• The Journal of Korea Robotics Society
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• v.16 no.3
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• pp.250-259
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• 2021

Path planning is one of the important technologies for automated parking. It requires to plan a collision-free path considering the vehicle's kinematic constraints such as minimum turning radius or steering velocity. In a complex parking lot, Rapidly-exploring Random Tree^* (RRT^*) can be used for planning a parking path, and Reeds-Shepp or Hybrid Curvature can be applied as a tree-extension method to consider the vehicle's constraints. In this case, each of these methods may affect the computation time of planning the parking path, path-tracking error, and parking success rate. Therefore, in this study, we conduct comparative analysis of two tree-extension functions: Reeds-Shepp (RS) and Hybrid Curvature (HC), and show that HC is a more appropriate tree-extension function for parking path planning. The differences between the two functions are introduced, and their performances are compared by applying them with RRT^*. They are tested at various parking scenarios in simulation, and their advantages and disadvantages are discussed by computation time, cross-track error while tracking the path, parking success rate, and alignment error at the target parking spot. These results show that HC generates the parking path that an autonomous vehicle can track without collisions and HC allows the vehicle to park with lower alignment error than those of RS.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.19-24
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• 1992

Real-time mobile robot controllers usually have been designed with an emphasis on control theory ignoring the importance of system integration. This paper demonstrates that useful mobile robots require a real time controller with a wide range of capabilities in addition to control theory. These capabilities include: path-planning, position estimation, path tracking control and wheel control. An architectural framework supporting these capabilities has been designed and implemented. Using this frame work, individual modules such as a path planner, a path tracking controller, position estimators, wheel controllers and other cruical elements have been successfully integrated into the control system for the LCAR robot which was developed as a proto-type mobile robot in our laboratory. The context of the research, the architecture, its implementation and performance results from experiments are discussed.

• Journal of Korea Multimedia Society
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• v.11 no.3
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• pp.281-291
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• 2008

In this paper, we propose a new double talk detection algorithm which detects near end signals with less degradation, tracking echo path variation of echo canceler simultaneously. Our method makes use of a cross-correlation between channel input signals and estimated error signals and a normalized cross-correlation between microphone input signals and estimated error signals. By combing thresholds for these cross-correlations pertinently, this algorithm discriminates between variation of echo path and occurrence of double talk. These two cross-correlation are used to detect double talk periods, tracking echo path variation. During the detection period, adjustive adaptive filter is ceased to prevent the echo canceler from being disturbed by near end signals. Also, the echo canceler will still be kept on for tracking any variation in echo path. Through computer simulation results, it was confirmed that the proposed algorithm shows better performance, tracking echo path variation and detecting the double talk periods, than the Ye et. al's and the NLMS algorithms from ERLE viewpoint.

• The KIPS Transactions:PartC
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• v.10C no.2
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• pp.191-198
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• 2003

Location tracking is used to keep track of the location information of a mobile terminal in an idle state for a call setup between mobile terminals. In this paper, we introduce a new location tracking strategy that utilizes spatial locality to have better performance than a movement based location tracking strategy. We reduce a lot of unnecessary location updates by updating the location information of a mobile terminal using the virtual movement path, which is generated after removing spatial localities in the actual movement path. Simulation results show that the proposed strategy greatly reduces the overall location tracking cost.

• Journal of Drive and Control
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• v.16 no.2
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• pp.22-29
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• 2019

This study presented a model predictive tracking control algorithm of autonomous truck based on object state estimation using extended Kalman filter. To design the model, the 1-layer laser scanner was used to estimate position and velocity of the object using extended Kalman filter. Based on these estimations, the desired linear path for object tracking was computed. The lateral and yaw angle errors were computed using the computed linear path and relative positions of the truck. The computed errors were used in the model predictive control algorithm to compute the optimal steering angle for object tracking. The performance evaluation was conducted on Matlab/Simulink environments using planar truck model and actual point data obtained from laser scanner. The evaluation results showed that the tracking control algorithm developed in this study can track the object reasonably based on the model predictive control algorithm based on the estimated states.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.9
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• pp.723-732
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• 2001

