• Journal of the Korean Society of Industry Convergence
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• v.25 no.3
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• pp.341-347
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• 2022

This paper presents a comparison result of two simultaneous localization and mapping (SLAM) algorithms for navigation that have been proposed in literature. The performances of Extended Kalman Filter (EKF) SLAM under Gaussian condition, FastSLAM algorithms using Rao-Blackwellised method for particle filtering are compared in terms of accuracy of state estimations for localization of a robot and mapping of its environment. The algorithms were run using the same type of robot on indoor environment. The results show that the Particle filter based FastSLAM has the better performance in terms of accuracy of localization and mapping. The experimental results are discussed and compared.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.2
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• pp.145-153
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• 1995

This paper proposes a fuzzy algorithm for determining navigation path of an autonomous mobile robot in uncertain environment. The proposed fuzzy algorithm includes three type (MIN-TIME, ECONOMY, SAFETY) motion modes for the robot to get the ability to meet the ambiguous situation which the robot encounters. Ech ode is applied to both static and dynamic environmental situation. This paper concludes by showing the efficiency of the proposed method through some computer simulation results.

• The Journal of Korea Robotics Society
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• v.17 no.2
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• pp.198-208
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• 2022

In this paper, an autonomous driving system is implemented for the Segye AI Robot Race Competition that multiple vehicles drive simultaneously. By utilizing the ERP42-racing platform, RTK-GPS, and LiDAR sensors provided in the competition, we propose an autonomous driving system that can drive safely and quickly in a road environment with multiple vehicles. This system consists of a recognition, judgement, and control parts. In the recognition stage, vehicle localization and obstacle detection through waypoint-based LiDAR ROI were performed. In the judgement stage, target velocity setting and obstacle avoidance judgement are determined in consideration of the straight/curved section and the distance between the vehicle and the neighboring vehicle. In the control stage, adaptive cruise longitudinal velocity control based on safe distance and lateral velocity control based on pure-pursuit are performed. To overcome the limited experimental environment, simulation and partial actual experiments were conducted together to develop and verify the proposed algorithms. After that, we participated in the Segye AI Robot Race Competition and performed autonomous driving racing with verified algorithms.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.77-82
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• 2022

The effect of indoor air pollution on the human body is greater and more dangerous than outdoor air pollution. In general, a person stays indoors for a long time, and in a closed room, pollutants are continuously accumulated and the polluted air is better delivered to the lungs. Especially in the case of young children, it is very sensitive to indoor air and it is fatal. In addition, methods to reduce indoor air pollution, which cannot be ventilated with more frequent indoor activities and continuously increasing external fine dust due to Covid 19, are becoming more important. In order to improve the problems of the existing autonomous driving air purifying robot, this paper divided the map and Upper Confidence bounds applied to Trees(UCT) based algorithm to solve the problem of the autonomous driving robot not sterilizing a specific area or staying in one space continuously, and the problem of children who are vulnerable to indoor air pollution. We propose a space-adaptive autonomous driving air purifying robot for a green smart school that can be improved.

• Journal of Biosystems Engineering
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• v.34 no.4
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• pp.278-285
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• 2009

Navigation system is applied in variety of fields including the simple location positioning, autopilot navigation of unmanned robot tractor, autonomous guidance systems for agricultural vehicles, construction of large field works that require high precision and map making process. Particularly utilization of GPS (Global Positioning System) is very common in the present navigation system. This study introduces a navigation system for autonomous field robot that travels to the pre-input path using GPS information. Performance of the GPS- based navigation is highly depended on its receiving rate because GPS receivers do not acquire any navigation information in the period between the refresh intervals. So this study presents an algorithm that improves an accuracy of the navigation by estimation the positional information during the blind period of a low rate GPS receiver. In fact the algorithm calculated the robot's heading in a 50 Hz rate, so the blind period of an 1 Hz GPS receiver is extensively covered. Consequently implementation of the algorithm to the GPS based navigation showed an improvement in guidance accuracy. The conventional field robot directly carried an expensive control computer and sensors onboard, therefore the miniaturization and weight reduction of the robot was limited. In this paper, the field robot carried only communication equipments such as GPS module, normal RC receiver, and bluetooth modem. This enabled the field robot to be built in an economic cost and miniature size.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.4
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• pp.19-25
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• 2008

