• The Journal of Korea Robotics Society
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• v.12 no.1
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• pp.86-93
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• 2017

The Expanded Guide Circle (EGC) method has been originally proposed as the guidance navigation method for improving the efficiency of the remote operation using the sensory information. The previous algorithm is, however, concerned only for the omni-directional mobile robot, so it needs to suggest a suitable one for a mobile robot with non-holonomic constraints. The ego-kinematic transform is a method to map points of $R^2$ into the ego-kinematic space which implicitly represents non-holonomic constraints for admissible paths. Thus, robots with non-holonomic constraints in the ego-kinematic space can be considered as "free-flying object". In this paper, we propose an effective obstacle avoidance method for mobile robots with non-holonomic constraints by applying EGC method in the ego-kinematic space using the ego-kinematic transformation. This proposed method shows that it works better for non-holonomic mobile robots such as differential-drive robot than the original one. The simulation results show its effectiveness of performance.

• ETRI Journal
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• v.43 no.4
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• pp.617-629
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• 2021

Although the actual visual simultaneous localization and mapping (SLAM) algorithms provide highly accurate tracking and mapping, most algorithms are too heavy to run live on embedded devices. In addition, the maps they produce are often unsuitable for path planning. To mitigate these issues, we propose a completely closed-loop online dense RGB-D SLAM algorithm targeting autonomous indoor mobile robot navigation tasks. The proposed algorithm runs live on an NVIDIA Jetson board embedded on a two-wheel differential-drive robot. It exhibits lightweight three-dimensional mapping, room-scale consistency, accurate pose tracking, and robustness to moving objects. Further, we introduce a navigation strategy based on the proposed algorithm. Experimental results demonstrate the robustness of the proposed SLAM algorithm, its computational efficiency, and its benefits for on-the-fly navigation while mapping.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.10
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• pp.861-866
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• 2013

This paper presents the development of a racing game by controlling multiple smartphones based on ad hoc communication. The proposed racing game by the smartphone-based control interface does not require any specific game console or remote controller. Thus, any multiple users who have smartphones are able to play the game in a monitor at the same time. In addition, the developed game is applicable to actual mobile robots in cases where the positions of all robots are measured, since its game unit is a mobile robot model with a differential drive. An experimental result shows that the racing game in a PC can be realized by the proposed communication interface through four iPhones based on acceleration sensors.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.7
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• pp.827-835
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• 1999

In this paper, a dead reckoning navigation system for differential drive mobile robots is presented. The navigation system consists of two incremental encoders and a gyroscope. We have built a third order polynomial function for compensating the nonlinear scale factor errors of the gyroscope. We utilize an indirect Kalman filter that feeds back estimated errors to the main navigation system. Also, the observability of the filter is analyzed in order to systematically evaluate the filter's performance. Experimental results show that the proposed navigation system provides a reliable position and heading angle by mutually compensating the encoder and the gyroscope errors. The proposed filter also reduces the computational burden and enhances the navigation system's reliability. The observability analysis confirms the characteristics of inevitably unbounded position error growth in dead reckoning navigation systems.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.421-432
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• 2013

This paper describes implementation of functions for mobile robot localization, which is one of the vital technologies for autonomous navigation of a mobile robot. There are several function libraries for mobile robot navigation. Some of them have limited applicability for practical use since they can be used only for simulation. Our research focuses on development of functions which can be used for localization of indoor robots. The functions implement deadreckoning and motion model of mobile robots, measurement model of range sensors, and frequently used calculations on angular directions. The functions encompass various types of robots and sensors. Also, various types of uncertainties in robot motion and sensor measurements are implemented so that the user can select proper ones for their use. The functions are tested and verified through simulation and experiments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.532-535
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• 2022

Path following algorithms have been intensively studied for various mobile platforms such as planetary exploration, unmanned delivery, and autonomous driving. However, ensuring high accuracy in practical applications is challenging due to enormous uncertainty inherent in real environment. In this paper, we aim to reveal the guideline for the design and implementation by investigating the path following performance of mobile robot controlled by the pure pursuit algorithm. To this end, we evaluate the accuracy of the pure pursuit algorithm when tuning the look ahead distance and deploying erroneous actuator.

• The Journal of Korea Robotics Society
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• v.17 no.1
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• pp.32-39
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• 2022

Since the mobile platform and the manipulator mounted on it move at the same time in a mobile manipulator, the risk of mutual collision increases. Most of the studies on collision avoidance of mobile manipulators cannot be applied to differential drive type mobile platforms or the end-effector tends to deviate from the desired trajectory for collision avoidance. In this study, a collision avoidance algorithm based on null space projection (CANS) that solves these two problems is proposed. To this end, a modified repulsive force that overcomes the non-holonomic constraints of a mobile platform is generated by adding a virtual repulsive force in the direction of its instantaneous velocity. And by converting this repulsive force into a repulsive velocity and applying it to the null space, the end-effector of the robot avoids a collision while moving along its original trajectory. The proposed CANS algorithm showed excellent performance through self-collision avoidance tests and door opening tests.

• The Journal of Korea Robotics Society
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• v.5 no.3
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• pp.186-196
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• 2010

In this paper, we present a global localization and position error compensation method in a known indoor environment using magnet hall sensors. In previous our researches, it was possible to compensate the pose errors of $x_e$, $y_e$, ${\theta}_e$ correctly on the surface of indoor environment with magnets sets by regularly arrange the magnets sets of identical pattern. To improve the proposed method, new strategy that can realize the global localization by changing arrangement of magnet pole is presented in this paper. Total six patterns of the magnets set form the unique landmarks. Therefore, the virtual map can be built by using the six landmarks randomly. The robots search a pattern of magnets set by rotating, and obtain the current global pose information by comparing the measured neighboring patterns with the map information that is saved in advance. We provide experimental results to show the effectiveness of the proposed method for a differential drive wheeled mobile robot.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.361-368
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• 2015

This paper reports an unscented Kalman filter approach for localization of a mobile robot in an indoor environment. The method proposes a new model of measurement uncertainty which adjusts the error covariance according to the measured distance. The method also uses non-zero off diagonal values in error covariance matrices of motion uncertainty and measurement uncertainty. The method is tested through experiments in an indoor environment of 100*40 m working space using a differential drive robot which uses Laser range finder as an exteroceptive sensor. The results compare the localization performance of the proposed method with the conventional method which doesn't use adaptive measurement uncertainty model. Also, the experiment verifies the improvement due to non-zero off diagonal elements in covariance matrices. This paper contributes to implementing and evaluating a practical UKF approach for mobile robot localization.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.5
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• pp.457-462
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• 2005

An empirical way of a covariance matrix which expresses the odometry uncertainty of mobile robots is proposed. This method utilizes PC-method which removes systematic errors of odometry. Once the systematic errors are removed, the odometry error can be modeled using the Gaussian probability distribution, and the parameters of the distribution can be represented by the covariance matrix. Experimental results show that the method yields $5{\%}$ and $2.3{\%}$ offset for the synchro and differential drive robots.