• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.438-444
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• 2004

The intelligent robots that will be needed in the near future are human-friendly robots that are able to coexist with humans and support humans effectively. To realize this, robots need to recognize their position and posture in known environment as well as unknown environment. Moreover, it is necessary for their localization to occur naturally. It is desirable for a robot to estimate of his position by solving uncertainty for mobile robot navigation, as one of the best important problems. In this paper, we describe a method for the localization of a mobile robot using image information of a moving object. This method combines the observed position from dead-reckoning sensors and the estimated position from the images captured by a fixed camera to localize a mobile robot. Using the a priori known path of a moving object in the world coordinates and a perspective camera model, we derive the geometric constraint equations which represent the relation between image frame coordinates for a moving object and the estimated robot's position. Since the equations are based or the estimated position, the measurement error may exist all the time. The proposed method utilizes the error between the observed and estimated image coordinates to localize the mobile robot. The Kalman filter scheme is applied for this method. its performance is verified by the computer simulation and the experiment.

• The Journal of Korea Robotics Society
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• v.8 no.1
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• pp.51-57
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• 2013

Recently, the number of jellyfish has been rapidly grown because of the global warming, the increase of marine structures, pollution, and etc. The increased jellyfish is a threat to the marine ecosystem and induces a huge damage to fishery industries, seaside power plants, and beach industries. To overcome this problem, a manual jellyfish dissecting device and pump system for jellyfish removal have been developed by researchers. However, the systems need too many human operators and their benefit to cost is not so good. Thus, in this paper, the design, implementation, and experiments of autonomous jellyfish removal robot system, named JEROS, have been presented. The JEROS consists of an unmanned surface vehicle (USV), a device for jellyfish removal, an electrical control system, an autonomous navigation system, and a vision-based jellyfish detection system. The USV was designed as a twin hull-type ship, and a jellyfish removal device consists of a net for gathering jellyfish and a blades-equipped propeller for dissecting jellyfish. The autonomous navigation system starts by generating an efficient path for jellyfish removal when the location of jellyfish is received from a remote server or recognized by a vision system. The location of JEROS is estimated by IMU (Inertial Measurement Unit) and GPS, and jellyfish is eliminated while tracking the path. The performance of the vision-based jellyfish recognition, navigation, and jellyfish removal was demonstrated through field tests in the Masan and Jindong harbors in the southern coast of Korea.

• Journal of KIISE:Software and Applications
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• v.34 no.3
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• pp.235-245
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• 2007

UUV(Unmanned Underwater Vehicle) should possess an intelligent control software performing intellectual faculties such as cognition, decision and action which are parts of domain expert's ability, because unmanned underwater robot navigates in the hazardous environment where human being can not access directly. In this paper, we suggest a RVC intelligent system architecture which is generally available for unmanned vehicle and develope an autonomous navigation system for UUV, which consists of collision avoidance system, path planning system, and collision-risk computation system. We present an obstacle avoidance algorithm using fuzzy relational products for the collision avoidance system, which guarantees the safety and optimality in view of traversing path. Also, we present a new path-planning algorithm using poly-line for the path planning system. In order to verify the performance of suggested autonomous navigation system, we develop a simulation system, which consists of environment manager, object, and 3-D viewer.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.2
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• pp.508-516
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• 2010

An autonomous and automatic home mess-cleanup robot is newly developed in this paper. Thus far, vacuum-cleaners have lightened the burden of household chores but the operational labor that vacuum-cleaners entail has been very severe. Recently, a cleaning robot was commercialized to solve but it also was not successful because it still had the problem of mess-cleanup, which pertained to the clean-up of large trash and the arrangement of newspapers, clothes, etc. Hence, we develop a new home mess-cleanup robot (McBot) to completely overcome this problem. The robot needs the capability for agile navigation and a novel manipulation system for mess-cleanup. The autonomous navigational system has to be controlled for the full scanning of the living room and for the precise tracking of the desired path. It must be also be able to recognize the absolute position and orientation of itself and to distinguish the messed object that is to be cleaned up from obstacles that should merely be avoided. The manipulator, which is not needed in a vacuum-cleaning robot, has the functions of distinguishing the large trash that is to be cleaned from the messed objects that are to be arranged. It needs to use its discretion with regard to the form of the messed objects and to properly carry these objects to the destination. In particular, in this paper, we describe our approach for achieving accurate localization using RFID for home mess-cleanup robots. Finally, the effectiveness of the developed McBot is confirmed through live tests of the mess-cleanup task.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.11
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• pp.1092-1098
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• 2007

A mobile robot localization problem can be classified into following three sub-problems as an observation likelihood model, a motion model and a filtering technique. So far, we have developed the range sensor based, integrated localization scheme, which can be used in human-coexisting real environment such as a science museum and office buildings. From those experiences, we found out that there are several significant issues to be solved. In this paper, we focus on three key issues, and then illustrate our solutions to the presented problems. Three issues are listed as follows: (1) Investigation of design requirements of a desirable observation likelihood model, and performance analysis of our design (2) Performance evaluation of the localization result by computing the matching error (3) The semi-global localization scheme to deal with localization failure due to abrupt wheel slippage In this paper, we show the significance of each concept, developed solutions and the experimental results. Experiments were carried out in a typical modern building environment, and the results clearly show that the proposed solutions are useful to develop practical and integrated localization schemes.

