• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6A
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• pp.481-487
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• 2005

In order to increase the autonomous navigation capability of a mobile robot, it is very crucial to develop a method for recognizing a priori known environmental characteristics. This paper proposes an ultrasonic sensor based real-time method for recognizing a priori known indoor environmental characteristics like a wall and corner. The ultrasonic sensor consists of an ultrasonic transmitter and two ultrasonic receivers placed symmetrically about the transmitter. Unlike previous methods the information obtained from the sensor is processed in real-time by extended Kalman filter to be able to correct the position and orientation of robot with respect to known environmental characteristics.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.1
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• pp.58-69
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• 2014

This paper proposes a method for predicting maximum friction coefficients and optimal slip ratios as optimal control parameters for traction control or slip control of autonomous mobile robots on rough terrain. This paper focuses on strength of ground surface which indicates different characteristics depending on material types on surface. Strength of various material types can be estimated by Willoughby sinkage model and by a developed testbed which can measure forces, velocities, and displacements generated by wheel-terrain interaction. Estimated strength is collaborated on building improved Brixius model with friction-slip data from experiments with the testbed over sand and grass material. Improved Brixius model covers widespread material types in outdoor environments on predicting friction-slip characteristics depending on strength of ground surface. Thus, a prediction model for obtaining optimal control parameters is derived by partial differentiation of the improved Brixius model with respect to slip. This prediction model can be applied to autonomous mobile robots and finally gives secure maneuverability on rough terrain. Proposed method is verified by various experiments under similar conditions with the ones for real outdoor robots.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.39 no.1
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• pp.77-83
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• 2003

Autonomous mobile robots based on the Web have been already used in public places such as museums. There are many kinds of problems to be solved because of the limitation of Web and the dynamically changing environment. We present a methodology for intelligent mobile robot that demonstrates a certain degree of autonomy in navigation applications. In this paper, we focus on a mobile robot navigator equipped with neuro-fuzzy controller which perceives the environment, make decisions, and take actions. The neuro-fuzzy controller equipped with collision avoidance behavior and target trace behavior enables the mobile robot to navigate in dynamic environment from the start location to goal location. Most telerobotics system workable on the Web have used standard Internet techniques such as HTTP, CGI and Scripting languages. However, for mobile robot navigations, these tools have significant limitations. In our study, C# and ASP.NET are used for both the client and the server side programs because of their interactivity and quick responsibility. Two kinds of simulations are performed to verify our proposed method. Our approach is verified through computer simulations of collision avoidance and target trace.

• The Journal of Korea Robotics Society
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• v.11 no.2
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• pp.60-72
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• 2016

This study proposes a multi-robot system, using multiple autonomous robots, to explore concrete structures and assist in their maintenance by sealing any cracks present in the structure. The proposed system employed a new self-localization method that is essential for autonomous robots, along with a visualization system to recognize the external environment and to detect and explore cracks efficiently. Moreover, more efficient crack search in an unknown environment became possible by arranging the robots into search areas divided depending on the surrounding situations. Operations with increased efficiency were also realized by overcoming the disadvantages of the infeasible logical behavioral model design with only six basic behavioral strategies based on distributed control-one of the methods to control swarm robots. Finally, this study investigated the efficiency of the proposed multi-robot system via basic sensor testing and simulation.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.23-30
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• 2024

This paper studies a GPS error filtering method that takes into account the continuity of the ongoing path to enhance the safety of autonomous agricultural mobile robots. Real-Time Kinematic Global Positioning System (RTK-GPS) is increasingly utilized for robot position evaluation in outdoor environments due to its significantly higher reliability compared to conventional GPS systems. However, in orchard environments, the robot's current position obtained from RTK-GPS information can become unstable due to unknown disturbances like orchard canopies. This problem can potentially lead to navigation errors and path deviations during the robot's movement. These issues can be resolved by filtering out GPS information that deviates from the continuity of the waypoints traversed, based on the robot's assessment of its current path. The contributions of this paper is as follows. 1) The method based on the previous waypoints of the traveled path to determine the current position and trajectory. 2) GPS filtering method based on deviations from the determined path. 3) Finally, verification of the navigation errors between the method applying the error filter and the method not applying the error filter.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.2
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• pp.391-399
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• 2012

This paper proposes a particle filter approach for SLAM(Simultaneous Localization and Mapping) of a mobile robot. The SLAM denotes estimation of both the robot location and map while the robot navigates in an unknown environment without map. The proposed method estimates robot location simultaneously with the locations of the ultrasonic beacons which constitute landmarks for navigation. The particle filter method represents the locations of the robot and landmarks in probabilistic manner by the distribution of particles. The method takes care of the uncertainty of the landmarks' location as well as that of the robot motion. Therefore, the locations of the landmarks are updated including uncertainty at every sampling time. Performance of the proposed method is verified through simulation and experiments. The method yields practically useful mapping information even if the range data from the landmarks include random noise. Also, it provides more accurate and robust estimation of the robot location than the usual least squares methods or dead-reckoning method.

