• 대한임베디드공학회논문지
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• 제16권6호
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• pp.293-297
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• 2021

This paper presents how to create a 3D map and solve problems related to generating a global path planning for navigation. Map creation and localization were performed using the RTAB-Map package to create a 3D map of the environment. In addition, when the target point is within the obstacle space, the problem of not generating a global path was solved using the asr_navfn package. The performance of the proposed system is validated through experiments with a wheelchair-type robot.

• 제어로봇시스템학회논문지
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• 제22권3호
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• pp.210-216
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• 2016

This paper presents a novel collision avoidance technique for mobile robots based on omni-directional vision simultaneous localization and mapping (SLAM). This method estimates the avoidance path and speed of a robot from the location of an obstacle, which can be detected using the Lucas-Kanade Optical Flow in images obtained through fish-eye cameras mounted on the robots. The conventional methods suggest avoidance paths by constructing an arbitrary force field around the obstacle found in the complete map obtained through the SLAM. Robots can also avoid obstacles by using the speed command based on the robot modeling and curved movement path of the robot. The recent research has been improved by optimizing the algorithm for the actual robot. However, research related to a robot using omni-directional vision SLAM to acquire around information at once has been comparatively less studied. The robot with the proposed algorithm avoids obstacles according to the estimated avoidance path based on the map obtained through an omni-directional vision SLAM using a fisheye image, and returns to the original path. In particular, it avoids the obstacles with various speed and direction using acceleration components based on motion information obtained by analyzing around the obstacles. The experimental results confirm the reliability of an avoidance algorithm through comparison between position obtained by the proposed algorithm and the real position collected while avoiding the obstacles.

• 항공우주시스템공학회지
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• 제14권2호
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• pp.1-11
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• 2020

Detecting obstacles and generating a suitable path to avoid obstacles in real time is a prime mission requirement for UAVs. In areas, close to buildings and people, detecting obstacles in the path and estimating its own position (egomotion) in GPS degraded/denied environments are usually addressed with vision-based Simultaneous Localization and Mapping (SLAM) techniques. This presents possibilities and challenges for the feasible path generation with constraints of vehicle dynamics in the configuration space. In this paper, a near real-time feasible path is shown to be generated in the ORB-SLAM framework using a chain-based path planning approach in a force field with dynamic constraints on path length and minimum turn radius. The chain-based path plan approach generates a set of nodes which moves in a force field that permits modifications of path rapidly in real time as the reward function changes. This is different from the usual approach of generating potentials in the entire search space around UAV, instead a set of connected waypoints in a simulated chain. The popular ORB-SLAM, suited for real time approach is used for building the map of the environment and UAV position and the UAV path is then generated continuously in the shortest time to navigate to the goal position. The principal contribution are (a) Chain-based path planning approach with built in obstacle avoidance in conjunction with ORB-SLAM for the first time, (b) Generation of path with minimum overheads and (c) Implementation in near real time.

• 로봇학회논문지
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• 제17권2호
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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.

• 제어로봇시스템학회논문지
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• 제20권4호
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• pp.456-462
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• 2014

In this paper, we present a deployable bio-inspired robot called the Pillbot-light, which utilizes a safe autonomous navigation system. The Pillbot-light is mounted the station robot, and can be operated in a disaster relief operation or military operation. However, the Pilbot-light has a challenge to navigate autonomously because the Pilbot-light cannot be equipped with various sensors. As a result, we propose a new robot system for autonomous navigation that the station robot controls Pillbot-light equipped with vision camera and CPU of high performance. This system detects obstacles based on the edge extraction using vision camera. Also, it cannot only achieve path planning using the hazard cost function, but also localization using the Particle Filter. And this system is verified by simulation and experiment.

• 자동차안전학회지
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• 제9권2호
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• pp.6-12
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• 2017

An autonomous driving system based on a precise digital map is developed. The system is implemented to the Hyundai's Tucsan fuel cell car, which has a camera, smart cruise control (SCC) and Blind spot detection (BSD) radars, 4-Layer LiDARs, and a standard GPS module. The precise digital map has various information such as lanes, speed bumps, crosswalks and land marks, etc. They can be distinguished as lane-level. The system fuses sensed data around the vehicle for localization and estimates the vehicle's location in the precise map. Objects around the vehicle are detected by the sensor fusion system. Collision threat assessment is performed by detecting dangerous vehicles on the precise map. When an obstacle is on the driving path, the system estimates time to collision and slow down the speed. The vehicle has driven autonomously in the Hyundai-Kia Namyang Research Center.

