• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.648-651
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• 1997

This paper proposes methods for the cooperative control of multiple mobile robots and constructs a robotic soccer system in which the cooperation will be implemented as a pass play of two robots. To play a soccer game, elementary actions such as shooting and moving have been designed, and Q-learning, which is one of the popular methods for reinforcement learning, is used to determine what actions to take. Through simulation, learning is successful in case of deliberate initial arrangements of ball and robots, thereby cooperative work can be accomplished.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.9
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• pp.1686-1691
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• 2010

In this paper, we aim at providing a novel sweeping method for multi-security mobile robots. The sweeping problem of the multi-robots can be modeled as the stick pulling problem in which the swarm robots should sweep unknown terrains in order to remove sticks collaboratively. For the purpose, we define a certain map, what is called stick map. And we suggest how to make swarm robots build up and utilize the map in order to improve the productivity of collaborative removing sticks. Finally, the efficiency of our algorithm is verified with simulation experiments.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.282-287
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• 2008

In distributed mobile robot systems, autonomous robots accomplish complicated tasks through intelligent cooperation with each other. This paper presents behavior learning and online distributed evolution for cooperative behavior of a group of autonomous robots. Learning and evolution capabilities are essential for a group of autonomous robots to adapt to unstructured environments. Behavior learning finds an optimal state-action mapping of a robot for a given operating condition. In behavior learning, a Q-learning algorithm is modified to handle delayed rewards in the distributed robot systems. A group of robots implements cooperative behaviors through communication with other robots. Individual robots improve the state-action mapping through online evolution with the crossover operator based on the Q-values and their update frequencies. A cooperative material search problem demonstrated the effectiveness of the proposed behavior learning and online distributed evolution method for implementing cooperative behavior of a group of autonomous mobile robots.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.10
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• pp.1047-1055
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• 2009

Since nuclear and chemical materials could damage people and disturb battlefield missions in a wide region, nuclear/chemical reconnaissance systems utilizing multiple mobile robots are highly desirable for rapid and safe reconnaissance. In this paper, we design a nuclear/chemical reconnaissance system including mobile robots. Also we propose time-efficient trajectory planning algorithms using grid coverage and contour finding methods for reconnaissance operation. For grid coverage, we performed in analysis on time consumption for various trajectory patterns generated by straight lines and arcs. We proposed BCF (Bounded Contour Finding) and BCFEP (Bounded Contour Finding with Ellipse Prediction) algorithms for contour finding. With these grid coverage and contour finding algorithms, we suggest trajectory planning algorithms for single, two or four mobile robots. Various simulations reveal that the proposed algorithms improve time-efficiency in nuclear/chemical reconnaissance missions in the given area. Also we conduct basic experiments using a commercial mobile robot and verify the time efficiency of the proposed contour finding algorithms.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.1
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• pp.66-71
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• 2006

Recently, there are lots of concerning on ubiquitous environment of robots and URC (Ubiquitous Robotic Companion). In this paper, a practical ubiquitous interface system far controlling mobile robots in indoor environments was proposed. The interface system was designed as a manager-agent model including a PC manager, a mobile manager, and robot agents for being able to be accessed by any network. In the system, the PC manager has a 3D virtual environment and shows real images for a human-friendly interface, and share the computation load of the robot such as path planning and managing geographical information. It also contains Hybrid Format Manager(HFM) working for transforming the image, position, and control data and interchanging them between the robots and the managers. Mobile manager working in the minimized computing condition of handsets has a mobile interface environment displaying the real images and the position of the robot and being able to control the robots by pressing keys. Experimental results showed the proposed system was able to control robots rising wired and wireless LAN and mobile Internet.

• Industrial Engineering and Management Systems
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• v.8 no.3
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• pp.181-193
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• 2009

This paper presents a framework for controlling mobile multiple robots connected by communication networks. This framework provides novel methods to control coordinated systems using mobile agents. The combination of the mobile agent and mobile multiple robots opens a new horizon of efficient use of mobile robot resources. Instead of physical movement of multiple robots, mobile software agents can migrate from one robot to another so that they can minimize energy consumption in aggregation. The imaginary application is making "carts," such as found in large airports, intelligent. Travelers pick up carts at designated points but leave them arbitrary places. It is a considerable task to re-collect them. It is, therefore, desirable that intelligent carts (intelligent robots) draw themselves together automatically. Simple implementation may be making each cart has a designated assembly point, and when they are free, automatically return to those points. It is easy to implement, but some carts have to travel very long way back to their own assembly point, even though it is located close to some other assembly points. It consumes too much unnecessary energy so that the carts have to have expensive batteries. In order to ameliorate the situation, we employ mobile software agents to locate robots scattered in a field, e.g. an airport, and make them autonomously determine their moving behaviors by using a clustering algorithm based on the Ant Colony Optimization (ACO). ACO is the swarm intelligence-based methods, and a multi-agent system that exploit artificial stigmergy for the solution of combinatorial optimization problems. Preliminary experiments have provided a favorable result. In this paper, we focus on the implementation of the controlling mechanism of the multi-robots using the mobile agents.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.4
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• pp.601-608
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• 2023

Mobile robots are widely used in industries because mobile robots perform tasks in various environments. In order to carry out tasks, determining the precise location of the robot in real-time is important due to the need for path generation and obstacle detection. In particular, when mobile robots autonomously navigate in indoor environments and carry out assigned tasks within pre-determined areas, highly precise positioning performance is required. However, mobile robots frequently experience data missing in wireless communication environments. The robots need to rely on predictive techniques to autonomously determine the mobile robot positions and continue performing mobile robot tasks. In this paper, we propose an extended Kalman filter-based algorithm to enhance the accuracy of mobile robot localization and address the issue of data missing. Trilateration algorithm relies on measurements taken at that moment, resulting in inaccurate localization performance. In contrast, the proposed algorithm uses residual values of predicted measurements in data missing environments, making precise mobile robot position estimation. We conducted simulations in terms of data missing to verify the superior performance of the proposed algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.5
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• pp.439-444
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• 2012

In this study, we address the formation control problem of coupled unicycle-type mobile robots, each of which can interact with its neighboring robots by communicating their position outputs. Each communication link between two mobile robots is assumed to be established according to the given time-varying interconnection topology that switches within a finite set of connected fixed undirected networks and has a non-vanishing dwell time. Under this setup, we propose a distributed formation control algorithm by using the dynamics extension and feedback linearization methods, and by employing a consensus algorithm for linear multi-agent systems which provides arbitrary fast convergence rate to the agreement of the multi-agent system. Finally, the proposed result is demonstrated through a computer simulation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.164-169
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• 1998

Complex "lifelike" behaviors are composed of local interactions of individuals under fundamental rules of artificial life. In this paper, fundamental rules for cooperative group behaviors, "flocking" and "arrangement", of multiple autonomous mobile robots are represented by a small number of fuzzy rules. Fuzzy rules in Sugeno type and their related paramenters are automatically generated from clustering input-output data obtained from the algorithms the group behaviors. Simulations demonstrate the fuzzy rules successfully realize group intelligence of mobile robots.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.266-268
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• 2005

Recently, the ability of sensing obstacles by oneself and creating suitable moving path in mobile robots are required to provide various kinds automation services. Therefore, in this paper, we studied the avoidance behavior of mobile robots from dynamic obstacles using potential function that minimizes distance and time. We examined the performance of the proposed algorithm by comparing the method of based on the geometrical experience in simulations.