• Journal of Institute of Control, Robotics and Systems
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• v.16 no.5
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• pp.428-432
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• 2010

In this paper, an adaptive formation control based on the leader-following approach is proposed for multiple mobile robots with time varying parameters. The proposed controller does not require the velocity information of the leader robot, which is commonly assumed that it is either measured or telecommunicated. In order to estimate time varying velocities of the leader robot, the smooth projection algorithm is employed. From the Lyapunov stability theory, it is proved that the proposed control scheme can guarantee the uniform ultimate boundedness of error signals of the closed-loop system. Finally, the computer simulations are performed to demonstrate the performance of the proposed control system.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.79-82
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• 1987

This paper describes the mobile robot system to recognize the guidance tape, and presents the locomotion algorithm. It is composed of image processing unit, A/ID converter and camera. This system converts video image to binary image by setting an optimal threshold and obtains the parameters to move the robot. The mobile robot moves according to the programmed route in memory. But after recognized the obstacle on the locomotion route, this system constructs the new route and the robot moves following the new route.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1258-1261
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• 1993

This paper presents a methodology of path planning and navigation for an autonomous mobile robot. A fast algorithm using decomposition technique, which computes the optimal paths between all pairs of nodes, is proposed for real-time calculation. The robot is controlled by fuzzy approximation reasoning. Our new methodology has been implemented on a mobile robot. The results show that the robot successfully navigates to its destination following the optimal path.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1249-1255
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• 2004

A new parameter adaptation scheme for RBF Neural Network (NN) has been developed in this paper. Even though the RBF Neural Network (NN) based controllers are robust against both un-modeled dynamics and external disturbances, the performance is not satisfactory for a fast and precise mobile robot. To improve the tracking performance as well as robustness, all the parameters of RBF NN are updated in real time. The stability of this control law is rigorously proved by following the Lyapunov stability theory and shown by the experimental simulations. The fact that all of the weighting factors, width and center of RBF NN have been updated implies that this scheme utilizes all the possibilities in RBF NN to make the controller robust and precise while the mobile robot is following un-known trajectories. The performance of this new algorithm has been compared to the conventional RBF NN controller where some of the parameters are adjusted for robustness.

• The Journal of Korea Robotics Society
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• v.6 no.3
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• pp.255-262
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• 2011

Many intelligent robots have to be given environmental information to perform tasks. In this paper an intelligent robot, that is, a cleaning robot used a sensor fusing method of two sensors: LRF and StarGazer, and then was able to obtain the information. Throughout wall following using laser displacement sensor, LRF, the working area is built during the robot turn one cycle around the area. After the process of wall following, a path planning which is able to execute the work effectively is established using flow network algorithm. This paper describes an algorithm for minimal turning complete coverage path planning for intelligent robots. This algorithm divides the whole working area by cellular decomposition, and then provides the path planning among the cells employing flow networks. It also provides specific path planning inside each cell guaranteeing the minimal turning of the robots. The proposed algorithm is applied to two different working areas, and verified that it is an optimal path planning method.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.2
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• pp.207-216
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• 1991

In the case that the robot manipulator should respond to the variance and uncertainty of the environment in performing preforming precision tasks, it is indispensable that the robot utilizes the various sensors for intrlligence. In this paper, the robot force control method is implemented with a force/torque sensor, two personal computers, and a PUMA 560 manipulator for performing the various application tadks. The hybrid position/force control method is used to control the force and position axis separately. An interface board is designed to read the force/torque sensor output into the computer. Since the two computers should exchange the information quickly, a common memory board is designed. Before the algorithms of application tasks are developed, the basic force commands must be supplied. Thus, the MOVE-UNTIL command is used at the discrete time instant and, the MOVE-COMPLY is used at the continuous time instant for receiving the force feedback information. Using the two basic force commands, three application algorithms are developed and implemented for edge-following, insertion, and grinding tasks.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.1
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• pp.35-42
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• 2020

In this paper, we propose a method to keep the robot at a distance of 30 to 45cm from the user in consideration of each individual's minimum area and inconvenience by using a 2D LiDAR sensor LDS-01 as the secondary sensor along with a QR code. First, the robot determines the brightness of the video and the presence of a QR code. If the light is bright and there is a QR code due to human's presence, the range of the 2D LiDAR sensor is set based on the position of the QR code in the captured image to find and follow the correct target. On the other hand, when the robot does not recognize the QR code due to the low light, the target is followed using a database that stores obstacles and human actions made before the experiment using only the 2D LiDAR sensor. As a result, our robot can follow the target person in four situations based on nine locations with seven types of motion.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.1
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• pp.29-34
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• 2013

This study presents the remote control of a mobile robot using iPhone based on ad hoc communication. Two control interfaces are proposed to control a mobile robot using iPhone : Remote control by a user and autonomous control. To evaluate the effectiveness of algorithms for trajectory following, a simulator are developed where a virtual robot follows a referenced trajectory in a monitor by iPhone interface. In the proposed simulator, some algorithms are tested how they work well or not for trajectory following of a mobile robot. Comparative results by remote user control and autonomous control are shown. Results of an experiment show that the proposed simulator can be effectively used for testing the effectiveness of autonomous tracking algorithms.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.102-105
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• 2005

A Effective wall following plays important role for the mapping behaviors which determine the entire memory size and the shape of map before building a map. In case of wall following, attacking those cause by curved wall or obstacles brings a bad stuff that makes ripples on the moving trajectory. These types of ripples come to an end with problems that increase the load of calculation and sensing errors. In this paper, a new sensing method and its corresponding controller are suggested for problems. It minimizes the occurrence of the trajectory ripples.

• 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.