• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.123.5-123
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• 2001

For autonomous navigation of the mobile robots, the robots' capability to recognize 3D environment is necessary. In this paper, an on-line 3D map building method for autonomous mobile robots is proposed. To get range data on the environment, we use a sensor system which is composed of a structured light and a CCD camera based on optimal triangulation. The structured laser is projected as a horizontal strip on the scene. The sensor system can rotate$\pm$30$^{\circ}$ with a goniometer. Scanning the system, we get the laser strip image for the environments and update planes composing the environment by some image processing steps. From the laser strip on the captured image, we find a center point of each column, and make line segments through blobbing these center points. Then, the planes of the environments are updated. These steps are done on-line in scanning phase. With the proposed method, we can efficiently get a 3D map about the structured environment.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.11
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• pp.1102-1107
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• 2009

An ultrasonic sensor has been widely used as a range sensor for its low cost and capability of detecting some obstacles, such as glasses and black surfaces, which are not well detected by a laser scanner and an IR sensor. Although low-cost sensors are preferred for practical service robots, they suffer from the inaccurate and insufficient range information. This paper proposes a novel approach to obstacle avoidance using low-cost anisotropic ultrasonic sensors with wide beam angle. In this paper, obstacles can be detected by the proposed sensor configuration which consists of one transmitter and three receivers. Because even wide obstacles are represented by a point, which corresponds to the intersection of range data from each receiver of the anisotropic sensor, a robot cannot avoid wide obstacles successfully. This paper exploits the probabilistic mapping technique to avoid collision with various types of obstacles. The experimental results show that the proposed method can robustly avoid obstacles in most indoor environments.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.12
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• pp.1626-1632
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• 2018

In this paper, we describe the fabrication and characterization of a hydrogen peroxide ($H_2O_2$) sensor based on palladium and copper (PdCu) electroplated laser induced graphene (LIG) electrodes. $CO_2$ laser was used to form LIG electrodes on a PI film. This fabrication method allows simple control of the LIG electrode size and shape. The PdCu was electrochemically deposited on the LIG electrodes to improve the electrocatalytic reaction with $H_2O_2$. The electrochemical performance of this sensor was evaluated in terms of selectivity, sensitivity, and linearity. The physical characterization of this sensor was conducted using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS), which confirmed that PdCu was formed on the laser induced graphene electrode. In order to increase the sensor sensitivity, the Pd:Cu ratio of the electroplated PdCu was varied to five different values and the condition of highest amperometric current at an identical of $H_2O_2$ concentration was chosen among them. The resulting amperometric current was highest when the ratio of Pd:Cu was 7:3 and this Pd;Cu ratio was employed in the sensor fabrication. The fabricated PdCu/LIG electrode based $H_2O_2$ sensor exhibited a sensitivity of $139.4{\mu}A/mM{\cdot}cm^2$, a broad linear range between 0 mM and 16 mM of $H_2O_2$ concentrations at applied potential of -0.15 V, and high reproducibility (RSD = 2.6%). The selectivity of the fabricated sensors was also evaluated by applying ascorbic acid, glucose, and lactose separately onto the sensor in order to see if the sensor ourput is affected by one of them and the sensor output was not affected. In conclusion, the proposed PdCu/LIG electrode based $H_2O_2$ sensor seems to be suitable $H_2O_2$ sensor in various applications.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.8
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• pp.697-702
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• 2001

The ring laser gyro(RLG) has been used extensively in strapdown inertial navigation system(SDINS) because of the apparent of having wide dynamic range, digital output and high accuracy. The dithered RLG system has dynamic motion at sensor level, caused by the dithering motion to overcome the lock-in threshold. In this case, an attitude error is produced by not only the true coning of the vehicle motion but also the pseudo coning of the sensor motion. This paper describes the definition of the multi-frequency coning motion and its noncommutativity error to reject the pseudo coning error produced by the sensor motion such as the dithered RLG. The simulation results are presented to minimize the multi-frequency coning error.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.7
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• pp.635-641
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• 2008

