• Journal of the Korean Society for Precision Engineering
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• v.26 no.6
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• pp.74-80
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• 2009

Path tracking of unmanned vehicle is a basis of autonomous driving and navigation. For the path tracking, it is very important to find the exact position of a vehicle. GPS is used to get the position of vehicle and a direction sensor and a velocity sensor is used to compensate the position error of GPS. To detect path lines in a road image, the bird's eye view transform is employed, which makes it easy to design a lateral control algorithm simply than from the perspective view of image. Because the driving speed of vehicle should be decreased at a curved lane and crossroads, so we suggest the speed control algorithm used GPS and image data. The control algorithm is simulated and experimented from the basis of expert driver's knowledge data. In the experiments, the results show that bird's eye view transform are good for the steering control and a speed control algorithm also shows a stability in real driving.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.174-192
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• 2021

The LIDAR sensor, which provides higher cognitive performance than cameras and radar, is difficult to apply to ADAS or autonomous driving because of its high price. On the other hand, as the price is decreasing rapidly, expectations are rising to improve existing autonomous driving functions by taking advantage of the LIDAR sensor. In level 3 autonomous vehicles, when a dangerous situation in the cognitive module occurs due to a sensor defect or sensor limit, the driver must take control of the vehicle for manual driving. If the driver does not respond to the request, the system must automatically kick in and implement a minimum risk maneuver to maintain the risk within a tolerable level. In this study, based on this background, a LIDAR-based LKS MRM algorithm was developed for the case when the normal operation of LKS was not possible due to troubles in the cognitive system. From point cloud data collected by LIDAR, the algorithm generates the trajectory of the vehicle in front through object clustering and converts it to the target waypoints of its own. Hence, if the camera-based LKS is not operating normally, LIDAR-based path tracking control is performed as MRM. The HAZOP method was used to identify the risk sources in the LKS cognitive systems. B, and based on this, test scenarios were derived and used in the validation process by simulation. The simulation results indicated that the LIDAR-based LKS MRM algorithm of this study prevents lane departure in dangerous situations caused by various problems or difficulties in the LKS cognitive systems and could prevent possible traffic accidents.

• The Journal of Korea Robotics Society
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• v.17 no.2
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• pp.142-151
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• 2022

With the advent of the 4th industrial revolution, autonomous driving technology is being commercialized in various industries. However, research on autonomous driving so far has focused on platforms with wheel-type platform. Research on a tracked platform is at a relatively inadequate step. Since the tracked platform has a different driving and steering method from the wheel-type platform, the existing research cannot be applied as it is. Therefore, a path-tracking algorithm suitable for a tracked platform is required. In this paper, we studied a path-tracking algorithm for a tracked platform based on a GPS sensor. The existing Pure Pursuit algorithm was applied in consideration of the characteristics of the tracked platform. And to compensate for "Cutting Corner", which is a disadvantage of the existing Pure Pursuit algorithm, an algorithm that changes the LAD according to the curvature of the path was developed. In the existing pure pursuit algorithm that used a tracked platform to drive a path including a right-angle turn, the RMS path error in the straight section was 0.1034 m and the RMS error in the turning section was measured to be 0.2787 m. On the other hand, in the variable LAD algorithm, the RMS path error in the straight section was 0.0987 m, and the RMS path error in the turning section was measured to be 0.1396 m. In the turning section, the RMS path error was reduced by 48.8971%. The validity of the algorithm was verified by measuring the path error by tracking the path using a tracked robot platform.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.3
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• pp.202-207
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• 2013

In the paper, we design a autonomous mobile robot for freight transportation and propose an operation method of the robot in the warehouse. In order to implement autonomous navigation, it is needed to recognize the position of the robot and track the path to the target. Previous methods are hard to change the workspace environment and need high cost to install and keep a maintenance of the system. The lifter of freight transportation robot is designed to load and unload a baggage through up and down motion. Also, ultrasonic sensor, RFID, QR-code and camera sensor is used to carry out various functions while the robot navigates in the various environment. We design an operation method of the mobile robot in order to effectively arrive a goal position and transport a freight. The proposed methods are verified through various experiments.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.5
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• pp.84-92
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• 2000

Recently, the progress of industrialization has been taken concern of material handling automation. So for, the conveyor belt has been popular for material handling. However, this system has many disadvantages such as the space, cost, etc. In this paper, a new navigation algorithm using fuzzy is introduced. The mobile robot follows a line installed on the roads. These informations are inputted with three approximate sensors. These obtained informations are analyzed with fuzzy control technique fur autonomous steering. Therefore, unlike existing systems, high reliability is guaranteed under bad environment conditions. The installation and maintenance of a line is easily made at lower cost. This developed mobile robot can be applied to material handling automation in manufacturing system, hospital, inter-office document del ivory.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1417-1423
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• 2020

