Hyunhak Cho

Positioning and Driving Control of Fork-type Automatic Guided Vehicle With Laser Navigation

We designed and implemented a fork-type automatic guided vehicle (AGV) with a laser guidance system. Most previous AGVs have used two types of guidance systems: magnetgyro and wire guidance. However, these guidance systems have high costs, are difficult to maintain with changes in the operating environment, and can drive only a pre-determined path with installed sensors. A laser guidance system was developed for addressing these issues, but limitations including slow response time and low accuracy remain. We present a laser guidance system and control system for AGVs with laser navigation. For analyzing the performance of the proposed system, we designed and built a fork-type AGV, and performed repetitions of our experiments under the same working conditions. The results show an average positioning error of 51.76 mm between the simulated driving path and the driving path of the actual fork-type AGV. Consequently, we verified that the proposed method is effective and suitable for use in actual AGVs.

Map building of indoor environment using laser range finder and geometrics

This paper proposes map building method. Sensors such as a laser range finder, a gyroscope, encoders multiply compose the system. Generally, mobile robot can measure its relative position using a gyroscope and encoders in an environment. However, in this case, a large number of errors occur due to accumulative errors of sensor over time. Therefore, a map based on feature points is used. And an absolute position is measured by the feature points and geometrics. As combining the relative position and the absolute position, mobile robot can recognize its position. According to compositional data of a map, in case of a laser range finder, it takes a long time when adding a map or calculating a position of mobile robot. Therefore, it is necessary to arrange the feature points for computational time and map building. In this paper, a map was formed by laser range finder and features of geometrics. As a result of proposed method, the map was built efficiently in an aspect of time.

Improvement of Position Accuracy of Magnetic Guide Sensor Using Kalman Filter

This paper is represented to research of method to improve the performance of magnetic guide sensor using Kalman filter. The magnetic guide sensor is calculating the center position of the AGV (Automatic Guided Vehicle) by to measure the magnetic information of a magnetic substance that is magnetic tape, magnet spot. The existing magnetic guide sensor is the device that calculates the center position of the AGV using the magnetic force of the measured data that is one pole to measure each axis at the histogram algorithm. But, the existing method is unfit for requiring the high accuracy such as industrial setting because of interference between sensors and the effect by disturbance. Therefore, in this paper proposed method that increases the position accuracy of the magnetic guide sensor using Kalman filter. To verify the proposed method, we use the AGV to install magnetic guide sensor. And it compare the positioning accuracy of the propose method and the commercialized magnetic guide sensor. As a result, the proposed method was found 24.78% to improve the positioning accuracy of the proposed method than that of the commercialized magnetic guide sensor.

Improvement of Positioning Accuracy of Laser Navigation System using Particle Filter

This paper presents a method for improving the positioning accuracy of the laser navigation. As a wireless navigation system, the laser navigation which is more flexible than a wired guidance system is used for the localization and control of an AGV(automatic guided vehicle). However, the laser navigation causes the large positioning error while the AGV turns or moves fast. To solve the problem, we propose the method for improving the positioning accuracy of the laser navigation using particle filter which has robust and reliable performance in non-linear/non-gaussian systems. For the experiment, we use the actual fork-type AGV. The AGV has a gyro, two encoders and a laser navigation. To verify the performance, the proposed method is compared with the laser navigation which is a product. In the experimental result, we verified that the proposed method could improve the positioning accuracy by approximately 66.5%.

Independence localization system for Mecanum wheel AGV

This study deals with the independent localization system of AGV which is sensor fusion strapdown inertial navigation system and laser navigation to develope more fast and accurate for the AGV. the strapdown inertial navigation system is very expensive and has large system. Hence, we propose the independent and stable localization system with sensor fusion using a low-cost strapdown inertial navigation system which consists of MEMS inertial sensor. To reduce errors of the strapdown inertial navigation system, we performed compensation using proportional method to gyro sensor and low-pass filter to accelerometer. We used transformation matrix with a result of attitude reference using Kalman filter. Finally, to compensate the result of error, fuzzy inference system is used. To verify the performance of the proposed system, experiments is performed with a Mecanum wheeled AGV and a forklift AGV. In the experiments result, we confirmed that the proposed system can estimate the position.

Positioning Accuracy Improvement of Analog-type Magnetic Positioning System using Fuzzy Inference System

This paper presents a development of an analog type magnetic positioning system and its positioning accuracy improvement using fuzzy inference system. As the magnetic positioning system used on a magnet-gyro guidance system for AGV(automatic guided vehicle), it measures a position of magnet embedded in floor of the work place. The existing product of the magnetic positioning system is very expensive in Korea because it is being sold in a foreign country exclusively. Moreover, the positioning accuracy of the product is low because it uses digital type unipolar hall sensors. Hence, we developed the magnetic positioning system by ourselves and improved the positioning accuracy of the developed magnetic positioning system using fuzzy inference system. For experiment, we used the analog type magnetic positioning system which we have developed, and compared the performance of the proposed method with the performance of the existing positioning method for the magnetic positioning system. In experimental results, we verified that the proposed method improved the positioning accuracy of the magnetic positioning system.

