• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.7
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• pp.655-663
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• 2017

With the development of ICT technology, the indoor utilization of robots is increasing. Research on transportation, cleaning, guidance robots, etc., that can be used now or increase the scope of future use will be advanced. To facilitate the use of mobile robots in indoor spaces, the problem of self-location recognition is an important research area to be addressed. If an unexpected collision occurs during the motion of a mobile robot, the position of the mobile robot deviates from the initially planned navigation path. In this case, the mobile robot needs a robust controller that enables the mobile robot to accurately navigate toward the goal. This research tries to address the issues related to self-location of the mobile robot. A robust position recognition system was implemented; the system estimates the position of the mobile robot using a combination of encoder information of the mobile robot and the absolute space coordinate transformation information obtained from external video sources such as a large number of CCTVs installed in the room. Furthermore, vector field histogram method of the pass traveling algorithm of the mobile robot system was applied, and the results of the research were confirmed after conducting experiments.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.291-296
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• 2008

This paper presents the dodecagon-based Q-leaning and SVM algorithm for object search with multiple robots. We organized an experimental environment with several mobile robots, obstacles, and an object. Then we sent the robots to a hallway, where some obstacles were tying about, to search for a hidden object. In experiment, we used four different control methods: a random search, a fusion model with Distance-based action making(DBAM) and Area-based action making(ABAM) process to determine the next action of the robots, and hexagon-based Q-learning and dodecagon-based Q-learning and SVM to enhance the fusion model with Distance-based action making(DBAM) and Area-based action making(ABAM) process.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.4
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• pp.385-389
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• 2009

This paper deals with the enhancement of the complex potential navigation for wheeled mobile robots. The circle theorem from complex function theory is used to avoid an obstacle, and the enhancement to avoid multiple obstacles is proposed. The limit cycle navigation can be combined for robot to kick the ball to the intentioned direction. Avoiding step and superposing twin vortices can be applied to adjust the direction of robot's trajectory. The proposed method is verified through a set of simulation works, and the feasibilities for the enhancement of complex potential theory are successful.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.363-363
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• 2000

The conventional algorithms which identify and follow a moving target using a camera located at a fixed position are not appropriate for applying to the cases o( using mobile robots, due to their long processing time. This paper proposes a new tracking algorithm based on the sensing system which uses a line light with a single camera. The algorithm categirizes the motion patterns of a pair of mobile objects into parallel, branching, and merging motion, to decide of which objects the trajectories should be calculated to follow the reference object. Kalman Filter is used to estimate the trajectories of selected objects. The proposed algorithm has shown in the experiments that the mobile robot does not miss the target in most cases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5496-5521
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• 2018

Object detection and tracking is the basic capability of mobile robots to achieve natural human-robot interaction. In this paper, an object tracking system of mobile robot is designed and validated using improved multiple instance learning algorithm. The improved multiple instance learning algorithm which prevents model drift significantly. Secondly, in order to improve the capability of classifiers, an active sample selection strategy is proposed by optimizing a bag Fisher information function instead of the bag likelihood function, which dynamically chooses most discriminative samples for classifier training. Furthermore, we integrate the co-training criterion into algorithm to update the appearance model accurately and avoid error accumulation. Finally, we evaluate our system on challenging sequences and an indoor environment in a laboratory. And the experiment results demonstrate that the proposed methods can stably and robustly track moving object.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1603-1623
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• 2020

