• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.9
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• pp.782-791
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• 2013

This paper proposes the location estimation techniques of distributed targets with the multi-sensor data perceived through IR sensors of the military robots. In order to match up targets with measured azimuths, we apply the maximum likelihood (ML), depth-first, and breadth-first tree search algorithms, in which we use the measured azimuths and the number of pixels on IR screen for pruning branches and selecting candidates. After matching up targets with azimuths, we estimate the coordinate of each target by obtaining the intersection point of the azimuths with the least square error (LSE) algorithm. The experimental results show the probability of missing target, mean of the number of calculating nodes, and mean error of the estimated coordinates of the proposed algorithms.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.5
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• pp.303-310
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• 2017

Underwater robots operating in constrained underwater environment have requirements for software systems. Firstly, it is necessary to provide reusable common software components for hardware interface of sensors and actuators that are frequently used in underwater robots. Secondly, it is required to support distributed execution environment on multiple embedded controllers. Thirdly, it is need to implement a monitoring system capable of high-speed and large-data transmission for underwater robots operating in an environment where it is difficult to check the robot status. For these requirements, we have designed the layered architecture pattern and applied several design patterns to enhance the reusability and the maintainability of software components, In addition, we overlaid the broker architecture pattern to support distributed execution environments. Finally, we implemented the underwater robot software system using ROS framework based on the software architecture design. In order to evaluate the performance of the implemented software system, we performed an experiment to measure the response time between components and the transmission rate of the monitoring data, and obtained the results satisfying the required performance.

• Journal of KIISE
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• v.43 no.12
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• pp.1365-1375
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• 2016

One of the most important capabilities for autonomous service robots working in living environments is to recognize and understand the correct context in dynamically changing environment. To generate high-level context knowledge for decision-making from multiple sensory data streams, many technical problems such as multi-modal sensory data fusion, uncertainty handling, symbolic knowledge grounding, time dependency, dynamics, and time-constrained spatio-temporal reasoning should be solved. Considering these problems, this paper proposes an effective dynamic context management and spatio-temporal reasoning method for intelligent service robots. In order to guarantee efficient context management and reasoning, our algorithm was designed to generate low-level context knowledge reactively for every input sensory or perception data, while postponing high-level context knowledge generation until it was demanded by the decision-making module. When high-level context knowledge is demanded, it is derived through backward spatio-temporal reasoning. In experiments with Turtlebot using Kinect visual sensor, the dynamic context management and spatio-temporal reasoning system based on the proposed method showed high performance.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.4
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• pp.114-130
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• 2022

The purpose of this study was to investigate the possibility of increasing the efficiency of disaster relief rescue operations through collaboration among multiple aerial and ground robots. The robots create 2.5D maps, which are merged into a 2.5D map. The 2.5D map can be handled by a low-specification controller of an aerial robot and is suitable for ground robot navigation. For localization of the aerial robot, a six-degree-of-freedom pose recognition method using VIO was applied. To build a 2.5D map, an image conversion technique was employed. In addition, to merge 2.5D maps, an image similarity calculation technique based on the features on a wall was used. Localization and navigation were performed using a ground robot to evaluate the reliability of the 2.5D map. As a result, it was possible to estimate the location with an average and standard error of less than 0.3 m for the place where the 2.5D map was normally built, and there were only four collisions for the obstacle with the smallest volume. Based on the 2.5D map building and map merging system for the aerial robot used in this study, it is expected that disaster response work efficiency can be improved by combining the advantages of heterogeneous robots.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.3030-3038
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• 2023

Nowadays, mobile robots have been used to search for uncontrolled radioactive source in indoor environments to avoid radiation exposure for technicians. However, in the indoor environments, especially in the presence of obstacles, how to make the robots with limited sensing capabilities automatically search for the radioactive source remains a major challenge. Also, the source search efficiency of robots needs to be further improved to meet practical scenarios such as limited exploration time. This paper proposes an automatic source search strategy, abbreviated as ACA: the location of source is estimated by a convolutional neural network (CNN), and the path is planned by the A-star algorithm. First, the search area is represented as an occupancy grid map. Then, the radiation dose distribution of the radioactive source in the occupancy grid map is obtained by Monte Carlo (MC) method simulation, and multiple sets of radiation data are collected through the eight neighborhood self-avoiding random walk (ENSAW) algorithm as the radiation data set. Further, the radiation data set is fed into the designed CNN architecture to train the network model in advance. When the searcher enters the search area where the radioactive source exists, the location of source is estimated by the network model and the search path is planned by the A-star algorithm, and this process is iterated continuously until the searcher reaches the location of radioactive source. The experimental results show that the average number of radiometric measurements and the average number of moving steps of the ACA algorithm are only 2.1% and 33.2% of those of the gradient search (GS) algorithm in the indoor environment without obstacles. In the indoor environment shielded by concrete walls, the GS algorithm fails to search for the source, while the ACA algorithm successfully searches for the source with fewer moving steps and sparse radiometric data.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2519-2523
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• 2003