Indoor SLAM application using geometric and ICP matching methods based on line features

Abstract This study presents an autonomous guided vehicle (AGV) with simultaneous localization and map building (SLAM) based on matching method, and extended Kalman filter SLAM. In general, the AGV is a large mobile robot that is used in transportation to carry cargoes, and it is guided using wired or wireless guidance systems. The guidance system based AGV accounts for a majority of robots in the mobile robot industry. However, in semiconductor factories, landmarks are unavailable; hence, the existing system has not been used in the mentioned environments. Therefore, the SLAM technology is applied in the environments, and can guide the AGV without landmarks. However, the accuracy of the SLAM can be low owing to measurement error of sensors and a cumulative calculation caused by localization sensors. Therefore, the accuracy is frequently assumed to be incorrect; moreover, the accuracy of the built map is low. In order to solve the problems, this study proposes the AGV with the SLAM based on matching methods; two matching method; geometric matching method and iterative closest point algorithm. The performance of the proposed method is compared with typical methods such as singular value decomposition / RIGID transformation based technologies using feature-point-based SLAM and is compared with the aforementioned two methods using the extended Kalman filter SLAM. The proposed method is more efficient than the typical methods used in the comparison.

Automatic brightness adjustment system by fuzzy inference system for object recognition

A camera has been widely used in practical fields with a diversity of purposes recently. There is a variety purpose of photography: images for memory, medical images for diagnosis, images for object recognition, surveillance images, and so on. In case of images for object recognition, the clarity of images is necessary to analyze the images which are obtained using vision sensors. However, a brightness of the image highly depends on the intensity of illumination in the certain environment. Therefore, we propose a method to solve the problems mentioned above by adjusting brightness automatically by utilizing CIE L∗a∗b∗ color space and fuzzy inference system. At first, the proposed method adjusts the brightness of a given image by considering both RGB component and L component of CIE L∗a∗b∗ color space. Secondly, the proposed method applies the fuzzy inference system to determine adjustment coefficients of each pixel for adjusting brightness of the image. Through the processes as mentioned above, we can obtain the result which is adjusted its brightness. To verify the proposed method, we compare the result image with two different images, a reference image, and an adjusted image by using offset. It is confirmed that the proposed method can adjust a given image efficiently and automatically.

Performance Comparison of Probabilistic Methods Based Correction Algorithms for Localization of Autonomous Guided Vehicle

This paper presents performance comparison of probabilistic methods based correction algorithms for localization of AGV (Autonomous Guided Vehicle). Wireless guidance systems among the various guidance systems guides the AGV using position information from localization sensors. Laser navigation is mostly used to the AGV of a wireless type, however the performance of the laser navigation is influenced by a slow response time, big error of rotation driving and a disturbance with light and reflection. Therefore, the localization error of the laser navigation by the above-mentioned weakness has a great effect on the performance of the AGV. There are many different methods to correct the localization error, such as a method using a fuzzy inference system, a method with probabilistic method and so on. Bayes filter based estimation algorithms (Kalman Filter, Extended Kalman Filter, Unscented Kalman Filter and Particle Filter) are mostly used to correct the localization error of the AGV. This paper analyses performance of estimation algorithms with probabilistic method at localization of the AGV. Algorithms for comparison are Extended Kalman Filter, Unscented Kalman Filter and Particle Filter. Kalman Filter is excluded to the comparison, because Kalman Filter is applied only to a linear system. For the performance comparison, a fork-type AGV is used to the experiments. Variables of algorithms is set experiments based heuristic values, and then variables of same functions on algorithms is set same values.

Image brightness adjustment system based on ANFIS by RGB and CIE L∗a∗b∗

This paper proposes the method to adjust brightness information by applying CIE L∗a∗b∗ color space and adaptive neuro-fuzzy inference system. The image which is already captured by vision sensor should be adjusted brightness to recognize objects in an image. In case of proper intensity of lights, the clarity of an image is good to recognize objects. However, in case of improper intensity of lights, the image has darkish regions. It will leads to reduce success of object recognition. To make up for this week point, we adjust the image, which is a darkish image, by controlling brightness information of an image. Brightness information can be represented by CIE L∗a∗b∗ color space. So based on CIE L∗a∗b∗ color space, adaptive neuro-fuzzy inference system is implemented as control function. Control function carries out adjusting of brightness information by dealing with the value of L component of CIE L∗a∗b∗ color space. L component describes brightness information of an image. The values which is calculated by adaptive neuro-fuzzy inference system is called the adjustment coefficient. Finally, the adjustment coefficient is added to L component for adjusting brightness information. To verify the propose method, we calculated color difference with respect to RGB and CIE L∗a∗b∗ color space. As experimental results, the propose method can reduce color difference and makes the target image will be similar with reference image under proper intensity of lights.