• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.7
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• pp.1437-1443
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• 2009

This paper is presented an simultaneous localization and mapping (SLAM) algorithm using ultrasonic for robot and electric compass, encoder, and gyro. Generally, localization based upon electric compass, encoder, and gyro can be measured just local position in workspace. However, actual robot must need an information of the absolute position in workspace to perform its mission, Absolute position in workspace could be calculated using SLAM algorithm. To implement SLAM in this paper, a map is built using ultrasonic sensor and hierarchical map building method. And then, we the map will be transformed into a feature map. The absolute position could be calculated using the feature map and map mapping method. As a test bed, we designed and construct an autonomous robot and showed the experimental performance of the proposed SLAM algorithm based on feature map. Experimental result, we verified that robot can found all absolute position on experiments using proposed SLAM algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.10a
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• pp.134-134
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• 2003

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.73-74
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• 2010

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.149-156
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• 2008

SLAM(Simultaneous Localization and Mapping) system is to find a global position and build a map with sensing data when an unmanned-robot navigates an unknown environment. Two kinds of system were developed. One is used distance measurement sensors such as an ultra sonic and a laser sensor. The other is used stereo vision system. The distance measurement SLAM with sensors has low computing time and low cost, but precision of system can be somewhat worse by measurement error or non-linearity of the sensor In contrast, stereo vision system can accurately measure the 3D space area, but it needs high-end system for complex calculation and it is an expensive tool. In this paper, we implement the SLAM system using a single camera image and a PSD sensors. It detects obstacles from the front PSD sensor and then perceive size and feature of the obstacles by image processing. The probability SLAM was implemented using the data of sensor and image and we verify the performance of the system by real experiment.

• 대한공업교육학회지
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• v.33 no.2
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• pp.325-339
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• 2008

In this paper, we developed a module that can execute the data acquisition of the real-time measured radiant rays in the specific part of the nuclear power station. This module that includes the RF communication function, paces around the power station, being loaded on robot and can obtain the generated radiant rays in the various places through the detecting devices. It is considered that this new developed radiant rays acquisition method will have the higher degree of efficiency as compared with the existing method and reduce the expenses of the maintenance and repair work.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.49-54
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• 2011

In this paper, we represent a mobile robot which can recognize chemical odor, measure concentration, and track its source indoors. The mobile robot has the function of smell that can sort several gases in experiment such as ammonia, ethanol, and their mixture with neural network algorithm and measure each gas concentration with fuzzy rules. In addition, it can not only navigate to the desired position with vision system by avoiding obstacles but also transmit odor information and warning messages earned from its own operations to other nodes by multi-hop communication in wireless sensor network. We suggest the way of odor sorting, concentration measurement, and source tracking for a mobile robot in wireless sensor network using a hybrid algorithm with vision system and gas sensors. The experimental studies prove that the efficiency of the proposed algorithm for odor recognition, concentration measurement, and source tracking.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.12
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• pp.2146-2155
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• 1997

This paper presents the Wire Parallel Mechanism for robot pose measurement which can be used to robot calibration. It is constructed with six parallel links using wire. The position and orientation of the end effector of a robot are calculated from the wire length that measured by the encoder. The unique solution is obtained from a Newton-Raphson method and geometric configuration of the mechanism, also the method to estimate a measuring space is presented. Through the simulations, it is verified that the proposed mechanism can measure a robot pose, and has a large measuring space. In conclusion, it can be used effectively in a robot pose measurement with little cost and effort.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.70-77
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• 2015

This paper proposes an application of unscented Kalman filter(UKF) for localization of an underwater robot. The method compares the bathymetric measurement from the robot with the seabed terrain information. For the measurement of bathymetric range to seabed, it uses a DVL which typically yields four range data together with velocity of the robot. Usual extended Kalman filter is not appropriated for application in case of terrain navigation, since it is not feasible to derive Jacobian for the bathymetric range measurement. Though particle filter(PF) is a nice solution which doesn't require Jacobian and can deal with non-linear and non-Gaussian system and measurement, it suffers from heavy computational burden. The paper compares the localization performance and the computation time of the UKF approach and PF approach. Though there have been some UKF methods which are used for underwater navigation, application of the UKF for bathymetric localization is rare. Especially, the proposed method uses only four range data whereas many of the bathymetric navigation methods have used multibeam sonar which yields hundreds of scanned range data. The result shows feasibility of the UKF approach for terrain-based navigation using small numbers of range data.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.242-250
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• 2019

In this paper, we propose a method to predict the failure of industrial robot using Seq2Seq (Sequence to Sequence) model, which is a model for transforming time series data among Artificial Neural Network models. The proposed method uses the data of the joint current and angular value, which can be measured by the robot itself, without additional sensor for fault diagnosis. After preprocessing the measured data for the model to learn, the Seq2Seq model was trained to convert the current to angle. Abnormal degree for fault diagnosis uses RMSE (Root Mean Squared Error) during unit time between predicted angle and actual angle. The performance evaluation of the proposed method was performed using the test data measured under different conditions of normal and defective condition of the robot. When the Abnormal degree exceed the threshold, it was classified as a fault, and the accuracy of the fault diagnosis was 96.67% from the experiment. The proposed method has the merit that it can perform fault prediction without additional sensor, and it has been confirmed from the experiment that high diagnostic performance and efficiency are available without requiring deep expert knowledge of the robot.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.3
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• pp.1-13
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• 2024

This paper proposes a cost-effective system design and user-friendly approach for the key technological elements necessary to configure an autonomous mobile robot. To implement a high-precision positioning system using an autonomous mobile robot, we established a Linux-based VRS (virtual reference station)-RTK (real-time kinematic) GNSS (global navigation satellite system) system with NTRIP (Network Transport of RTCM via Internet Protocol) client functionality. Notably, we reduced the construction cost of the GNSS positioning system by performing dynamic location analysis of the established system, without utilizing an RTK replay system. Dynamic location analysis involves sampling each point during the trajectory following of the autonomous mobile robot and comparing the location precision with ground-truth points. The proposed system ensures high positioning performance with fast sampling times and suggests a GPS waypoint system for user convenience. The centimeter-level precision GNSS information is provided at a 30Hz sampling rate, and the dead reckoning function ensures valid information even when passing through tall buildings and dense forests. The horizontal position error measured through the proposed system is 6.7cm, demonstrating a highly precise dynamic location measurement error within 10cm. The VRS network-RTK Linux system, which provides precise dynamic location information at a high sampling rate, supports a GPS waypoint planner function for user convenience, enabling easy destination setting based on GPS information.