• Journal of Astronomy and Space Sciences
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• v.37 no.3
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• pp.187-197
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• 2020

In order to avoid the high cost and high risk of demonstration mission of rendezvous-docking technology, missions using nanosatellites have recently been increasing. However, there are few successful mission cases due to many limitations of nanosatellites like small size, power limitation, and limited performances of sensor, thruster, and controller. To improve the probability of rendezvous-docking mission success using nanosatellite, a rendezvous-docking phase analysis tool for nanosatellites is developed. The tool serves to analyze the relative position and attitude control of the chaser satellite at the docking phase. In this tool, the Model Predictive Controller (MPC) is implemented as a controller, and Extended Kalman Filter (EKF) is adopted as a filter for noise filtering. To verify the performance and effectiveness of the developed tool for nanosatellites, simulation study was conducted. Consequently, we confirmed that this tool can be used for the analysis of relative position and attitude control for nanosatellites in the rendezvous-docking phase.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.147.5-147
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• 2001

This paper describes a gait algorithm and a velocity limitation method for a Leg-Wheel Robot. The gait algorithm enables the robot to preserve continuous locomotion even if the velocity command varies extensively. The velocity limitation method restricts the commanded velocity when it exceeds the mechanical limitation of the robot. Combined use of the velocity limitation method with the gait algorithm ensures the continuity of locomotion, and makes the gait pattern efficient with a long step length and low frequency of leg phase change. These methods can be applied to locomotion on unexplored rough terrain even if the range of roughness is unknown.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.7
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• pp.575-583
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• 2017

This paper describes the design and evaluation of a driving mode decision algorithm for automated driving for merge situations on highways. For the development of a highly automated driving control algorithm for merge situations, the driving mode decision is crucial for merging appropriately. There are two driving modes: lane keeping and lane changing (merging). The merge mode decision is determined based on the state of the surrounding vehicles and the remaining length of the merge lane. In the merge mode decision algorithm, merge possibility and the desired merge position are decided to change the lane safely and quickly. A safety driving envelope is defined based on the desired driving mode using the information on the surrounding vehicles' behaviors. To obtain the desired steering angle and longitudinal acceleration for maintaining the subject vehicle in the safe driving envelope, a motion planning controller is designed using model predictive control (MPC), with constraints that are decided considering the vehicle dynamics, safe driving envelope, and actuator limit. The proposed control algorithm has been evaluated via computer simulation studies.

• The Journal of Korea Robotics Society
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• v.18 no.2
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• pp.172-181
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• 2023

This paper presents research on the stability of control for a quadruped robot with two different leg structure designs. The focus of the research is on the design and analysis of the leg structures in terms of their impact on the stability and robustness of the robot's motion. First, a static analysis was performed in the simulation to compare the structural strength of the legs when the same force was applied. Secondly, two quadruped robots were built, each equipped with differently designed legs, and performed trot gait walking in the real world. And the states of the robots and the torques of each joint were analyzed and compared. In conclusion, based on the results of structural analysis in simulation and the actual walking experiments with the robots, it was demonstrated that the legs designed to be structurally robust improved the control stability of the quadruped robot.

• The Journal of Korea Robotics Society
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• v.15 no.2
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• pp.147-159
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• 2020

Over the last years, a number of different path following methods for the autonomous parking system have been proposed for tracking planned paths. However, it is difficult to find a study comparing path following methods for a short path length with large curvature such as a parking path. In this paper, we conduct a comparative study of the path following methods for perpendicular parking. By using Monte-Carlo simulation, we determine the optimal parameters of each controller and analyze the performance of the path following. In addition, we consider the path following error occurred at the switching point where forward and reverse paths are switched. To address this error, we conduct the comparative study of the path following methods with the one thousand switching points generated by the Monte-Carlo method. The performance of each controller is analyzed using the V-rep simulator. With the simulation results, this paper provides a deep discussion about the effectiveness and limitations of each algorithm.