• Journal of Ocean Engineering and Technology
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• v.8 no.2
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• pp.56-63
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• 1994

This paper presents a design method of multivariable robust servo system for tracking control system for AUV(Autonomous Underwater Vehicle). In order to obtain the basic data for the design of the tracking control system, the control algorithm is evaluated in the view of computer simulation results. The tracking control is carried out for an AUV with 2 main thrusters, 2 side thrusters and 2 thrusters for the movement to up-down direction. The results of computer simulation show that the proposed multivariable servo system design method is an efficient method for the control performance of tracking control system of AUV under severe underwater environment.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.800-804
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• 1999

An intelligent path planning algorithm for an autonomous cleaning robot is presented. This algorithm recognizes obstacle on the architectural CAD draft and generates subgoals as tracking points which executes the area filling task based on heuristic approach. A sweeping path is planned by sequentially connecting the tracking points in such a way that (1) the connected line segments should be crossed, (2) the total tracking points should be as short as possible, (3) the tracking line should not pass through the obstacle. Feasibility of the developed techniques has been demonstrated on real architectural CAD draft.

• 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 Society for Precision Engineering
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• v.26 no.12
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• pp.69-77
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• 2009

In this study an ANFIS-based trajectory tracking motion control algorithm is proposed for autonomous garage and parallel parking of a model car. The ANFIS controller is trained off-line using data set which obtained by Mandani fuzzy inference system and thereby the processing time decreases almost in half. The controller with a steering delay compensator is tuned through simulations performed under MATLAB/Simulink environment. Experiments are carried out with the model car for garage and parallel parking. The experimental results show that the trajectory tracking performance is satisfactory under various initial and road conditions

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.2
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• pp.204-210
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• 2018

As the 4th industrial revolution and artificial intelligence technology develop, it is expected that there will be a revolutionary changes in the security robot. However, artificial intelligence system requires enormous hardwares for tremendous computing loads, and there are many challenges that need to be addressed more technologically. This paper introduces precise tracking control technique of autonomous system that need to move repetitive paths for security purpose. The input feedforward signal is generated by using the inverse based iterative learning control theory for the 2 input 2 output nonminimum-phase system which was difficult to overcome by the conventional feedback control system. The simulation results of the input signal generation and precision tracking of given path corresponding to the repetition rate of extreme, such as bandwidth of the system, shows the efficacy of suggested techniques and possibility to be used in military security purposes.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.417-420
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• 1998

A heuristic sweeping algorithm for an autonomous smearing robot which executes the area filling task is proposed. This algorithm searches tracking points with the obstacle andenvironment wall while the robot tracking whole workspace, and finds sequential tracking line by sequentally connecting the tracking points in such a way that (1) the line should be never crossed, (2) the total tracking points should be is linked as short as possible, and (3) the tracking link should be cross over the obstacle in the work-space. If the line pass through the obstacle, hierarchical collision free algorithm proposed is implied. The proposed algorithm consists of (1) collision detection procedure, (2) obstacle map making procedures, (3) tracking points generation procedures for subgosls, (4) tracking points scanning procedures, and (5) obstacle avoidance procedure.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.11a
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• pp.483-484
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• 2008

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.87-88
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• 2010

• 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 Auto-vehicle Safety Association
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• v.12 no.2
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• pp.27-32
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• 2020

This paper presents steering system requirements to ensure the stabilized lateral control of autonomous driving vehicles. The two main objectives of a lateral controller in autonomous vehicles are maintenance of vehicle stability and tracking of the desired path. Even if the desired steering angle is immediately determined by the upper level controller, the overall controller performance is greatly influenced by the specification of steering system actuators. Since one of the major inescapable traits that affects controller performance is the time delay of the steering actuator, our work is mainly focused on finding adequate parameters of high level control algorithm to compensate these response characteristics and guarantee vehicle stability. Actual vehicle steering angle response was obtained with Electric Power Steering (EPS) actuator test subject to various longitudinal velocity. Steering input and output response analysis was performed via MATLAB system identification toolbox. The use of system identification is advantageous since the transfer function of the system is conveniently obtained compared with methods that require actual mathematical modeling of the system. Simulation results of full vehicle model suggest that the obtained tuning parameter yields reduced oscillation and lateral error compared with other cases, thus enhancing path tracking performance.