• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.498-504
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• 2005

A fuzzy logic for collision avoidance of underwater robots is proposed in this paper. The VFF(Virtual Force Field) method, which is widely used in the field of mobile robots, is modified for application to the autonomous navigation of underwater robots. This Modified Virtual Force Field(MVFF) method using the fuzzy logic can be used in either track keeping or obstacle avoidance. Fuzzy logics are devised to handle various situations which can be faced during autonomous navigation of underwater robots. The obstacle avoidance algorithm has the ability to handle multiple static obstacles. Results of simulation show that the proposed method can be efficiently applied to obstacle avoidance of the underwater robots.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.4
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• pp.307-314
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• 2013

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.

• Journal of Navigation and Port Research
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• v.35 no.2
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• pp.121-130
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• 2011

The localization of vehicle is an important part of an unmanned vehicle control problem. Pseudolite ultrasonic system(PUS) is the method to find an absolute position with a high accuracy by using ultrasonic sensor. And Gyro is the inertial sensor to measure yaw angle of vehicle. PUS can be able to estimate the position of mobile robot precisely, in which errors are not accumulated. And Gyro is a more faster measure method than PUS. In this paper, we suggest a more accuracy method of calculating PUS which is numerical analysis approach named Newtonian method. And also propose the fusion method to increase the accuracy of estimated angle on moving vehicle by using PUS and Gyro integrated system by Kalman filtering. To control the 4WS unmanned vehicle, the trajectory following algorithm is suggested. And the new concept arbitration of goal controller is suggested. This method considers the desirability function of vehicle state. Finally, the performances of Newtonian method and designed controller were verified from the experimental results with the 4WS vehicle scaled 1/10.

• Journal of Ocean Engineering and Technology
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• v.31 no.2
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• pp.170-176
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• 2017

In this paper, we propose an underwater localization scheme through the fusion of an inertial navigation system (INS) and the received signal strength (RSS) of electromagnetic (EM) wave sensors to guarantee precise localization performance with high sampling rates. In this localization scheme, the INS predicts the pose of the unmanned underwater vehicle (UUV) by dead reckoning at every step, and the RF sensors corrects the UUV position functions using the Earth-fixed reference when the UUV is located in underwater wireless sensor networks (UWSN). The localization scheme and state modeling were conducted in the extended Kalman filter framework, and UUV localization experiments were conducted in a basin environment. The scheme achieved reliable localization accuracy during long-term navigation, demonstrating the feasibility of exploiting EM wave attenuation as Earth-fixed reference sensors.

• Journal of Ocean Engineering and Technology
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• v.31 no.2
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• pp.177-182
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• 2017

This paper describes an unscented Kalman filter approach to estimate the bias in magnetic field measurements. A microelectromechanical systems attitude heading reference system (MEMS AHRS) was used to measure the magnetic field, together with the acceleration and angular rate. A magnetic field is usually used for yaw detection, while the acceleration serves to detect the roll and pitch. Magnetic field measurements are vulnerable to distortion due to hard-iron effect and soft-iron effect. The bias in the measurement accounts for the hard-iron effect, and this paper focuses on an approach to estimate this bias. The proposed method is compared with other methods through experiments that implement the navigation of an underwater robot using an AHRS and Doppler velocity log. The results verify that the compensation of the bias by the proposed method improves the navigation performance more than or comparable to the compensation by other methods.

• Journal of Drive and Control
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• v.19 no.4
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• pp.97-103
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• 2022

Low fertility rates and increased life expectancy further exacerbate the process of an aging society. This is also reflected in the gradual increase in the proportion of vulnerable groups in the social population. The demand for improved mobility among vulnerable groups such as the elderly or the disabled has greatly driven the growth of the electric-assisted mobility device market. However, such mobile devices generally require a certain operating capability, which limits the range of vulnerable groups who can use the device and increases the cost of learning. Therefore, autonomous driving technology needs to be introduced to make mobility easier for a wider range of vulnerable groups to meet their needs of work and leisure in different environments. This study uses mini PC Odyssey, Velodyne Lidar VLP-16, electronic device and Linux-based ROS program to realize the functions of working environment recognition, simultaneous localization, map generation and navigation of electric powered mobile devices for vulnerable groups. This autonomous driving mobility device is expected to be of great help to the vulnerable who lack the immediate response in dangerous situations.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.209-215
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• 2022

