• Journal of Digital Convergence
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• v.19 no.12
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• pp.145-150
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• 2021

In order to continuously perform quarantine in public places, it is not easy to secure manpower, but using self-driving-based robots can solve problems caused by manpower. Self-driving-based quarantine robots can continuously prevent the spread of harmful viruses and diseases in public institutions and hospitals without additional manpower. The location of the autonomous driving function was estimated by applying the Pinnacle filter algorithm, and the UV sterilization system and chlorine dioxide injection system were applied for quarantine. The driving time is more than 3 hours and the position error is 0.5m.Soon, the stop-avoidance function was operated at 95% and the obstacle detection distance was 1.5 m, and the automatic charge recovery was charged by moving to the charging cradle at the remaining 10% of the battery capacity. As a result of quarantine with an unmanned quarantine system, UV sterilization is 99% and chlorine dioxide is sterilized more than 95%, which can contribute to reducing enormous social costs.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.868-871
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• 2016

Recently, Unmanned vehicle and Wearable Technology using iot research is being carried out. The unmanned vehicle is the result of it technology. Robots, autonomous navigation vehicle and obstacle avoidance, data communications, power, and image processing, technology integration of a unmanned vehicle or an unmanned robot. The final goal of the unmanned vehicle manual not autonomous by destination safely and quickly reaching. This paper managed to cover One of the key skills of unmanned vehicle is to image processing. Currently battery technology of unmanned vehicle can drive for up to 1 hours. Therefore, we use the Raspberry Pi 3 to reduce power consumption to a minimum. Using the Raspberry Pi 3 and to develop an image recognition system. The goal is to propose a system that recognizes all the objects in the image received from the camera.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.312-321
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• 2003

In this paper, we implement robot which are ability to recognize obstacles and moving automatically to destination. we present two results in this paper; hardware implementation of image processing board and software implementation of visual feedback algorithm for a self-controlled robot. In the first part, the mobile robot depends on commands from a control board which is doing image processing part. We have studied the self controlled mobile robot system equipped with a CCD camera for a long time. This robot system consists of a image processing board implemented with DSPs, a stepping motor, a CCD camera. We will propose an algorithm in which commands are delivered for the robot to move in the planned path. The distance that the robot is supposed to move is calculated on the basis of the absolute coordinate and the coordinate of the target spot. And the image signal acquired by the CCD camera mounted on the robot is captured at every sampling time in order for the robot to automatically avoid the obstacle and finally to reach the destination. The image processing board consists of DSP (TMS320VC33), ADV611, SAA7111, ADV7l76A, CPLD(EPM7256ATC144), and SRAM memories. In the second part, the visual feedback control has two types of vision algorithms: obstacle avoidance and path planning. The first algorithm is cell, part of the image divided by blob analysis. We will do image preprocessing to improve the input image. This image preprocessing consists of filtering, edge detection, NOR converting, and threshold-ing. This major image processing includes labeling, segmentation, and pixel density calculation. In the second algorithm, after an image frame went through preprocessing (edge detection, converting, thresholding), the histogram is measured vertically (the y-axis direction). Then, the binary histogram of the image shows waveforms with only black and white variations. Here we use the fact that since obstacles appear as sectional diagrams as if they were walls, there is no variation in the histogram. The intensities of the line histogram are measured as vertically at intervals of 20 pixels. So, we can find uniform and nonuniform regions of the waveforms and define the period of uniform waveforms as an obstacle region. We can see that the algorithm is very useful for the robot to move avoiding obstacles.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.12
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• pp.6986-6992
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• 2014

The generalized Voronoi graph (GVG) is a topological map of a constrained environment. This is defined in terms of workspace distance measurements using only sensor-provided information, with a robot having a maximum distance from obstacles, and is the optimum for exploration and obstacle avoidance. This is the safest path for the robot, and is very significant when studying the GVG edges of highly articulated robots. In previous work, the point-GVG edge and Rod-GVG were built with point robot and rod robot using sensor-based control. An attempt was made to use a higher degree of freedom robot to build GVG edges. This paper presents GVG-based a new local roadmap for the two-link robot in the constrained two-dimensional environment. This new local roadmap is called the two-identical-link generalized Voronoi graph (L2-GVG). This is used to explore an unknown planar workspace and build a local roadmap in an unknown configuration space $R^2{\times}T^2$ for a planar two-identical-link robot. The two-identical-link GVG also can be constructed using only sensor-provided information. These results show the more complex properties of two-link-GVG, which are very different from point-GVG and rod-GVG. Furthermore, this approach draws on the experience of other highly articulated robots.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.639-645
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• 2018