• ETRI Journal
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• 제37권6호
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• pp.1220-1230
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• 2015

An autonomous valet parking (AVP) system is designed to locate a vacant parking space and park the vehicle in which it resides on behalf of the driver, once the driver has left the vehicle. In addition, the AVP is able to direct the vehicle to a location desired by the driver when requested. In this paper, for an AVP system, we introduce technology to recognize a parking space using image sensors. The proposed technology is mainly divided into three parts. First, spatial analysis is carried out using a height map that is based on dense motion stereo. Second, modelling of road markings is conducted using a probability map with a new salient-line feature extractor. Finally, parking space recognition is based on a Bayesian classifier. The experimental results show an execution time of up to 10 ms and a recognition rate of over 99%. Also, the performance and properties of the proposed technology were evaluated with a variety of data. Our algorithms, which are part of the proposed technology, are expected to apply to various research areas regarding autonomous vehicles, such as map generation, road marking recognition, localization, and environment recognition.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권2호
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• pp.85-92
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• 2003

In this paper, we propose a method to evaluate performances of mobile personal robots. A set of performance measures is proposed and the corresponding evaluation methods are developed. Different from industrial manipulators, personal robots need to be evaluated with its mobility, navigation, task and intelligent performance in environments where human beings exist. The proposed performance measures are composed of measures for mobility including vibration, repeatability, path accuracy and so on, as well as measures for navigation performance including wall following, overcoming doorsill, obstacle avoidance and localization. But task and intelligent behavior performances such as cleaning capability and high-level decision-making are not considered in this paper. To measure the proposed performances through a series of tests, we designed a test environment and developed measurement systems including a 3D Laser tracking system, a vision monitoring system and a vibration measurement system. We measured the proposed performances with a mobile robot to show the result as an example. The developed systems, which are installed at Korea Agency for Technology and Standards, are going to be used for many robot companies in Korea.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.94-96
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• 2021

본 연구는 산업현장에서 사용되는 이동로봇이 익숙하지 못한 환경에서 목적지에 도착할 수 있는 항법 알고리즘을 개발하고자 한다. 이를 위해 동적창 접근(DWA)과 Dijkstra 경로설정 알고리즘을 결합하여 항법 알고리즘을 제안한다. 이를 성능 비교하기 위해 로컬 동적창 접근(LDWA), 글로벌 동적창 접근(GDWA), 고속 탐색 랜덤 트리 (RRT) 알고리즘을 비교 분석한다. LDWA과 GDWA을 적용한 Dijkstra 알고리즘을 활용한 항법 알고리즘을 구현하여 제한된 정보를 이용하여 이동로봇이 목적지에 도달할 수 있도록 한다. 이 알고리즘들의 목적지에 도착할 때까지 걸리는 시간, 장애물 회피와 계산복잡도에 대한 비교 분석한다. 위 알고리즘의 한계를 극복하기 위한 새로운 항법 알고리즘을 제안하고 제시된 최적화된 항법 알고리즘의 산업현장에서의 활용 방안을 모색한다.

• 한국콘텐츠학회논문지
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• 제16권8호
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• pp.202-208
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• 2016

시각장애인의 활동범위가 늘어나면서 복잡하고 대형화된 건물들 속에서 목적지까지 안전하게 찾아가기란 쉽지 않다. 시각장애인을 위해 GPS 신호나 음성 알림 정보, 점자 유도 블록, 음향 신호기 등을 활용한 안내 시스템이 있지만 이는 대부분 실외 안내 시스템으로 실내에서는 적합하지 않다. 이에 본 논문에서는 보편화된 스마트 폰을 이용하여 시각장애인에게 해당 목적지에 대한 방향, 거리, 높이, 장애물 등의 목적지까지의 다양한 정보를 음성기술인 TTS(Text to Speech)와 촉각기술인 햅틱(Haptic) 그리고 블루투스 4.0기반의 근거리 무선통신 기술인 비콘을 이용하여 사용자에게 알려 줄 수 있는 실내 위치 추정 및 안내 시스템을 제안한다. 제한된 시스템의 실험 결과에서 사용자는 목적지까지의 최적 경로를 검색하여 TTS와 Haptic 기술을 이용해 안전하고 정확하게 안내받을 수 있었다.