• Journal of Sensor Science and Technology
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• v.29 no.5
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• pp.348-353
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• 2020

This paper presents an adaptive method to avoid obstacles in various environmental settings, using a two-dimensional (2D) LiDAR sensor for mobile robots. While the conventional reaction based smooth nearness diagram (SND) algorithms use a fixed safety distance criterion, the proposed algorithm autonomously changes the safety criterion considering the obstacle density around a robot. The fixed safety criterion for the whole SND obstacle avoidance process can induce inefficient motion controls in terms of the travel distance and action smoothness. We applied a multinomial logistic regression algorithm, softmax regression, to classify 2D LiDAR point clouds into seven obstacle structure classes. The trained model was used to recognize a current obstacle density situation using newly obtained 2D LiDAR data. Through the classification, the robot adaptively modifies the safety distance criterion according to the change in its environment. We experimentally verified that the motion controls generated by the proposed adaptive algorithm were smoother and more efficient compared to those of the conventional SND algorithms.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.4
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• pp.516-523
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• 2009

Recently, the applications of unmanned robots are increasing in various fields including surveillance and reconnaissance, planet exploration and disaster relief. To perform their missions with success, the robots should be able to evaluate terrain's characteristics quantitatively and identify traversable regions to progress toward a goal using mounted sensors. Recently, the authors have proposed techniques that extract terrain information and analyze traversability for off-road navigation of an unmanned robot. In this paper, we examine the use of 3D world models(terrain maps) to classify obstacle and safe terrain for increasing the reliability of the proposed techniques. A world model is divided into several patches and each patch is classified as belonging either to an obstacle or a non-obstacle using three types of metrics. The effectiveness of the proposed method is verified on real terrain maps.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.9
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• pp.805-812
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• 2013

This paper describes the design, control, and localization which comprise major aspects of the development of underwater robots for the mine disposal. The developed robots are called the Mine Killer (MK-1) and MK-2. MK-1 had been developed from September 2009 and was presented at the 9-th International Symposium at NPS Monterey CA, on May 17-21, 2010[1]. The paper presents design of MK-1 and MK-2 in detail with comparison of these two versions of MKs. Then it derives hydrodynamic coefficients of MK-1. Based on the coefficients, the motion of MK-1 is simulated for straight line motion and circular motion. Also simulation results for PD control, LQ control and sliding mode control are presented. Finally, it shows a particle filter method for localization of MK-1 and MK-2 using simple range data from acoustic beacons.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.12
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• pp.1240-1247
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• 2011

The localization of mobile robot in environment is a major concern in mobile robot navigation. So, many kinds of localization techniques have been researched for several years. Among them, the positioning system using ultrasound has received attention. Most of these ultrasonic positioning systems to synchronize the transmitters and receivers are used for RF (Radio Frequencies). However, due to the use of RF, the interference problems can not be avoided and the performance of radio frequencies directly affects the positioning performance. So we proposed the ultrasonic positioning system without synchronizing RF. The proposed system is based on existing USAT (Ultrasonic Satellite System) adopted infrastructure transmitting type, and consists of transmitter and receiver synchronizing modules instead of the radio frequency transmitters and receiver. The ultrasonic transmitters and receivers are synchronized individually by the transmitter and receiver synchronizing modules. In order to calculate the bias between the transmitter and receiver synchronizing modules, new positioning algorithm similar to GPS was proposed. The positioning performance of the improved USAT without synchronizing RF and the validity of the proposed positioning algorithm are verified and evaluated by experiments.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.163-168
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• 2002

The voting algorithm for action selection performs self-improvement by Reinforcement learning algorithm in the dynamic environment. The proposed voting algorithm improves the navigation of the robot by adapting the eligibility of the behaviors and determining the Command Set Generator (CGS). The Navigator that using a proposed voting algorithm corresponds to the CGS for giving the weight values and taking the reward values. It is necessary to decide which Command Set control the mobile robot at given time and to select among the candidate actions. The Command Set was learnt online by means as Q-learning. Action Selector compares Q-values of Navigator with Heterogeneous behaviors. Finally, real-world experimentation was carried out. Results show the good performance for the selection on command set as well as the convergence of Q-value.