• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.12
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• pp.577-582
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• 2002

The objective of this paper is to produce a weighted graph map for path-planning of an autonomous mobile robot(AMR) based on the measurements from a single ultrasonic sensor, which are acquired when the autonomous mobile robot explores unknown indoor circumstance. The AMR navigates in th unknown space by following the wall and gathers the range data using the ultrasonic sensor, from which the occupancy grid map is constructed by associating the range data with occupancy certainties. Then, the occupancy grid map is converted to a weighted graph map suing morphological image processing and thinning algorithms. the path- planning for autonomous navigation of a mobile robot can be carried out based on the occupancy grid map. These procedures are implemented and tested using an AMR, and primary results are presented in this paper.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.3
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• pp.39-45
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• 2009

In this paper we present a cleaning robot system for an autonomous mobile robot. Our robot performs goal reaching tasks into unknown indoor environments by using sensor fusion. The robot's operation objective is to clean floor or any other applicable surface and to build a map of the surrounding environment for some further purpose such as finding the shortest path available. Using its cleaning robot system for an autonomous mobile robot can move in various modes and perform dexterous tasks. Performance of the cleaning robot system is better than a fixed base redundant robot in avoiding singularity and obstacle. Sensor fusion using the clean robot improves the performance of the robot with redundant freedom in workspace and Map-Building. In this paper, Map-building of the cleaning robot has been studied using sensor fusion. A sequence of this alternating task execution scheme enables the clean robot to execute various tasks efficiently. The proposed algorithm is experimentally verified and discussed with a cleaning robot, KCCR.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.86-88
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• 2005

An autonomous semantic-map building method is proposed, with the robot localized in the semantic-map. Our semantic-map is organized by objects represented as SIFT features and vision-based relative localization is employed as a process model to implement extended Kalman filters. Thus, we expect that robust SLAM performance can be obtained even under poor conditions in which localization cannot be achieved by classical odometry-based SLAM

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.900-903
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• 1996

The objective of this paper is to make the weighted graph map for path planning using the ultrasonic sensor measurements that are acquired when an A.M.R (autonomous mobile robot) explores the unknown circumstance. First, The A.M.R navigates on unknown space with wall-following and gathers the sensor data from the environments. After this, we constructs the occupancy grid map by interpreting the gathered sensor data to occupancy probability. For the path planning of roadmap method, the weighted graph map is extracted from the occupancy grid map using morphological image processing and thinning algorithm. This methods is implemented on an A.M.R having a ultrasonic sensor.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.12
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• pp.1053-1060
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• 2016

As location recognition for autonomous vehicles develops, the need for a precise map for autonomous driving has increased. A precise map must be built based upon accurate position. Recent studies have accelerated research in this area by using various sensors that calculate the accurate position by comparing and recognizing objects around the roads. However, application of such methods is limited because these studies only take objects with significant verticality into consideration. Thus, new research is needed to overcome the limitations: a method that is not constrained by the existence of certain types of surrounding objects shall be proposed. Most roads contain road marking information, such as lanes, direction signs, and pedestrian crossings. Such information on the road surface is a valuable resource for building a precise map. This paper proposes a method of building a precise map by using road marking information.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1132-1135
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• 1996

The objective of this paper is to make a model-based map for the localization of an autonomous mobile robot(AMR) from ultrasonic sensor measurements, that are acquired when the AMR explores unknown indoors. First, the AMR navigates on unknown space by wall-following and gathers range data from the ultrasonic sensor. Then, the range data are converted to a wall-marked gird map, from which lines representing the walls are extracted using the Hough transform. This process is implemented on an AMR having an ultrasonic sensor, and a preliminary experimental result is presented.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.1
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• pp.148-156
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• 2011

This paper presents an implementation of autonomous navigation of a mobile robot indoors. It explains methods for map building, localization, obstacle avoidance and path planning. Geometric map is used for localization and path planning. The localization method calculates sensor data based on the map for comparison with the real sensor data. Monte Carlo Localization(MCL) method is adopted for estimation of the robot position. For obstacle avoidance, an artificial potential field generates repulsive and attractive force to the robot. Dijkstra algorithm plans the shortest distance path from a start position to a goal point. The methods integrate into autonomous navigation method and implemented for indoor navigation. The experiments show that the proposed method works well for safe autonomous navigation.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.6
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• pp.14-22
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• 2014

An autonomous vehicle requires a technology of generating maps by recognizing surrounding environment. The recognition of the vehicle's environment can be achieved by using distance information from a 2D laser scanner and color information from a camera. Such sensor information is used to generate 2D or 3D maps. A 2D map is used mostly for generating routs, because it contains information only about a section. In contrast, a 3D map involves height values also, and therefore can be used not only for generating routs but also for finding out vehicle accessible space. Nevertheless, an autonomous vehicle using 3D maps has difficulty in recognizing environment in real time. Accordingly, this paper proposes the technology for generating 2D maps that guarantee real-time recognition. The proposed technology uses only the color information obtained by removing height values from 3D maps generated based on the fusion of 2D laser scanner and camera data.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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• pp.451-459
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• 2011

Fundamentally, there are 5 systems are needed for autonomous navigation of unmanned ground vehicle: Localization, environment perception, path planning, motion planning and vehicle control. Path planning and motion planning are accomplished based on result of the environment perception process. Thus, high reliability of localization and the environment perception will be a criterion that makes a judgment overall autonomous navigation. In this paper, via map matching using vehicle dynamic model and LIDAR sensors, replace high price localization system to new one, and have researched an algorithm that lead to robust autonomous navigation. Finally, all results are verified via actual unmanned ground vehicle tests.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.2
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• pp.115-120
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• 2015

Recently, in automotive technology area, intelligent safety systems have been actively accomplished for drivers, passengers, and pedestrians. Also, many researches are focused on development of autonomous vehicles. This paper propose the application of LiDAR sensors, which takes major role in perceiving environment, terrain classification, obstacle data clustering method, and local map building for autonomous driving. Finally, based on these results, planning for lane change path that vehicle tracking possible were created and the reliability of path generation were experimented.