• Title/Summary/Keyword: autonomous robot localization

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Experiments of Urban Autonomous Navigation using Lane Tracking Control with Monocular Vision (도심 자율주행을 위한 비전기반 차선 추종주행 실험)

  • Suh, Seung-Beum;Kang, Yeon-Sik;Roh, Chi-Won;Kang, Sung-Chul
    • Journal of Institute of Control, Robotics and Systems
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    • v.15 no.5
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    • pp.480-487
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    • 2009
  • Autonomous Lane detection with vision is a difficult problem because of various road conditions, such as shadowy road surface, various light conditions, and the signs on the road. In this paper we propose a robust lane detection algorithm to overcome shadowy road problem using a statistical method. The algorithm is applied to the vision-based mobile robot system and the robot followed the lane with the lane following controller. In parallel with the lane following controller, the global position of the robot is estimated by the developed localization method to specify the locations where the lane is discontinued. The results of experiments, done in the region where the GPS measurement is unreliable, show good performance to detect and to follow the lane in complex conditions with shades, water marks, and so on.

Implementation of Wheelchair Robot Applying SLAM and Global Path Planning Methods Suitable for Indoor Autonomous Driving (실내 자율주행에 적합한 SLAM과 전역경로생성 방법을 적용한 휠체어로봇 구현)

  • Baek, Su-Jin;Kim, A-Hyeon;Kim, Jong-Wook
    • IEMEK Journal of Embedded Systems and Applications
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    • v.16 no.6
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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.

Development of Precise Localization System for Autonomous Mobile Robots using Multiple Ultrasonic Transmitters and Receivers in Indoor Environments (다수의 초음파 송수신기를 이용한 이동 로봇의 정밀 실내 위치인식 시스템의 개발)

  • Kim, Yong-Hwi;Song, Ui-Kyu;Kim, Byung-Kook
    • Journal of Institute of Control, Robotics and Systems
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    • v.17 no.4
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    • pp.353-361
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    • 2011
  • A precise embedded ultrasonic localization system is developed for autonomous mobile robots in indoor environments, which is essential for autonomous navigation of mobile robots with various tasks. Although ultrasonic sensors are more cost-effective than other sensors such as LRF (Laser Range Finder) and vision, they suffer inaccuracy and directional ambiguity. First, we apply the matched filter to measure the distance precisely. For resolving the computational complexity of the matched filter for embedded systems, we propose a new matched filter algorithm with fast computation in three points of view. Second, we propose an accurate ultrasonic localization system which consists of three ultrasonic receivers on the mobile robot and two or more transmitters on the ceiling. Last, we add an extended Kalman filter to estimate position and orientation. Various simulations and experimental results show the effectiveness of the proposed system.

A Study on the Implementation of RFID-based Autonomous Navigation System for Robotic Cellular Phone(RCP)

  • Choe, Jae-Il;Choi, Jung-Wook;Oh, Dong-Ik;Kim, Seung-Woo
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.457-462
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    • 2005
  • Industrial and economical importance of CP(Cellular Phone) is growing rapidly. Combined with IT technology, CP is currently one of the most attractive technologies for all. However, unless we find a breakthrough to the technology, its growth may slow down soon. RT(Robot Technology) is considered one of the most promising next generation technology. Unlike the industrial robot of the past, today's robots require advanced technologies, such as soft computing, human-friendly interface, interaction technique, speech recognition, object recognition, and many others. In this study, we present a new technological concept named RCP(Robotic Cellular Phone), which combines RT & CP, in the vision of opening a new direction to the advance of CP, IT, and RT all together. RCP consists of 3 sub-modules. They are $RCP^{Mobility}$, $RCP^{Interaction}$, and $RCP^{Interaction}$. $RCP^{Mobility}$ is the main focus of this paper. It is an autonomous navigation system that combines RT mobility with CP. Through $RCP^{Mobility}$, we should be able to provide CP with robotic functionalities such as auto-charging and real-world robotic entertainments. Eventually, CP may become a robotic pet to the human being. $RCP^{Mobility}$ consists of various controllers. Two of the main controllers are trajectory controller and self-localization controller. While Trajectory Controller is responsible for the wheel-based navigation of RCP, Self-Localization Controller provides localization information of the moving RCP. With the coordinate information acquired from RFID-based self-localization controller, Trajectory Controller refines RCP's movement to achieve better RCP navigations. In this paper, a prototype system we developed for $RCP^{Mobility}$ is presented. We describe overall structure of the system and provide experimental results of the RCP navigation.

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Localization of Outdoor Wheeled Mobile Robots using Indirect Kalman Filter Based Sensor fusion (간접 칼만 필터 기반의 센서융합을 이용한 실외 주행 이동로봇의 위치 추정)

  • Kwon, Ji-Wook;Park, Mun-Soo;Kim, Tae-Un;Chwa, Dong-Kyoung;Hong, Suk-Kyo
    • Journal of Institute of Control, Robotics and Systems
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    • v.14 no.8
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    • pp.800-808
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    • 2008
  • This paper presents a localization algorithm of the outdoor wheeled mobile robot using the sensor fusion method based on indirect Kalman filter(IKF). The wheeled mobile robot considered with in this paper is approximated to the two wheeled mobile robot. The mobile robot has the IMU and encoder sensor for inertia positioning system and GPS. Because the IMU and encoder sensor have bias errors, divergence of the estimated position from the measured data can occur when the mobile robot moves for a long time. Because of many natural and artificial conditions (i.e. atmosphere or GPS body itself), GPS has the maximum error about $10{\sim}20m$ when the mobile robot moves for a short time. Thus, the fusion algorithm of IMU, encoder sensor and GPS is needed. For the sensor fusion algorithm, we use IKF that estimates the errors of the position of the mobile robot. IKF proposed in this paper can be used other autonomous agents (i.e. UAV, UGV) because IKF in this paper use the position errors of the mobile robot. We can show the stability of the proposed sensor fusion method, using the fact that the covariance of error state of the IKF is bounded. To evaluate the performance of proposed algorithm, simulation and experimental results of IKF for the position(x-axis position, y-axis position, and yaw angle) of the outdoor wheeled mobile robot are presented.