This paper proposes a hierarchically structured navigation algorithm for autonomous mobile robot under unknown environment based on fuzzy-neal network. The proposed algorithm consists of two basic layers as follows. The lower layer consists of two parts such as fuzzy algorithm for goal approach and fuzzy-neural algorithm for obstacle avoidance. The upper layer which is basically fuzzy algorithm adjusts the magnitude of the weighting factor depending on the environmental situation. The proposed algorithm provides an efficient method to escape local mimimum points as shown in the simulation result. Most simulation results show that this algorithm is very effective for autonomous mobile robots' traveling in unknown field.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2346-2349
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• 2005

Distributed Autonomous Robotic System (DARS) is defined as a system that independent autonomous robots in the restricted environments infer their status from pre-assigned conditions and operate their jobs through the cooperation with each other. In the DARS, a robot contains sensor part to percept the situation around themselves, communication part to exchange information, and actuator part to do a work. Especially, in order to cooperate with other robots, communicating with other robots is one of the essential elements. Because Bluetooth has many advantages such as low power consumption, small size module package, and various standard protocols, Bluetooth is rated as one of the efficient communicating technologies which can apply to small-sized robot system. In this paper, we will develop Bluetooth communicating system for autonomous robots such as DARS robots. For this purpose, The Bluetooth communication system must have several features. The first, this system should be separated from other robot parts and operate spontaneously and independently. In other words, this communication system should have the ability to organize and maintain and reorganize a network scheme. The next, this system had better support any kinds of standard interfaces in order to guarantee flexible applicability to other embedded system. We will discuss how to construct and what kind of procedure to develop the network system.

• The Journal of Korea Robotics Society
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• v.9 no.1
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• pp.1-10
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• 2014

In recent years, the research of 3D mapping technique in urban environments obtained by mobile robots equipped with multiple sensors for recognizing the robot's surroundings is being studied actively. However, the map generated by simple integration of multiple sensors data only gives spatial information to robots. To get a semantic knowledge to help an autonomous mobile robot from the map, the robot has to convert low-level map representations to higher-level ones containing semantic knowledge of a scene. Given a 3D point cloud of an urban scene, this research proposes a method to recognize the objects effectively using 3D graph model for autonomous mobile robots. The proposed method is decomposed into three steps: sequential range data acquisition, normal vector estimation and incremental graph-based segmentation. This method guarantees the both real-time performance and accuracy of recognizing the objects in real urban environments. Also, it can provide plentiful data for classifying the objects. To evaluate a performance of proposed method, computation time and recognition rate of objects are analyzed. Experimental results show that the proposed method has efficiently in understanding the semantic knowledge of an urban environment.

• The Journal of the Korea Contents Association
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• v.19 no.12
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• pp.164-172
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• 2019

In this paper, we implement a wheeled mobile robot that accurately and autonomously finds the optimal route from the starting point to the destination point based on computer vision in a complex indoor environment. We get a number of waypoints from the starting point to get the best route to the target through deep reinforcement learning. However, in the case of autonomous driving, the majority of cases do not reach their destination accurately due to external factors such as surface curvature and foreign objects. Therefore, we propose an algorithm to deepen the waypoints and destinations included in the planned route and then correct the route through the waypoint recognition while driving to reach the planned destination. We built an autonomous wheeled mobile robot controlled by Arduino and equipped with Raspberry Pi and Pycamera and tested the planned route in the indoor environment using the proposed algorithm through real-time linkage with the server in the OSX environment.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.2
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• pp.57-65
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• 2008

Recently many researches on intelligent robots have been studied. An intelligent robot is capable of recognizing environments or objects to autonomously perform specific tasks using sensor readings. One of fundamental problems in vision-based robot applications is to recognize where it is and to decide safe path to perform autonomous navigation. However, previous approaches only consider well-organized environments that there is no moving object and environment changes. In this paper, we introduce a novel navigation strategy to handle occlusions caused by moving objects using various computer vision techniques. Experimental results demonstrate the capability to overcome such difficulties for autonomous navigation.