In this paper, we used a laser range finder (LRF) to detect both the static and dynamic obstacles for the safe navigation of a mobile robot. LRF sensor measurements containing the information of obstacle's geometry are first processed to extract the characteristic points of the obstacle in the sensor field of view. Then the dynamic states of the characteristic points are approximated using kinematic model, which are tracked by associating the measurements with Probability Data Association Filter. Finally, the collision avoidance algorithm is developed by using fuzzy decision making algorithm depending on the states of the obstacles tracked by the proposed obstacle tracking algorithm. The performance of the proposed algorithm is evaluated through experiments with the experimental mobile robot.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.118.2-118
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• 2001

This research describes a development of laser distance sensor with precise resolution even in the case that the object surface has some curvature. There are typical two methods in measuring the distance by using laser light source, so called time of flight and optic-triangular methods. Both methods have an advantage and a disadvantage each other. In general, the time of flight method produces wide range of the measurement, but low accuracy. The other method is vice versa. In this research, the optic-triangular methods with one-dimensional CCD cell are proposed to obtain the precise distance measure from the sensor the surface of the curved object ...

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.8
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• pp.678-687
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• 2005

This paper describes a relative localization algorithm using odometry data and consecutive local maps. The purpose of this paper is the odometry error correction using the area matching of two consecutive local maps. The local map is built up using a sensor module with dual laser beams and USB camera. The range data form the sensor module is measured using the structured lighting technique (active stereo method). The advantage in using the sensor module is to be able to get a local map at once within the camera view angle. With this advantage, we propose the AVS (Aligned View Sector) matching algorithm for. correction of the pose error (translational and rotational error). In order to evaluate the proposed algorithm, experiments are performed in real environment.

• Korean Journal of Construction Engineering and Management
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• v.5 no.5 s.21
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• pp.151-162
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• 2004

Current methods for construction site modeling employ large, expensive laser range scanners that produce dense range point clouds of a scene from different perspectives. Days of skilled interpretation and of automatic segmentation may be required to convert the clouds to a finished CAD model. The dynamic nature of the construction environment requires that a real-time local area modeling system be capable of handling a rapidly changing and uncertain work environment. However, in practice, large, simple, and reasonably accurate embodying volumes are adequate feedback to an operator who, for instance, is attempting to place materials in the midst of obstacles with an occluded view. For real-time obstacle avoidance and automated equipment control functions, such volumes also facilitate computational tractability. In this research, a human operator's ability to quickly evaluate and associate objects in a scene is exploited. The operator directs a laser range finder mounted on a pan and tilt unit to collect range points on objects throughout the workspace. These groups of points form sparse range point clouds. These sparse clouds are then used to create geometric primitives for visualization and modeling purposes. Experimental results indicate that these models can be created rapidly and with sufficient accuracy for automated obstacle avoidance and equipment control functions.

• The Journal of Korea Robotics Society
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• v.9 no.2
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• pp.117-123
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• 2014

Localization is one of the essential tasks necessary to achieve autonomous navigation of a mobile robot. One such localization technique, Monte Carlo Localization (MCL) is often applied to a digital surface model. However, there are differences between range data from laser rangefinders and the data predicted using a map. In this study, commonly observed from air and ground (COAG) features and candidate selection based on the shape of sensor data are incorporated to improve localization accuracy. COAG features are used to classify points consistent with both the range sensor data and the predicted data, and the sample candidates are classified according to their shape constructed from sensor data. Comparisons of local tracking and global localization accuracy show the improved accuracy of the proposed method over conventional methods.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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• pp.617-624
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• 2010

This article describes the system architecture of KUVE (KIST Unmanned Vehicle Electric) and unmanned autonomous navigation of it in KIST. KUVE, which is an electric light-duty vehicle, is equipped with two laser range finders, a vision camera, a differential GPS system, an inertial measurement unit, odometers, and control computers for autonomous navigation. KUVE estimates and tracks the boundary of road such as curb and line using a laser range finder and a vision camera. It follows predetermined trajectory if there is no detectable boundary of road using the DGPS, IMU, and odometers. KUVE has over 80% of success rate of autonomous navigation in KIST.