As intelligent autonomous driving vehicle development has become a big topic around the world, accurate reference dynamics estimation has been more important than before. Current systems generally use speed and heading information sensed from a distant point as a vehicle reference dynamic, however, the dynamics between different points are not same especially during rotating motions. In order to estimate properly estimate the reference dynamics from the information such as velocity and heading sensed at a point distant from the reference point such as center of gravity, this study proposes estimating reference dynamics from any location in the vehicle by combining the Bicycle and Ackermann models. A test system was constructed by implementing multiple GNSS/INS equipment on an Robot Operating System (ROS) and an actual car. Angle and speed errors of 10° and 0.2 m/s have been reduced to 0.2° and 0.06 m/s after applying the suggested method.

• Journal of Electrical Engineering and Technology
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• v.6 no.6
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• pp.806-816
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• 2011

Interest about social security has recently increased in favor of safety for infrastructure. In addition, advances in computer vision and pattern recognition research are leading to video-based surveillance systems with improved scene analysis capabilities. However, such video surveillance systems, which are controlled by human operators, cannot actively cope with dynamic and anomalous events, such as having an invader in the corporate, commercial, or public sectors. For this reason, intelligent surveillance systems are increasingly needed to provide active social security services. In this study, we propose a core technique for intelligent surveillance system that is based on swarm robot technology. We present techniques for invader enclosing using swarm robots based on multiple distributed object environment. The proposed methods are composed of three main stages: location estimation of the object, specified object tracking, and decision of the cooperative behavior of the swarm robots. By using particle filter, object tracking and location estimation procedures are performed and a specified enclosing point for the swarm robots is located on the interactive positions in their coordinate system. Furthermore, the cooperative behaviors of the swarm robots are determined via the result of path navigation based on the combination of potential field and wall-following methods. The results of each stage are combined into the swarm robot-based invader-enclosing technique on multiple distributed object environments. Finally, several simulation results are provided to further discuss and verify the accuracy and effectiveness of the proposed techniques.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.11
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• pp.2021-2032
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• 1992

A redundant sensor system, which consists of two incremental encoders and a gyro sensor, has been proposed for the estimation of the posture of mobile robots. A hardware system was built for estimating the heading angle change of the mobile robot from outputs of the gyro sensor. The proposed hardware system of the gyro sensor produced an accurate estimate for the heading angle change of the robot. A sensor data fusion algorithm has been developed to find the optimal estimates of the heading angle change based on the stochastic measurement equations of our readundant sensor system. The maximum likelihood estimation method is applied to combine the noisy measurement data from both encoders and gyro sensor. The proposed fusion algorithm demonstrated a satisfactory performance, showing significantly reduced estimation error compared to the conventional method, in various navigation experiments.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.6 s.312
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• pp.1-8
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• 2006

Many researches on path planning and obstacle avoidance for the fundamentals of mobile robot have been done recently. Informations from various sensors can be used to find obstacles and plan feasible path. In spite of many solutions of finding optimal path, each can be applied in only a constrained condition. This means that it is difficult to find university good algorithm. An optimal path with a complicated computation generates a time delay which cannot avoid moving obstacles. In this paper, we propose an algorithm of path planning and obstacle avoidance for mobile robot. We call the proposed method Random Access Sequence(RAS) method. In the proposed method, a small region is set first and numbers are assigned to its neighbors, then the path is selected using these numbers and cumulative numbers. It has an advantage of fast planning time and completeness of path if one exists. This means that new path selection may be possible within short time and that helps a robot to avoid obstacle in dynamic environments. Using the information of the start and destination position, the RAS can be performed for collision-free navigation by reforming feasible paths repeatedly in dynamic environments.