Ultrasonic sensors are widely used in mobile robot applications to recognize external environments, because they are cheap, easy to use, and robust under varying lighting conditions. In most cases, a single ultrasonic sensor is used to measure the distance to an object based on time-of-flight (TOF) information, whereas multiple sensors are used to recognize the shape of an object, such as a corner, plane, or edge. However, the conventional sequential driving technique involves a long measurement time. This problem can be resolved by pulse coding ultrasonic signals, which allows multi-sensors to be fired simultaneously and adjacent objects to be distinguished. Accordingly, the current presents a new simultaneous coded driving system for an ultrasonic sensor array for object recognition in autonomous mobile robots. The proposed system is designed and implemented using a DSP and FPGA. A micro-controller board is made using a DSP, Polaroid 6500 ranging modules are modified for firing the coded signals, and a 5-channel coded signal generating board is made using a FPGA. To verify the proposed method, experiments were conducted in an environment with overlapping signals, and the flight distances for each sensor were obtained from the received overlapping signals using correlations and conversion to a bipolar PCM-NRZ signal.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.280-285
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• 2011

Recently, with the development of service robots and the concept of ubiquitous, the position estimation of mobile objects has received great interest. Some of the localization schemes are introduced, which provide the relative location of the moving objects subjected to accumulated errors. To implement a real time localization system, a new absolute position estimation method for a mobile robot in indoor environment is proposed. Design and implementation of the localization system comes from the usage of active beacon systems (based upon RFID technology). The active beacon system is composed of an RFID receiver and an ultra-sonic transmitter. The RFID receiver gets the synchronization signal from the mobile robot and the ultra-sonic transmitter sends out the traveling signal to be used for measuring the distance. Position of a mobile robot in a three dimensional space can be calculated basically from the distance information from three beacons and the absolute position information of the beacons themselves. In some case, the mobile robot can acquire the ultrasonic signals from only one or two beacons, due to the obstacles located along the moving path. In this paper, a position estimation scheme using fewer than three sensors is developed. Also, the extended Kalman filter algorithm is applied for the improvement of position estimation accuracy of the mobile robot.

• The Journal of Korea Robotics Society
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• v.4 no.1
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• pp.25-33
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• 2009

One of the main problems of topological localization in a real indoor environment is variations in the environment caused by dynamic objects and changes in illumination. Another problem arises from the sense of topological localization itself. Thus, a robot must be able to recognize observations at slightly different positions and angles within a certain topological location as identical in terms of topological localization. In this paper, a possible solution to these problems is addressed in the domain of global topological localization for mobile robots, in which environments are represented by their visual appearance. Our approach is formulated on the basis of a probabilistic model called the Bayes filter. Here, marginalization of dynamics in the environment, marginalization of viewpoint changes in a topological location, and fusion of multiple visual features are employed to measure observations reliably, and action-based view transition model and action-associated topological map are used to predict the next state. We performed experiments to demonstrate the validity of our proposed approach among several standard approaches in the field of topological localization. The results clearly demonstrated the value of our approach.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.2
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• pp.154-160
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• 2006

Instantaneous reaction and intelligence are required for autonomous mobile robots to achieve multiple goals in the unpredictable and dynamic environments. Design of the appropriate control architecture and clear definitions of systems are needed to construct and control these robots. This research proposes the hybrid deliberative/reactive control architecture which consists of three layers and uses the method of software structure design. The highest layer, Deliberative Layer makes the overall run-time schedule for navigation and/or manipulation, and the middle layer, Task Execution Layer carries out various missions. The lowest layer, Reactive Layer enables a robot to react rapidly in the dynamic environment and controls the mechanical devices concurrently. This paper proposes independent system supervisors called Manager to reuse the modules so that the Manager supports common use of the system and multi-processing tasks. It is shown that the mobile robot based on the proposed control scheme can perform the basic navigation and cope with the dynamic obstacles reasonably well.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.249-254
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• 2001

Abstract In this paper, we propose a method of cooperative control based on artifical intelligent system in distributed autonomous robotic system. In general, multi-agent behavior algorithm is simple and effective for small number of robots. And multi-robot behavior control is a simple reactive navigation strategy by combining repulsion from obstacles with attraction to a goal. However when the number of robot goes on increasing, this becomes difficult to be realized because multi-robot behavior algorithm provide on multiple constraints and goals in mobile robot navigation problems. As the solution of above problem, we propose an architecture of fuzzy system for each multi-robot speed control and fuzzy-neural network for obstacle avoidance. Here, we propose an architecture of fuzzy system for each multi-robot speed control and fuzzy-neural network for their direction to avoid obstacle. Our focus is on system of cooperative autonomous robots in environment with obstacle. For simulation, we divide experiment into two method. One method is motor schema-based formation control in previous and the other method is proposed by this paper. Simulation results are given in an obstacle environment and in an dynamic environment.