• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1844-1845
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• 2011

In this paper, we compared several localization methods for indoor mobile robot navigation using a global ultrasonic sensor network. To estimate the pose of mobile robot in the sensor network, the range or range difference information with or without robot kinematics is used. Simulation results showed that the localization methods with robot kinematics have better performances.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.387-392
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• 1992

This paper presents a local trajectory generation method which is based on a sequence of reference posture-velocities and the efficient low level control algorithm which constructs the complete smooth curve from the trajectory specification. The reference trajectory generator(RTG) which is in between the local path planner(LPP) and the robot motion controller(RMC) generates a sequence of set-points for each path segments from the LPP and pass it to the RMC. The RMC controls the motions of vehicle which should follow the sequence. In the feedback controller of VMC, the method which compensates robot posture-velocity error correctly is used. These methods are implemented on indoor autonomous vehicle, 'ALIVE' mobile robot. The ALIVE mobile robot system is implemented on the 32bit VME bus system: the two VME CPU's are used for RTG and RMC, while the 80C196KC-based VME board is used for motor controller.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.11
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• pp.1044-1049
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• 2015

This paper proposes an autonomous navigation system of mobile robots for indoor corridor environment. The system uses a laser scanner but does not use reflectors. The laser scanner measures the distance between robot and structures such as wall, pillar, and fixtures. Adaptive breakpoint detector and modified IEPF (iterative endpoint fit) are developed to find mark points from the distance data. The robot path for corridor is then generated using the angle histogram of the mark points. The experimental results are finally presented to show the effectiveness of the proposed method.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2581-2584
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• 2003

In this paper, we propose a self-localization algorithm using vertical line segments. Indoor environment is consist of horizontal and vertical line features such as doors, furniture, and so on. From the input image, vertical line edges are detected by an edge operator, Then, line segments are obtained by projecting edge image vertically and detecting local maximum from the projected histogram. From the relation of horizontal position of line segments and the location of the robot, nonlinear equations are come out Localization is done by solving the equations by using Newton's method. Experimental results show that the proposed algorithm using one camera is simple and applicable to indoor environment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6A
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• pp.481-487
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• 2005

In order to increase the autonomous navigation capability of a mobile robot, it is very crucial to develop a method for recognizing a priori known environmental characteristics. This paper proposes an ultrasonic sensor based real-time method for recognizing a priori known indoor environmental characteristics like a wall and corner. The ultrasonic sensor consists of an ultrasonic transmitter and two ultrasonic receivers placed symmetrically about the transmitter. Unlike previous methods the information obtained from the sensor is processed in real-time by extended Kalman filter to be able to correct the position and orientation of robot with respect to known environmental characteristics.

• Journal of the Korea Convergence Society
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• v.10 no.12
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• pp.251-255
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• 2019

The QR codes are printed in sticker form and have many advantages in terms of location recognition accuracy or installation cost compared to the location recognition method, which attaches artificial indicators to ceilings or walls for low-cost location recognition, and the way in which the location is recognized by vision, to create robots that are generally applicable to all industries. In this study, it is shown that the two-dimensional square bar code applied to the robot within 3 mm of error allows the robot to be made with high accuracy and accurate location control. In particular, the fusion research, combined with various engineering technologies, describes QR code-aware indoor mobile robot control research centered on the construction of the system.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1738-1741
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• 1997

This paper addresses the issue fo target classification and localization with a SONAR for mobiler robot indoor navigation. In particular, multiple refetions of SONAR sound are used actively and interntionally. As for the SONAR sensor, the multiple reflection has been generally considered as one of the noisy phenomena, which is inevitable in the indoor environments. However, these multiple reflections can be a clue for classifying and localizing targets in the indoor environment if those can be controlled and used well. This paper develops a new SONAR sensor module with a reflection plane which can actively create the multiple refection. This paper also intends to suggest a new target classification emthod which uses the multiple refectiions. We approximate the world as being two dimensional and assume that the targets consisting of the indoor environment are pland, corner, and edge. Multiple reflection paths of an acoustic bean by a SONAR are analyzed, by simulations and the patterns of the TOPs (Time Of Flight) and angles of multiple reflections from each target are also analyzed. In addition, a new algorithm for target classification and localization is proposed.

• The Journal of Korea Robotics Society
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• v.14 no.4
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• pp.293-300
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• 2019

In this paper, an Embedded solution for fast navigation and precise positioning of mobile robots by floor features is introduced. Most of navigation systems tend to require high-performance computing unit and high quality sensor data. They can produce high accuracy navigation systems but have limited application due to their high cost. The introduced navigation system is designed to be a low cost solution for a wide range of applications such as toys, mobile service robots and education. The key design idea of the system is a simple localization approach using line features of the floor and delayed localization strategy using topological map. It differs from typical navigation approaches which usually use Simultaneous Localization and Mapping (SLAM) technique with high latency localization. This navigation system is implemented on single board Raspberry Pi B+ computer which has 1.4 GHz processor and Redone mobile robot which has maximum speed of 1.1 m/s.

• The Journal of Korea Robotics Society
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• v.15 no.3
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• pp.277-285
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• 2020

A differential drive wheeled robot is a kind of mobile robot suitable for indoor navigation. Model predictive control is an optimal control technique with various advantages and can achieve excellent performance. One of the main advantages of model predictive control is that it can easily handle constraints. Therefore, it deals with realistic constraints of the mobile robot and achieves admirable performance for trajectory tracking. In addition, the intention of the robot can be properly realized by adjusting the weight of the cost function component. This control technique is applied to the local planner of the navigation component so that the mobile robot can operate in real environment. Using the Robot Operating System (ROS), which has transcendent advantages in robot development, we have ensured that the algorithm works in the simulation and real experiment.

• The Journal of Korea Robotics Society
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• v.18 no.2
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• pp.135-142
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• 2023

Light detection and ranging (LiDAR) sensors have been most widely used in terrestrial robotic applications because they can provide dense and precise measurements of the surrounding environments. However, the reliability of LiDAR measurements can considerably vary due to the different reflectivities of laser beams to the reflecting surface materials. This study presents a robust LiDAR-based mapping method for the varying laser reflectivities in indoor environments using the framework of simultaneous localization and mapping (SLAM). The proposed method can minimize the performance degradations in the SLAM accuracy by checking and discarding potentially unreliable LiDAR measurements in the SLAM front-end process. The gaps in point-cloud maps created by the proposed approach are filled by a Gaussian process regression method. Experimental results with a mobile robot platform in an indoor environment are presented to validate the effectiveness of the proposed methodology.