• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.543-547
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• 2005

In this paper, we designed a intelligent autonomous driving robot by using Fuzzy algorithm. The object of designed robot is recognition of obstacle, avoidance of obstacle and safe arrival. We append a suspension system to auxiliary wheel for improvement in stability and movement. The designed robot can arrive at destination where is wanted to go by the old and the weak and the handicapped at indoor hospital and building.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.1
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• pp.27-32
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• 2002

This paper describes a fuzzy steering controller for an outdoor autonomous mobile robot using MR(magneto-resistive) sensor. Using the magnetic field difference values(dBy, dBz) obtained from the MR sensor, we designed fuzzy logic controller for driving the robot on the road center and proposed a method to eliminate the Earth magnetic field. To develop an autonomous mobile robot simulation program, we have done modeling MR sensor, mobile robot and coordinate transformation. A computer simulation of the robot including mobile robot dynamics and steering was used to verify the driving performance of the mobile robot controller using the fuzzy logic. So, we confirmed the robustness of the proposed fuzzy controller by computer simulation.

• The Journal of Korea Robotics Society
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• v.15 no.3
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• pp.233-239
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• 2020

The aims of this paper is to develop a modular agricultural robot and its autonomous driving algorithm that can be used in field farming. Actually, it is difficult to develop a controller for autonomous agricultural robot that transforming their dynamic characteristics by installation of machine modules. So we develop for the model based control algorithm of rotary machine connected to agricultural robot. Autonomous control algorithm of agricultural robot consists of the path control, velocity control, orientation control. To verify the developed algorithm, we used to analytical techniques that have the advantage of reducing development time and risks. The model is formulated based on the multibody dynamics methods for high accuracy. Their model parameters get from the design parameter and real constructed data. Then we developed the co-simulation that is combined between the multibody dynamics model and control model using the ADAMS and Matlab simulink programs. Using the developed model, we carried out various dynamics simulation in the several rotation speed of blades.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1113-1114
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• 1996

We describe an algorithm to control an autonomous guided vehicle with 2-diagonal driving wheels, which navigates in the clean room.

• Transactions of the Korean Society of Automotive Engineers
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• v.17 no.4
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• pp.86-92
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• 2009

A driver-vehicle model means the integrated dynamic model that is able to estimate the steering wheel angle from the driver's desired path based on the dynamic characteristics of the driver and vehicle. Autonomous driving robot for factory automation has individual four-wheels which are driven by electronic motors. In this paper, the dynamic characteristics of several four-wheel steering systems with the simultaneously steerable front and rear wheels are investigated and compared by means of the driver-vehicle model. A diver-vehicle model is proposed by using the PID control to velocity and trajectory of control autonomous driving robot. To determine the optimum speed of a autonomous driving robot, steady-state circle simulation is carried out with the ADAMS program and MATLAB control model.

• 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.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2200-2202
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• 1998

Autonomous mobile robot provide many functions such as sensing, processing, and driving. For more intelligent jobs, more intelligent functions are to be added and the existing functions may be updated. To execute a job autonomous mobile robot has a information of surrounding environment. So, robot uses sonar sensor, vision sensor and so on. Obtained sensor information is used map generation. This paper is focused on map generation using stereo vision system.

• Journal of Auto-vehicle Safety Association
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• v.14 no.1
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• pp.20-25
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• 2022

This paper presents a development of simulation environment for validation of autonomous driving (AD) algorithm based on Robot Operating System (ROS). ROS is one of the commonly-used frameworks utilized to control autonomous vehicles. For the evaluation of AD algorithm, a 3D autonomous driving simulator has been developed based on LGSVL. Two additional sensors are implemented in the simulation vehicle. First, Lidar sensor is mounted on the ego vehicle for real-time driving environment perception. Second, GPS sensor is equipped to estimate ego vehicle's position. With the vehicle sensor configuration in the simulation, the AD algorithm can predict the local environment and determine control commands with motion planning. The simulation environment has been evaluated with lane changing and keeping scenarios. The simulation results show that the proposed 3D simulator can successfully imitate the operation of a real-world vehicle.

• The Journal of Korea Robotics Society
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• v.16 no.4
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• pp.327-335
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• 2021

In this paper, we propose an embedded robot system based on "ROS2 on Yocto" that can support various robots. We developed a lightweight OS based on the Yocto Project as a next-generation robot platform targeting cloud robotics. Yocto Project was adopted for portability and scalability in both software and hardware, and ROS2 was adopted and optimized considering a low specification embedded hardware system. We developed SLAM, navigation, path planning, and motion for the proposed robot system validation. For verification of software packages, we applied it to home cleaning robot and indoor delivery robot that were already commercialized by LG Electronics and verified they can do autonomous driving, obstacle recognition, and avoidance driving. Memory usage and network I/O have been improved by applying the binary launch method based on shell and mmap application as opposed to the conventional Python method. Finally, we verified the possibility of mass production and commercialization of the proposed system through performance evaluation from CPU and memory perspective.