This paper proposes a waypoint guidance algorithm for the Autonomous Underwater Vehicle(AUV). The proposed simplified guidance algorithm is presented, which is combined LOS guidance and cross-track guidance for path following. Cross-track error is calculated using the position of the AUV and reference path. LOS guidance and cross-track guidance are appropriately changed according to cross-track error. And the stability of the system has been improved using variable cross-track control gain by cross-track error. Also, in this paper, navigation hardware in-the loop simulation(HILS) is implemented to verify navigation algorithm of AUV that performs combined navigation using inertial navigation device and doppler velocity log(DVL). Finally, we design integrated system HILS (including navigation HILS) for performance verification of guidance algorithm of the autonomous underwater vehicle. By comparing the sea test result with HILS result, the proposed guidance algorithm and HILS configuration were confirmed be correct.

• Journal of Drive and Control
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• v.20 no.4
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• pp.27-34
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• 2023

Weeding in orchards is closely associated with productivity and quality. The customary weeding process is both labor-intensive and time-consuming. To solve the problems, there is need for automation of agricultural robots and machines in the agricultural field. On the other hand, orchards have complicated working areas due to narrow spaces between trees and amorphous terrain. Therefore, it is necessary to develop customized robot technology for unmanned weeding work within the department. This study developed a path generation and path control method for unmanned weeding according to the orchard environment. For this, the width of the weeding span, the number of operations, and the width of the weeding robot were used as input parameters for the orchard environment parameters. To generate a weeding path, a weeding robot was operated remotely to obtain GNSS-based location data along the superheated center line, and a driving performance test was performed based on the generated path. From the results of orchard field tests, the RMSE in weeding period sections was measured at 0.029 m, with a maximum error of 0.15 m. In the steering period within row and steering to the next row sections, the RMSE was 0.124 m, and 0.047 m, respectively.

• Journal of Sensor Science and Technology
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• v.20 no.3
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• pp.178-186
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• 2011

Line tracking is a well defined method of mobile robot navigation. It is simple in concept, technically easy to implement, and already employed in many industrial sites. Among several different line tracking methods, magnetic sensing is widely used in practice. In comparison, vision-based tracking is less popular due mainly to its sensitivity to surrounding conditions such as brightness and floor characteristics although vision is the most powerful robotic sensing capability. In this paper, a vision-based robust path line detection technique is proposed for the navigation of a mobile robot assuming uncontrollable surrounding conditions. The technique proposed has four processing steps; color space transformation, pixel-level line sensing, block-level line sensing, and robot navigation control. This technique effectively uses hue and saturation color values in the line sensing so to be insensitive to the brightness variation. Line finding in block-level makes not only the technique immune from the error of line pixel detection but also the robot control easy. The proposed technique was tested with a real mobile robot and proved its effectiveness.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.219-230
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• 2017

This paper proposes a vehicle to road tracking algorithm based on vision sensor by using EKF(Extended Kalman Filter). The lateral offset, heading angle, and curvature which are obtained from vehicle to road tracking might be used as inputs to steering controller of LKAS(Lane Keeping Assist System) or for the warning decision logic of LDWS(Lane Departure Warning System). To the end, in this paper, the yaw rate, steering angle, and vehicle speed as well as lane raw points together with considering of vehicle lateral dynamics are utilized to improve the exactness and convergence of the vehicle to road tracking. The proposed algorithm has been tested at a proving ground that consists of straight and curve sections and compared with GPS datum using DGPS-RTK equipment to show the feasibility of the proposed algorithm.

• The Journal of Korea Robotics Society
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• v.18 no.3
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• pp.251-259
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• 2023

In the path traveling of differential-drive robots, the steering controller plays an important role in determining the path-following performance. When a robot with a pure-pursuit algorithm is used to continuously drive a right-angled driving path in an unstructured environment without turning in place, the robot cannot accurately follow the right-angled path and stops driving due to the ground and motor load caused by turning. In the case of pure-pursuit, only the current robot position and the steering angle to the current target path point are generated, and the steering component does not reflect the speed plan, which requires improvement for precise path following. In this study, we propose a driving algorithm for differentially driven robots that enables precise path following by planning the driving speed using the radius of curvature and fusing the planned speed with the steering angle of the existing pure-pursuit controller, similar to the Model Predict Control control that reflects speed planning. When speed planning is applied, the robot slows down before entering a right-angle path and returns to the input speed when leaving the right-angle path. The pure-pursuit controller then fuses the steering angle calculated at each path point with the accelerated and decelerated velocity to achieve more precise following of the orthogonal path.