Multiple Mobile Robots System (MMRS) has shown many attractive features in lots of real-world applications that motivate their rapid and wide diffusion. In MMRS, the Cooperative Localization (CL) is the basis and premise of its high-performance task. However, the statistical characteristics of the system noise should be already known in traditional CL algorithms, which is difficult to satisfy in actual MMRS because of the numerous of disturbances form the complex external environment. So the CL accuracy will be reduced. To solve this problem, an improved Adaptive Active Cooperative Localization (A²CL) algorithm based on information optimization strategy for MMRS is proposed in this manuscript. In this manuscript, an adaptive information fusion algorithm based on the variance component estimation under Extended Kalman filter (VCEKF) method for MMRS is introduced firstly to enhance the robustness and accuracy of information fusion by estimating the covariance matrix of the system noise or observation noise in real time. Besides, to decrease the effect of observation uncertainty on CL accuracy further, an observation optimization strategy based on information theory, the Model Predictive Control (MPC) strategy, is used here to maximize the information amount from observations. And semi-physical simulation experiments were carried out to verity the A²CL algorithm's performance finally. Results proved that the presented A²CL algorithm based on information optimization strategy for MMRS cannot only enhance the CL accuracy effectively but also have good robustness.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.1
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• pp.67-75
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• 2010

This paper presents localization algorithm for mobile robot in outdoor environment. Outdoor environment includes the uncertainty on the ground. Magnetic sensor or IMU(Inertial Measurement Unit) has been used to estimate robot's heading angle. Two sensor is unavailable because mobile robot is electric car affected by magnetic field. Heading angle estimation algorithm for mobile robot is implemented using gyro sensor module consisting of 1-axis gyro sensors. Localization algorithm applied Extended Kalman filter that utilized GPS and encoder, gyro sensor module. Experiment results show that proposed localization algorithm improve considerably localization performance of mobile robots.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.406-411
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• 2005

In this paper, we present the area-based Q-learning to search a target object using multiple robot. To search the target in Markovian space, the robots should recognize their surrounding at where they are located and generate some rules to act upon by themselves. Under area-based Q-learning, a robot, first of all, obtains 6-distances from itself to environment by infrared sensor which are hexagonally allocated around itself. Second, it calculates 6-areas with those distances then take an action, i.e., turn and move toward where the widest space will be guaranteed. After the action is taken, the value of Q will be updated by relative formula at the state. We set up an experimental environment with five small mobile robots, obstacles, and a target object, and tried to search for a target object while navigating in a unknown hallway where some obstacles were placed. In the end of this paper, we presents the results of three algorithms - a random search, area-based action making (ABAM), and hexagonal area-based Q-teaming.

• ETRI Journal
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• v.29 no.2
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• pp.189-200
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• 2007

This paper addresses the localization and navigation problem in the movement of service robots by using invisible two dimensional barcodes on the floor. Compared with other methods using natural or artificial landmarks, the proposed localization method has great advantages in cost and appearance since the location of the robot is perfectly known using the barcode information after mapping is finished. We also propose a navigation algorithm which uses a topological structure. For the topological information, we define nodes and edges which are suitable for indoor navigation, especially for large area having multiple rooms, many walls, and many static obstacles. The proposed algorithm also has the advantage that errors which occur in each node are mutually independent and can be compensated exactly after some navigation using barcodes. Simulation and experimental results were performed to verify the algorithm in the barcode environment, showing excellent performance results. After mapping, it is also possible to solve the kidnapped robot problem and to generate paths using topological information.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.4
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• pp.386-392
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• 1990

This paper discribes a sonar-based certainty grid, the probabilistic representation of the uncertain and incomplete sensor knowledge, for autonomous mobile robot navigation. We use sonar sensor range data to build a map of the robot's surroundings. This range data provides information about the location of the objects which may exist in front of the sensor. From this information, we can compute the probability of being occupied and that of being empty for each cell. In this paper, a new method using Bayesian formula is introduced, which enables us to overcome some difficulties of the Ad-Hoc formula that has been the only way of updating the grids. This new formula can be applied to other kinds of sensors as well as sonar sensor. The validity of this formula in the real world is verified through simulation and experiment. This paper also shows that a wide angle sensor such as sonar sensor can be used effectively to identify the empty area, and the simultaneous use of multiple sensors and fusion in a certainty grid can improve the quality of the map.