• The Transactions of the Korean Institute of Power Electronics
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• v.18 no.1
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• pp.63-71
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• 2013

This paper describes the coordinated droop control method for stand-alone type DC micro-grid to improve reliability and utilization of distributed generations and energy storage. The stand-alone type DC micro-grid consists of several distributed generations such as a wind power generation, solar power and micro-turbine, and energy storage. The proposed method which is based on autonomous control method shows high reliability and stability through coordinated droop control of distributed generations and energy storage and also capability of battery management. The operation of stand-alone type DC micro-grid was analyzed using detail simulation model with PSCAD/EMTDC software. Based on simulation results, a hardware simulator was built and tested with commercially available components and performance of system was verified.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1177-1180
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• 1996

In this paper, we propose an algorithm that realizes cooperative behavior by construction of autonomous mobile robot system. Each robot is able to sense other robots and obstacles, and it has the rule of behavior to achieve the goal of the system. In this paper, to improve performance of the whole system, we use Genetic Programming based on Natural Selection. Genetic Programming's chromosome is a program of tree structure and it's major operators are crossover and mutation. We verify the effectiveness of the proposed scheme from the several examples.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1286-1290
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• 2004

This paper proposes a Distributed Autonomous Robotic System (DARS) based on an Artificial Immune Network (AIN) and a Classifier System (CS). The behaviors of robots in the system are divided into global behaviors and local behaviors. The global behaviors are actions to search tasks in environment. These actions are composed of two types: aggregation and dispersion. AIN decides one between these two actions, which robot should select and act on in the global. The local behaviors are actions to execute searched tasks. The robots learn the cooperative actions in these behaviors by the CS in the local. The relation between global and local increases the performance of system. Also, the proposed system is more adaptive than the existing system at the viewpoint that the robots learn and adapt the changing of tasks.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1044-1047
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• 2005

In this paper, we present the strategy of object search for distributed autonomous robotic systems (DARS). The DARS are the systems that consist of multiple autonomous robotic agents to whom required functions are distributed. For instance, the agents should recognize their surrounding at where they are located and generate some rules to act upon by themselves. In this paper, we introduce the strategy for multiple DARS robots to search a hidden object at the unknown area. First, we present an area-based action making process to determine the direction change of the robots during their maneuvers. Second, we also present Q learning adaptation to enhance the area-based action making process. Third, we introduce the coordinate system to represent a robot's current location. In the end of this paper, we show experimental results using hexagon-based Q learning to find the hidden object.

• The Journal of Korea Robotics Society
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• v.6 no.2
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• pp.97-107
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• 2011

This paper describes efficient flight control algorithms for building a reconfigurable ad-hoc wireless sensor networks between nodes on the ground and airborne nodes mounted on autonomous vehicles to increase the operational range of an aerial robot or the communication connectivity. Two autonomous flight control algorithms based on adaptive gradient climbing approach are developed to steer the aerial vehicles to reach optimal locations for the maximum communication throughputs in the airborne sensor networks. The first autonomous vehicle control algorithm is presented for seeking the source of a scalar signal by directly using the extremum-seeking based forward surge control approach with no position information of the aerial vehicle. The second flight control algorithm is developed with the angular rate command by integrating an adaptive gradient climbing technique which uses an on-line gradient estimator to identify the derivative of a performance cost function. They incorporate the network performance into the feedback path to mitigate interference and noise. A communication propagation model is used to predict the link quality of the communication connectivity between distributed nodes. Simulation study is conducted to evaluate the effectiveness of the proposed reconfigurable airborne wireless networking control algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3572-3593
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• 2015

This article investigates the topology control problem in the space information network (SIN) using a hierarchical autonomous system (AS) approach. We propose an AS network topology control (AS-TC) algorithm to minimize the time delay in the SIN. Compared with most existing approaches for SIN where either the purely centralized or the purely distributed control method is adopted, the proposed algorithm is a hybrid control method. In order to reduce the cost of control, the control message exchange is constrained among neighboring sub-AS networks. We prove that the proposed algorithm achieve logical k-connectivity on the condition that the original physical topology is k-connectivity. Simulation results validate the theoretic analysis and effectiveness of the AS-TC algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.5
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• pp.465-470
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• 2012

This paper proposes a distributed autonomous control method of swarm robot behavior strategy based on artificial immune system and an optimization strategy for artificial immune system. The behavior strategies of swarm robot in the system are depend on the task distribution in environment and we have to consider the dynamics of the system environment. In this paper, the behavior strategies divided into dispersion and aggregation. For applying to artificial immune system, an individual of swarm is regarded as a B-cell, each task distribution in environment as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. The executing process of proposed method is as follows: When the environmental condition changes, the agent selects an appropriate behavior strategy. And its behavior strategy is stimulated and suppressed by other agent using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. In order to decide more accurately select the behavior strategy, the optimized parameter learning procedure that is represented by stimulus function of antigen to antibody in artificial immune system is required. In this paper, particle swarm optimization algorithm is applied to this learning procedure. The proposed method shows more adaptive and robustness results than the existing system at the viewpoint that the swarm robots learning and adaptation degree associated with the changing of tasks.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.176-180
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• 1998

We may gaze at some peculiar scenes of flocking of birds and fishes. This paper demonstrates that multiple agent mobile robots show complex behaviors from efficient and strategic rules. The simulated flock are realized by a distributed behavioral model and each mobile robot decides its own motion as an individual which moves constantly by sensing the dynamic environment.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.312-317
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• 1992

This paper proposes a new framework of an autonomous and distributed flexible manufacturing system - Multi Client Robot Groups(MCR) - and describes a stochastic learning scheme applied to managerial problems of the system. The MCR is composed of groups of manufacturing robots, named Client Robots (CRs), which are capable of both versatility and independence in their performances. The MCR is expected to have high performance because the MCR can perform concurrent and corporative processing. However, the system performance is determined by the organizations of the CR groups. Therefore the treatment of the managerial problems and organizations of the system are important problems. In this paper, it is assumed that CR groups being able to processing tasks are selected stochastically based on the strengths of the robot groups. The learning scheme adjusting the strength is introduced to organize the groups in the system and control the each performance of the groups according to the total system performance. Finally, some experimental results of the learning scheme are shown.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.2
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• pp.14-20
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• 1999

In this paper, we discuss about implementation of a wireless communication system for a distributed autonomous robotic system. In order to achieve cooperative behavior among mobile robots, it is required to perform communication. Based on this requirements, we examined to the application in a wireless communication system used by mobile robots. This paper describes a conceptual and experimental framework which provides a distributed control architecture for the study of interactions between multiple mobile robots.