This paper is intended to develop a decision model that can be applied to autonomous vehicles and autonomous mobile vehicles. The developed module has an independent configuration for application in various driving environments and is based on a platform for organically operating them. Each module is studied for decision making on lane changes and for securing safety through reinforcement learning using a deep learning technique. The autonomous mobile moving body operating to change the driving state has a characteristic where the next operation of the mobile body can be determined only if the definition of the speed determination model (according to its functions) and the lane change decision are correctly preceded. Also, if all the moving bodies traveling on a general road are equipped with an autonomous driving function, it is difficult to consider the factors that may occur between each mobile unit from unexpected environmental changes. Considering these factors, we applied the decision model to the platform and studied the lane change decision system for implementation of the platform. We studied the decision model using a modular learning method to reduce system complexity, to reduce the learning time, and to consider model replacement.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.11_12
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• pp.682-691
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• 2005

We present a two-step evolution system that produces controllable virtual creatures in physically simulated 3D environment. Previous evolutionary methods for virtual creatures did not allow any user intervention during evolution process, because they generated a creature's shape, locomotion, and high-level behaviors such as target-following and obstacle avoidance simultaneously by one-time evolution process. In this work, we divide a single system into manageable two sub-systems, and this more likely allowsuser interaction. In the first stage, a body structure and low-level motor controllers of a creature for straight movement are generated by an evolutionary algorithm. Next, a high-level control to follow a given path is achieved by a neural network. The connection weights of the neural network are optimized by a genetic algorithm. The evolved controller could follow any given path fairly well. Moreover, users can choose or abort creatures according to their taste before the entire evolution process is finished. This paper also presents a new sinusoidal controller and a simplified hydrodynamics model for a capped-cylinder, which is the basic body primitive of a creature.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.1
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• pp.20-27
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• 2018

In this paper, the problem of a double-parked car has been investigated and a method to solve it is studied. Double parking is very common in a public parking lot with insufficient parking space. If a double-parked car blocked the way, a person needs to push the double-parked car to move it. The problem is that moving a double-parked car with hands is very hard and dangerous, especially for the old and the weak. To solve the above mentioned problem, an automatic mover for a double-parked car is proposed and developed. The basic idea is that a double-parked car can be moved by rolling its one wheel. Two rollers are designed and manufactured, which are used to roll a wheel of a double-parked car. The developed automatic mover has two rollers, two driving wheels, and four castors. It also has several ultrasonic sensors so that it can detect obstacles in the way and prevent possible collision. It is verified through several experiments that the developed automatic mover can move a double-parked car safely and easily.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.4
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• pp.1824-1832
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• 2011

In this paper, a smart home service robot McBot II is newly developed in much more practical and intelligent system than McBot I which we had developed a few years ago. Thus far, vacuum-cleaners have lightened the burden of household chores but the operational labor that vacuum-cleaners entail has been very severe. Recently, a cleaning robot was commercialized to solve but it also was not successful because it still had the problem of mess-cleanup, which pertained to the clean-up of large trash and the arrangement of newspapers, clothes, etc. Hence, we develop a new home mess-cleanup robot McBot II to completely overcome this problem on real environments. The mechanical design and the basic control of McBot II, which performs mess-cleanup function etc. in house, is actually focused in this paper. McBot II is mechanically modeled in the same method that the human works in door by using the waist and the hands. The big-ranged vertical lift and the shoulder joints to be able to forward move are mechanically designed for the operating function as the human's waist when the robot works. The mobility of McBot II is designed in the holonomic mobile robot for the collision avoidance of obstacle and the high speed navigation on the small area in door. Finally, good performance of McBot II, which has been optimally desinged, is confirmed through the experimental results for the control of the robotic body, mobility, arms and hands in this paper.