In this paper, the neurofuzzy controller of optically guided AGV is proposed to improve the path-tracking performance A differential steered AGV has front-side and rear-side optical sensors, which can identify the guiding path. Due to the discontinuity of measured data in optical sensors, optically guided AGVs break away easily from the guiding path and path-tracking performance is being degraded. Whenever the On/Off signals in the optical sensors are generated discontinuously, the motion errors can be measured and updated. After sensing, the variation of motion errors can be estimated continuously by the dead reckoning method according to left/right wheel angular velocity. We define the estimated contour error as the sum of the measured contour in the sensing error and the estimated variation of contour error after sensing. The neurofuzzy system consists of incorporating fuzzy controller and neural network. The center and width of fuzzy membership functions are adaptively adjusted by back-propagation learning to minimize th estimated contour error. The proposed control system can be compared with the traditional fuzzy control and decision system in their network structure and learning ability. The proposed control strategy is experience through simulated model to check the performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.476-482
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• 2010

In this paper, we propose a robust path tracking control method for autonomous underwater vehicle with variable speed. The proposed path tracking controller consists of a kinematic controller and a dynamic controller. First, the kinematic controller computes the surge speed and yaw rate to follow the reference path with variable speed. Then the dynamic controller controls the thrust force and yaw torque to move the AUV actually. In the dynamic control, we assume that the sway speed is a disturbance. In addition the dynamic controller is designed based on sliding mode conrol. We also demonstrate the stability of the proposed control method by Lyapunov stability theory. Finally, simulation results illustrate the performance of the proposed control method.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.3
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• pp.90-101
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• 2000

In this paper, we propose a controller for differentially steered wheeled mobile robots. The controller uses input-output linearization algorithm and artificial neural network to stabilize the dynamic model and compensate uncertainties. The proposed neural network part has 6 inputs, 1 hidden layer, 2 torque outputs and features fast online learning and good performance on structure error learning basis. Simulation results show that the proposed controller perform precisely tracking of reference path and is robust to uncertainties.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1824-1827
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• 1997

Majority of industrial robots are controlled by a simple joint servo control of joint actuators. In this type of control, the performance of control is influenced greatly by the joint interaction torques including Coriolis and centrifugal forces, which act as disturbance torques to the control system. As the speed of the robot increases, the effect of this disturbance torque increases, and makes the high speed-high precision control more difficult to achieve. In this paper, the joint disturbance torque of robots is analyzed. The joint disturbance torque is defined using the coefficients of dynamic equation of motion, and for the case of a 2DOF planar robot, the conditions for the maximum joint disturbance torques are identified, and the effect of link parameters and joint variables on the joint disturbance torque are examined. Then, a solutioin to the optimal path placement problem is proposed that minimizes the joint disturbance torque are examined. then, a solution to the optimal path placement problem is proposed that minimizes the joint disturbance torque during a straight line motion. the proposed method is illustrated using computer simulation. the proposed solution method cna be applied to the class of robots that are controlled by independent joint sevo control, which includes the vast majority of industrial robots. By minimizing the joint disturbacne torque during the motion, the simple joint servo controlled robot can move with improved path tracking accuracy at high speed.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.35-42
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• 2003

This paper proposes a simple, robust, nonlinear controller based on Lyapunov stability for tracking the reference welding path and velocity of a two-wheeled welding mobile robot (WMR). The system has three degrees of freedom including two wheels and one torch slider. Torch slider motion is used for faster tracking because the welding speed is very slow. Control law is obtained from the Lyapunov control function to ensure the asymptotical stability of the system. The controller has three free parameters for adjusting the performance of the controlled system. A simple way of measuring the errors using two potentiometers is introduced. The effectiveness of the proposed controller is shown through simulation results.