• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2337-2340
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• 2002

This paper presents the method of AGV(Automatic guided vehicle)'s moving environment(plane, corner, edge) recognition using SONAR sensor configuration. As for the SONAR sensor, the Crosstalk effect has been generally considered as an inevitable noisy phenomenon in the indoor environment. However, this effect can be used as a clue for classifying and localizing targets in the indoor environment if those can be controlled and used well. EERUF(error eliminate rapid ultrasonic firing) is a method for firing multiple ultrasonic sensors in mobile robot application and multi-echo mode of POLARIOD Device can reduce the Crosstalk effect. Here, Crosstalk effect was reduced using EERUF and applied to the AGV with a simple wall-following algorithm in the indoor environment. This method was tesed by a typical AGV with multi SONAR sensors in the laboratory environment.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1999.10a
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• pp.315-320
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• 1999

Obstacle Detection System(ODS) is a essential system for automated vehicle, such as AGV(Automatic Guided Vehicle), mobile robot. Automated vehicle must have a capability to detect and to avoid obstacles to guarantee a safe driving condition. To implement obstacle detection system, virtual bumper concept adapted. Like real bumper in a car, such as in the truck, it protects vehicle from collision using laser distance sensor. When an obstacle(such as other vehicle, building, etc) intrudes this virtual bumper area, a virtual force is calculated and produces necessary strategy to be able to avoid collision. In this paper, simplified virtual bumper concept is presented, and various problems when happens to implement are discussed.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.349-350
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• 2022

Recently, AI (Artificial Intelligence) has been applied to various technologies such as automatic driving, robot and smart communication. Currently, AI system is developed by software-based method using tensor flow, and GPU (Graphic Processing Unit) is employed for processing unit. In this work, we developed an FPGA-based (Field Programmable Gate Array) AI system , and report on image recognition system to realize the AI system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.239-246
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• 2020

In this paper, an automatic dewatering system for drying water from umbrellas is proposed. In the past, there were problems that users had to put on plastic for removing the water from umbrella by covering it with a plastic or using a water dryer that removes water by manually touching the umbrella to the water surface. But this method was hard to expect. To solve these problems, an air compressor was used to develop a system to remove water from the umbrella by detecting the weight of the umbrella with pressure sensor when the user puts the umbrella into the dewatering machine and driving the motor. It is expected that this invention will have economic and environmental effects by eliminating the use of waste vinyl.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.1C
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• pp.26-35
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• 2006

In this paper, an automatic mobile robot system for a intelligent path planning using the detection scheme of the spatial coordinates based on stereo camera is proposed. In the proposed system, face area of a moving person is detected from a left image among the stereo image pairs by using the YCbCr color model and its center coordinates are computed by using the centroid method and then using these data, the stereo camera embedded on the mobile robot can be controlled for tracking the moving target in real-time. Moreover, using the disparity map obtained from the left and right images captured by the tracking-controlled stereo camera system and the perspective transformation between a 3-D scene and an image plane, depth information can be detected. Finally, based-on the analysis of these calculated coordinates, a mobile robot system is derived as a intelligent path planning and a estimation. From some experiments on robot driving with 240 frames of the stereo images, it is analyzed that error ratio between the calculated and measured values of the distance between the mobile robot and the objects, and relative distance between the other objects is found to be very low value of $2.19\%$ and $1.52\%$ on average, respectably.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.232-241
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• 2017

This paper presents an automated cow-feeding system based on an AGV and screw conveyor for domestic livestock farms, which are becoming larger and more commercialized. The system includes a hopper module for loading pellet-type mixed feed at the top of the system, a transfer module mounted with a screw conveyor to transfer feed from the hopper module to the outlet module, an outlet module composed of belt conveyors, and an electromagnetic guided driving-type AGV. The weight of the loaded feed is measured by a load cell located under the transfer module. The system reads the feed discharge information stored in RFID tags installed in each cowshed cell, and a predetermined amount of feed is discharged while the AGV is moving. A cow-feed test system was constructed to determine the design parameters of the screw conveyor in the transfer module that determine the feeding capacity. These parameters include the screw's outer diameter, the screw shaft outer diameter, and screw pitch. The parameters were applied to the finalized cow-feed system construction. A DSP-based main controller and cow-feeding algorithm for different scenarios were also developed to control the system. Experimental results confirmed that the system could supply a total of 21 kg of feed uniformly at 420 g/s for a cowshed cell which has 7 cows. The driving distance was 5 m and the speed was 0.1 m/s. Thus, the proposed system could be applied to standardized domestic livestock farms.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.6
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• pp.1036-1043
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• 2022

This paper discusses about the development of a visual sensor that can be installed in an automatic mooring device to detect the berthing condition of a vessel. Despite controlling the ship's speed and confirming its location to prevent accidents while berthing a vessel, ship collision occurs at the pier every year, causing great economic and environmental damage. Therefore, it is important to develop a visual system that can quickly obtain the information on the speed and location of the vessel to ensure safety of the berthing vessel. In this study, a visual sensor was developed to observe a ship through an image while berthing, and to properly check the ship's status according to the surrounding environment. To obtain the adequacy of the visual sensor to be developed, the sensor characteristics were analyzed in terms of information provided from the existing sensors, that is, detection range, real-timeness, accuracy, and precision. Based on these analysis data, we developed a 3D visual module that can acquire information on objects in real time by conducting conceptual designs of LiDAR (Light Detection And Ranging) type 3D visual system, driving mechanism, and position and force controller for motion tilting system. Finally, performance evaluation of the control system and scan speed test were executed, and the effectiveness of the developed system was confirmed through experiments.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.388-389
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• 2022

Recently, AI (Artificial Intelligence) has been applied to various technologies such as automatic driving, robot and smart communication. Currently, AI system is developed by software-based method using tensor flow, and GPU (Graphic Processing Unit) is employed for processing unit. However, if software-based method employing GPU is used for AI applications, there is a problem that we can not change the internal circuit of processing unit. In this method, if high-level jobs are required for AI system, we need high-performance GPU, therefore, we have to change GPU or graphic card to perform the jobs. In this work, we developed a CNN-based FPGA (Field Programmable Gate Array) chip to solve this problem.