• Journal of Institute of Control, Robotics and Systems
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• v.17 no.6
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• pp.521-525
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• 2011

Lane marking detection is one of important issues in the field of autonomous mobile robot. Especially, in urban environment, like pavement roads of downtown or tour tracks of Science Park, which have continuous patterns on the surface of the road, the lane marking detection becomes more important ability. Although there were many researches about lane detection and lane tracing, many of them used vision sensors mainly to detect lane marking. In this paper, we obtain 2 dimensional library data of 'Intensity' and 'Distance' using one laser rangefinder only. We design a simple classifier and filtering algorithm for the lane detection which uses only one LRF (Laser Range Finder). Allowing extended usage of LRF, this research provides more functionality not only in range finding but also in lane detecting to mobile robots. This work will be technically helpful for robot developers to design more simple and efficient autonomous driving system using LRF.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.3
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• pp.263-272
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• 2008

We implemented a real-time servo system for a manipulator based on Laser Range Finder (LRF). and established algorithms for grasping a moving cylinder. We devised a manipulator mechanism and driving hardware based on a system board equipped with Xscale Processor with real-time operating system RTAI on Linux. The manipulator motor driver is connected to the system board via CAN communication link, and LRF is connected via RS-232C. We implemented real-time software including CAN device driver, RS-232C device driver, manipulator trajectory generator, and LRF control software. A typical application experiment for grasping a cylinder with circle motion demonstrated our system's real-time performance.

• Proceedings of the Optical Society of Korea Conference
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• 2005.02a
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• pp.124-125
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• 2005

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.6
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• pp.601-607
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• 2012

The recognition of elevator door is needed for mobile service robots to moving between floors in the building. This paper proposed the sensor fusion approach using LRF (Laser Range Finder) and camera to solve the problem. Using the laser scans by the LRF, we extract line segments and detect candidates as the elevator door. Using the image by the camera, the door candidates are verified and selected as real door of the elevator. The outliers are filtered through the verification process. Then, the door state detection is performed by depth analysis within the door. The proposed method uses extrinsic calibration to fuse the LRF and the camera. It gives better results of elevator door recognition compared to the method using LRF only.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.448-453
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• 2012

There are many researches to develop robots that improve its mobility to adapt in various uneven environments. In the paper, a hybrid wheeled and legged mobile robot is designed and a obstacle avoidance algorithm is proposed based on low power walking using LRF(Laser Range Finder). In order to stabilize the robot's motion and reduce energy consumption, we implement a low-power walking algorithm through comparison of the current value of each motors and correction of posture balance. A low-power obstacle avoidance algorithm is proposed by using LRF sensor. We improve walking stability by distributing power consumption and reduce energy consumption by selecting a shortest navigation path of the robot. The proposed methods are verified through walking and navigation experiments with the developed hybrid robot.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.26-34
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• 2008

The information of traversability and path planning is essential for UGV(Unmanned Ground Vehicle) navigation. Such information can be obtained by analyzing 3D terrain. In this paper, we present the method of 3D terrain modeling with color information from a camera, precise distance information from a 2D Laser Range Finder(LRF) and wheel encoder information from mobile robot with less data. And also we present the method of 3B terrain modeling with the information from GPS/IMU and 2D LRF with less data. To fuse the color information from camera and distance information from 2D LRF, we obtain extrinsic parameters between a camera and LRF using planar pattern. We set up such a fused system on a mobile robot and make an experiment on indoor environment. And we make an experiment on outdoor environment to reconstruction 3D terrain with 2D LRF and GPS/IMU(Inertial Measurement Unit). The obtained 3D terrain model is based on points and requires large amount of data. To reduce the amount of data, we use cubic grid-based model instead of point-based model.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.5
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• pp.468-472
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• 2013

This paper deals with the indoor localization problem for a SSMR (Skid Steering Mobile Robot) subjected to wheel-ground friction and with one LRF (Laser Range Finder). In order to compensate for some friction effect, a friction related coefficient is estimated by the recursive least square algorithm and appended to the maneuvering command. Also to reduce odometric information based localization errors, the lines are extracted with scan points of LRF and matched with the ones of the corresponding map built in advance. The present friction compensation and scan map matching schemes have been applied to a laboratory SSMR, and experimental results are given to validate the localization performance along an indoor corridor.

• 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.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.307-313
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• 2012

This paper proposes a hand-eye laser range finder (LRF) based welding plane recognition method for autonomous robotic welding. The robot welding is the process of joining a metal piece and the welding plane along the welding path predefined by the shape of the metal piece. Thus, for successful robotic welding, the position and direction of the welding plane should be exactly detected. If the detected position and direction of the plane is not accurate, the autonomous robotic welding should fail. For precise recognition of the welding plane, a line on the plane is detected by the LRF. For obtaining the line on the plane, the Hough transform is applied to the obtained data from the LRF. Since the Hough transform is based on the voting method, the sensor noise can be reduced. Two lines on the plane are obtained before and after rotation of the robot joint, and then the direction of the plane is calculated by the cross product of two direction vectors of two lines. For verifying the feasibility of the proposed method, the simulation with the robot simulator, RoboticsLab developed by Simlab Co. Ltd., is carried out.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.5
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• pp.465-470
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• 2015

This paper proposes a robust 3D mapping system for a UAV (Unmanned Aerial Vehicle) that carries a LRF (Laser Range Finder) using the sinusoidal trajectory algorithm. In the case of previous 3D mapping research, the UAV usually takes off vertically and flights up and down while the LRF is measuring horizontally. In such cases, the measuring range is limited and it takes a long time to do mapping. By using the sinusoidal trajectory algorithm proposed in this research, the 3D mapping can be time-efficient and the measuring range can be widened. The 3D mapping experiments have been done to evaluate the performance of the sinusoidal trajectory algorithm by scanning indoor walls.