Implementation of Camera-Based Autonomous Driving Vehicle for Indoor Delivery using SLAM (SLAM을 이용한 카메라 기반의 실내 배송용 자율주행 차량 구현)

  • Kim, Yu-Jung;Kang, Jun-Woo;Yoon, Jung-Bin;Lee, Yu-Bin;Baek, Soo-Whang
    • The Journal of the Korea institute of electronic communication sciences
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    • v.17 no.4
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    • pp.687-694
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    • 2022
  • In this paper, we proposed an autonomous vehicle platform that delivers goods to a designated destination based on the SLAM (Simultaneous Localization and Mapping) map generated indoors by applying the Visual SLAM technology. To generate a SLAM map indoors, a depth camera for SLAM map generation was installed on the top of a small autonomous vehicle platform, and a tracking camera was installed for accurate location estimation in the SLAM map. In addition, a convolutional neural network (CNN) was used to recognize the label of the destination, and the driving algorithm was applied to accurately arrive at the destination. A prototype of an indoor delivery autonomous vehicle was manufactured, and the accuracy of the SLAM map was verified and a destination label recognition experiment was performed through CNN. As a result, the suitability of the autonomous driving vehicle implemented by increasing the label recognition success rate for indoor delivery purposes was verified.

Mapless Navigation Based on DQN Considering Moving Obstacles, and Training Time Reduction Algorithm (이동 장애물을 고려한 DQN 기반의 Mapless Navigation 및 학습 시간 단축 알고리즘)

  • Yoon, Beomjin;Yoo, Seungryeol
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.25 no.3
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    • pp.377-383
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    • 2021
  • Recently, in accordance with the 4th industrial revolution, The use of autonomous mobile robots for flexible logistics transfer is increasing in factories, the warehouses and the service areas, etc. In large factories, many manual work is required to use Simultaneous Localization and Mapping(SLAM), so the need for the improved mobile robot autonomous driving is emerging. Accordingly, in this paper, an algorithm for mapless navigation that travels in an optimal path avoiding fixed or moving obstacles is proposed. For mapless navigation, the robot is trained to avoid fixed or moving obstacles through Deep Q Network (DQN) and accuracy 90% and 93% are obtained for two types of obstacle avoidance, respectively. In addition, DQN requires a lot of learning time to meet the required performance before use. To shorten this, the target size change algorithm is proposed and confirmed the reduced learning time and performance of obstacle avoidance through simulation.

Relative Localization for Mobile Robot using 3D Reconstruction of Scale-Invariant Features (스케일불변 특징의 삼차원 재구성을 통한 이동 로봇의 상대위치추정)

  • Kil, Se-Kee;Lee, Jong-Shill;Ryu, Je-Goon;Lee, Eung-Hyuk;Hong, Seung-Hong;Shen, Dong-Fan
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.55 no.4
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    • pp.173-180
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    • 2006
  • A key component of autonomous navigation of intelligent home robot is localization and map building with recognized features from the environment. To validate this, accurate measurement of relative location between robot and features is essential. In this paper, we proposed relative localization algorithm based on 3D reconstruction of scale invariant features of two images which are captured from two parallel cameras. We captured two images from parallel cameras which are attached in front of robot and detect scale invariant features in each image using SIFT(scale invariant feature transform). Then, we performed matching for the two image's feature points and got the relative location using 3D reconstruction for the matched points. Stereo camera needs high precision of two camera's extrinsic and matching pixels in two camera image. Because we used two cameras which are different from stereo camera and scale invariant feature point and it's easy to setup the extrinsic parameter. Furthermore, 3D reconstruction does not need any other sensor. And the results can be simultaneously used by obstacle avoidance, map building and localization. We set 20cm the distance between two camera and capture the 3frames per second. The experimental results show :t6cm maximum error in the range of less than 2m and ${\pm}15cm$ maximum error in the range of between 2m and 4m.

Global Ultrasonic System for Autonomous Navigation of Indoor Mobile Robots

  • Park, Seong-Hoon;Yi, Soo-Yeong;Jin, Sang-Yoon;Kim, Jin-Won
    • 제어로봇시스템학회:학술대회논문집
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    • 2004.08a
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    • pp.846-851
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    • 2004
  • In this paper, we propose a global ultrasonic system for the self-localization and autonomous navigation of indoor mobile robots. The ultrasonic sensor is regarded as the most cost-effective ranging system among the possible alternatives, and it is widely used for general purpose, since it requires simple electronic drivers and has relatively high accuracy. The global ultrasonic system presented in this paper consists of four or more ultrasonic generators fixed at reference positions in the global coordinates of an indoor environment and two receivers mounted on the mobile robots. By using the RF (Radio Frequency) modules added to the ultrasonic sensors, the robot is able to control the ultrasonic generation and to obtain the critical distances from the reference positions, which are required in order to localize is position in the global coordinates. A kalman filter algorithm designed for the self-localization using the global ultrasonic system and the experimental results of the autonomous navigation are presented in this paper.

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Mobile Robots for the Concrete Crack Search and Sealing (콘크리트 크랙 탐색 및 실링을 위한 다수의 자율주행로봇)

  • Jin, Sung-Hun;Cho, Cheol-Joo;Lim, Kye-Young
    • 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.