• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.1
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• pp.80-85
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• 2007

As home network system begins serving in earnest, the recent fruits of research in home service robot show that the new epoch that human and intelligent robots are living, communicating and interacting together at home, may come true in the near future. In the other hand, it is generally known that the multiagent system, performing distributed process together with other different devices in a home network system, is better than single robot or single home server for adapting themselves to home environment and completing their mission because the characteristic of home environment is 'open'. Therefore, in this paper we suggest the framework model to define agents, which is needed lot the home with a home network system, and the communication protocol architecture between agents. For this, we focus our attention on an agent comprising the set of many agent instances rather than the single intelligent or ability of a robot or home server, and also suggest the way of adaptation for agent systems to their environments and interaction with human in the manner of cooperation and negotiation among agents or agent instances in each agent.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.6
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• pp.627-633
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• 1999

In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on immune system in distributed autonomous robotic system (DARS). Immune system is living body's self-protection and self-maintenance system. These features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For applying immune system to DARS, a robot is regarded as a ？3-cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. When the environmental condition (antigen) changes, a robot selects an appropriate behavior strategy (antibody). And its behavior strategy is stimulated and suppressed by other robot using communication (immune network). Finally much stimulated strateby is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and immune network hypothesis, and it is used for decision making of optimal swarm strategy. Adaptation ability of robot is enhanced by adding T-cell model as a control parameter in dynamic environments.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.8
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• pp.590-596
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• 2016

Biopsy is a typical needle type intervention procedure performed under radiographic image equipment such as computed tomography (CT) and cone-beam CT. This minimal invasive procedure is a simple and effective way for identifying cancerous condition of a suspicious tissue but radiation exposure for the patients and interventional radiologists is a critical problem. In order to overcome such trouble and improve accuracy in targeting of the needle, there have been various attempts using robot technology. Those devices and systems, however, are not for full procedure automation in biopsy without consideration for tissue sampling task. A robotic end-effector of a master-slave tele-operated needle type intervention robot system has been proposed to perform entire biopsy procedure by the authors. However, motorized sampling adopted in the device has different cutting speed from that of biopsy guns used in the conventional way. This paper presents the design of a novel robotic mechanism and protocol for the automation of biopsy procedure using spring-triggered biopsy gun mechanism. An experimental prototype has been successfully fabricated and shown its feasibility of the automated biopsy sequence.

• ETRI Journal
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• v.29 no.2
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• pp.169-178
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• 2007

With the advent of ubiquitous computing environments, it has become increasingly important for applications to take full advantage of contextual information, such as the user's location, to offer greater services to the user without any explicit requests. In this paper, we propose context-aware active services based on context-aware middleware for URC systems (CAMUS). The CAMUS is a middleware that provides context-aware applications with a development and execution methodology. Accordingly, the applications based on CAMUS respond in a timely fashion to contextual information. This paper presents the system architecture of CAMUS and illustrates the content recommendation and control service agents with the properties, operations, and tasks for context-aware active services. To evaluate CAMUS, we apply the proposed active services to a TV application domain. We implement and experiment with a TV content recommendation service agent, a control service agent, and TV tasks based on CAMUS. The implemented content recommendation service agent divides the user's preferences into common and specific models to apply other recommendations and applications easily, including the TV content recommendations.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.243-246
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• 1996

Modular robot manipulator is a robotic system assembled from discrete joints and links into one of many possible manipulator configurations. This paper describes the design method of newly developed modular robot manipulator and the methodology of a task based reconfiguration of it. New locking mechanism is proposed and it provides quick coupling and decoupling. A parallel connection method is devised and it makes modular robot manipulator working well and the number of components on each module reduced. To automatically determine a sufficient or optimal arrangement of the modules for a given task, we also devise an algorithm that automatically generates forward and inverse manipulator kinematics, and we propose an algorithm which maps task specifications to the optimized manipulator configurations. Efficient genetic algorithms are generated and used to search for a optimal manipulator from task specifications. A few of design examples are shown.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.803-806
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• 1997

In this paper, a force estimation method is proposed for the sensorless force control. For this, a disturbance observer is applied to each joint of an n degrees of freedom manipulator to obtain a simple equivalent robot dynamics(SERD) being represented as an n independent double integrator system. To estimate the output of disturbance observer in the absence of external force, the observer estimator is designed, where the uncertain parameters of the robot manipulator are adjusted by gradient method to minimize the output between the disturbance observer and the observer estimator. When the external force is exerted, the external force is estimated using the difference between the output of disturbance observer which include the external torque signal and that of observer estimator. And then, a force controller is designed for force feedback control employing the estimated force signal. To verify the effectiveness of the proposed force estimation method, several numerical examples are illustrated for the 2-axis planar type robot manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.124-127
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• 1997

In this paper, we propose a method of cooperative control based on immune system in distributed autonomous robotic system(DARS). Immune system is living body's self-protection and self-maintenance system. Thus these features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For the purpose of applying immune system to DARS, a robot is regarded as a B lymphocyte(B cell), each environmental condition as an antigen, and a behavior strategy as an antibody respectively. The executing process of proposed method is as follows. When the environmental condition changes, a robot selects an appropriate behavior strategy. And its behavior strategy is simulated and suppressed by other robot using communication. Finally much simulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and idiotopic network hypothesis. And it is used for decision making of optimal swarm strategy.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.143-146
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• 1997

In this paper, we propose a fuzzy-supervised nonlinear H$_{\infty}$ controller which guarantees the robustness and has exact tracking performance for robot manipulator with system parameter uncertainty and exogenous disturbance, The proposed controller which is based on robotic H$_{\infty}$ controller has fuzzy supervisor which decides the optimal control input weighting value through fuzzy making-decision process. Owing to the fuzzy supervisor, The proposed controller can take the optimal control input. Then, we will apply the proposed controller to rigid robot manipulator to verify the performance of our controller.r.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.227-230
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• 1997

In this paper, robot navigation experiments with a new navigation algorithm are carried out in real environments. The authors already proposed a reactive navigation algorithm for mobile robots using optimal via-point selection method. At each sampling time, a number of via-point candidates is constructed with various candidates of heading angles and velocities. The robot detects surrounding obstacles, and the proposed algorithm utilizes fuzzy multi-attribute decision making in selecting the optimal via-point the robot would proceed at next step. Fuzzy decision making allows the robot to choose the most qualified via-point even when the two navigation goals-obstacle avoidance and target point reaching-conflict each other. The experimental result shows the successful navigation can be achieved with the proposed navigation algorithm for real environments.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.552-555
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• 1997

Recently, increasing attention has been paid to the application of learning control method to robot manipulator control. Because the learning control method does not require an exact dynamic model, it is flexible and easy to implement. In this paper, we implement a learning control scheme which consists of a unique feedforward learning controller and a linear feedback controller. The learning control method does not require acceleration terms that are sensitive to noise and has the capability of rejecting unknown disturbances and adapting itself to time-varying system parameters. The feasibility of the learning control scheme is soon by implementing the control scheme to a commercial robot manipulator and the performance of which is also compared with the conventional linear PID control method.