• Journal of Auto-vehicle Safety Association
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• v.15 no.2
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• pp.42-48
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• 2023

In recent years, automated vehicles have garnered attention in the multidisciplinary research field, promising increased safety on the road and new opportunities for passengers. High-Definition (HD) maps have been in development for many years as they offer roadmaps with inch-perfect accuracy and high environmental fidelity, containing precise information about pedestrian crossings, traffic lights/signs, barriers, and more. Demonstrating autonomous driving requires verification of driving on actual roads, but this can be challenging, time-consuming, and costly. To overcome these obstacles, creating HD maps of real roads in a simulation and conducting virtual driving has become an alternative solution. However, existing HD maps using high-precision data are expensive and time-consuming to build, which limits their verification in various environments and on different roads. Thus, it is challenging to demonstrate autonomous driving on anything other than extremely limited roads and environments. In this paper, we propose a new and simple method for implementing HD maps that are more accessible for autonomous driving demonstrations. Our HD map combines the CARLA simulator and OpenStreetMap (OSM) data, which are both open-source, allowing for the creation of HD maps containing high-accuracy road information globally with minimal dependence. Our results show that our easily accessible HD map has an accuracy of 98.28% for longitudinal length on straight roads and 98.42% on curved roads. Moreover, the accuracy for the lateral direction for the road width represented 100% compared to the manual method reflected with the exact road data. The proposed method can contribute to the advancement of autonomous driving and enable its demonstration in diverse environments and on various roads.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.603-605
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• 2021

With the expansion of the indoor service-providing robot market and the electrification of automobiles, research on autonomous driving is being actively conducted. In general, in the case of outside, the location is mainly recognized through GPS, and location positioning is performed indoors using technologies such as WiFi, UWB (Ultra-Wide Band), VLP, LiDAR, and Vision. In this paper, we introduce a system for location-positioning using LED lights with different color temperatures in an indoor environment. After installing LED lights in a simulated environment such as a tunnel, it was shown that information about the current location can be obtained through the analysis of chromaticity values according to location. Through this, it is expected to be able to obtain information about the location of the vehicle in the tunnel and the movement of the device in a room such as a warehouse or a factory.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.51-56
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• 2003

The main goal of existing mobile robot system was a complete autonomous navigation and the vision information was just used as an assistant way such as monitoring For this reason, the researches have been going towards sophistication of autonomousness gradually and the production costs also has been risen. However, it is also important to control remotely an inexpensive mobile robot system which has no intelligence at all. Such systems may be much more effective than fully autonomous systems in practice. Visual information from a simple camera and distance information from ultrasonic sensors are used for this system. Collision avoidance becomes the most important problem for this system. In this paper, we developed a force feedback joystick to control the robot system remotely with collision avoiding capability. Fuzzy logic is used for the algorithm in order to implement the expert s knowledge intelligently. Some experimental results show the force feedback joystick werks very well.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.46-49
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• 2008

본 논문은 이동 로봇의 이동 및 거리 센싱의 불확실성을 고려한 시뮬레이터 개발에 대해 소개한다. 이동 로봇은 구동기, 바닥의 불안정성, 바퀴 및 구동 기구의 불확실성, 그리고 기타 구조적으로 어려운 다양한 원인으로 동작 명령과 차이가 있게 이동한다. 또한 이동 로봇에 장착된 각종 센서는 센서 자체의 불안정성, 주변 환경의 불안정성등에 의하여 정확한 측정값을 출력하지 못한다. 이러한 이동 및 센서의 불안정성은 로봇의 자율 주행 알고리즘의 구현이 가장 큰 장애물이 되고 있다. 예측하기 어려운 불안정성을 고려하지 않은 알고리즘은 실제 환경에서 필연적으로 동작에 실패하여 크고 작은 사고를 일으킨다. 따라서 알고리즘의 검증을 위해 시뮬레이터가 각종 불확실성을 포함하여 로봇 동작이 실제에 유사하도록 하여야 한다. 본 연구에서는 이동 로봇의 이동과 센싱에 불확실성을 포함하도록한 시뮬레이터를 개발하였다. 다양한 센서들 중 이동 로봇의 위치 추정, 장애물 인식, 지도 작성등에 가장 기본적으로 사용되는 영역 센서를 대상으로 불확실성을 구현하였다. 개발된 시뮬레이터를 사용하여 알고리즘을 검증하는 경우와 불확실성을 고려하지 않은 시뮬레이터를 사용하여 알고리즘을 검증하는 경우를 비교하여, 제안된 시뮬레이터의 성능을 검증하였다.