사용 중이고, 80%이상의 정확도가 있음을 확인하였다.

• Journal of the Korean Society of Industry Convergence
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• v.4 no.3
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• pp.339-347
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• 2001

Automatic automobile has been studied as the alternative energy system and the production flow automation device recently. But this is dependent on the import production, and its position cannot be controlled free from the fixed path. It is difficult to control the automobile position because of the eccentricity of inertia monent, slip and roughness between wheel and road surface. This problems is solved for the controller to be feedbacked the data of the multi-sensor system consisting of the rotary encoder and electronic compass. The proportional Integrated controller in the modified Ziegler-Nichols method is made up with Hitachi 7034 microprocessor. To the real time control the mechanical, electrical and electronic hardware and software device is produced by myself. The RF data of automobile speed and position is supplied to the remote PC to be displayed the automobile condition. By the experinent of the forward, spin, point path planning, it is known for autombile.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.3
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• pp.277-282
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• 2011

This paper describes a sensor fusion-based semantic map building which can improve the capabilities of a mobile robot in various domains including localization, path-planning and mapping. To build a semantic map, various environmental information, such as doors and cliff areas, should be extracted autonomously. Therefore, we propose a method to detect doors, cliff areas and robust visual features using a laser scanner and a vision sensor. The GHT (General Hough Transform) based recognition of door handles and the geometrical features of a door are used to detect doors. To detect the cliff area and robust visual features, the tilting laser scanner and SIFT features are used, respectively. The proposed method was verified by various experiments and showed that the robot could build a semantic map autonomously in various indoor environments.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.9
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• pp.825-832
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• 2004

A small biped robot system is designed and implemented. The robot system consists of a mechanical robot body, a control system, a sensor system, and a user interface system. The robot has 12 dofs for two legs, 6 dofs for two arms, 2 dofs for a neck, so it has total 20 dofs to have dexterous motion capability. The implemented robot has the capability of performing intelligent actions such as playing soccer, resisting external forces, and walking on a slope terrain. In this paper, we focus on the robot's capability of playing soccer. The robot uses a color CCD camera attached on its head as a sensor for playing soccer. To make the robot play soccer with only one camera, an algorithm, which consists of searching, localization, and motion planning, is proposed and experimented. The results show that the robot can play soccer successfully in the given environments.

• Journal of Sensor Science and Technology
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• v.27 no.4
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• pp.209-215
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• 2018

Recent research in animal behavior has shown that gradient information plays an important role in finding food and home. It is also important in optimization of performance because it indicates how the inputs should be adjusted for maximization/minimization of a performance index. We introduce perturbation as an additional input to obtain gradient information. Unlike the typical approach of calculating the gradient from the derivative, the proposed processing is very robust to noise since it is performed as a summation. Experimental results prove the validity of the process of spatial gradient acquisition. Quantitative indices for measuring the effect of the amplitude and the frequency are developed based on linear regression analysis. Drones are very useful for environmental monitoring and an autonomous path planning is required for unstructured environment. Guiding the drone for finding the origin of the interested physical property is done by estimating the gradient of the sensed value and generating the drone trajectories in the direction which maximizes the sensed value. Simulation results show that the proposed method can be successfully applied to identify the source of the physical quantity of interest by utilizing it for path planning of an autonomous drone in 3D environment.