• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.2 no.3
• /
• pp.242-247
• /
• 2002

According to increase of the factory-automation(FA) in the field of production, the importance of the autonomous guided vehicle's(AGV) role has also increased. This paper is about an active and effective controller which can flexibly prepare for changeable circumstances. For this study, research about an behavior-based system evolving by itself is also being considered. In this Paper, we constructed an active and effective AGV fuzzy controller to be able to carry out self-organization. To construct it, we tuned suboptimally membership function using a genetic algorithm(GA) and improved the control efficiency by self-correction and the generation of control rules.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.10 no.3
• /
• pp.210-217
• /
• 2010

This paper presents integrated path planning and collision avoidance for an omni-directional mobile robot. In this scheme, the autonomous mobile robot finds the shortest path by the descendent gradient of a navigation function to reach a goal. In doing so, the robot based on the proposed approach attempts to overcome some of the typical problems that may pose to the conventional robot navigation. In particular, this paper presents a set of analysis for an omni-directional mobile robot to avoid trapped situations for two representative scenarios: 1) Ushaped deep narrow obstacle and 2) narrow passage problem between two obstacles. The proposed navigation scheme eliminates the nonfeasible area for the two cases by the help of the descendent gradient of the navigation function and the characteristics of an omni-directional mobile robot. The simulation results show that the proposed navigation scheme can effectively construct a path-planning system in the capability of reaching a goal and avoiding obstacles despite possible trapped situations under uncertain world knowledge.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.104-104
• /
• 2000

Among durability tests, the accelerated durability test has been widely used to evaluate the durability of vehicle structure and chassis pans in a shon period of time on the designed road which has severe surface conditions. However it increases the drivers fatigue mainly caused by the severe driving conditions. The drivers difficulty of maintaining constant speed and controlling the steering wheel reduces the reliability of test results. The durability test includes the position and distance sensing system for the recognition of the absolute and relative driving position, the driving control system for the control of whole driving circumstance, the emergency system for responding to system errors. AGVDS (Autonomous Guided Vehicle Driving System) was Proved to facilitate the development of now car projects. Therefore the AGVDS we propose will help make the fundamentals for all future traffic systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.03a
• /
• pp.127-130
• /
• 1998

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. 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 cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-call 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 stimulated and suppressed by other robot using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based of clonal selection and idiotopic network hypothesis. And it is used for decision making of optimal swarm strategy. By T-cell modeling, adaptation ability of robot is enhanced in dynamic environments.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2000.05a
• /
• pp.175-178
• /
• 2000

생명체는 자신을 이루고 있는 단순한 구성 요소들이 적은 수의 근본 규칙들에 의하여 국부적 상호작용을 함으로써 복잡한 생명 현상을 보이고있다 이 연구에서는 생명 현상을 보이고 있는 개체가 많은 수의 단순한 구성 요소들의 집합으로 이루어져 있으며 그러한 구성 요소들이 적은 수의 근본 규칙들에 의하여 서로 국부적인 상호작용을 함으로써 복잡한 행동패턴들을 나타낸다는 가정 아래, 여러 대의 자율이동로봇(autonomous mobile robot)들의 군지능을 나타낼 수 있는 적은 수의 근본 규칙을 찾아내고 찾아진 근본규칙들을 퍼지규칙으로 표현하는 것을 목표로 한다. 각 자율이동로봇은 기능이 매우 제한되어 있으며, 자신만의 독특한 신호를 발생한다. 이 신호를 "heartbeat"이라 부르며 이를 이용하여 대략적으로 자신의 위치와 현재상태를 다른 개체에게 알리는 역할을 한다 이 논문에서는 "heartbeat"을 이용한 로봇간의 통신과 자재반송이라는 군행동을 퍼지시스템으로 구현하고 이를 평가한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.1254-1257
• /
• 1993

Presented in this paper is a newly developed motion planning method of an autonomous mobile robot(MAR) which can be applied to flexible manufacturing systems(FMS). The mobile robot is designed for transporting tools and workpieces between a set-up station and machines according to production schedules of the whole FMS. The proposed method is implemented based on an earlier developed real-time obstacle avoidance method which employs Kohonen network for pattern classification of sonar readings and fuzzy logic for local path planning. Particulary, a novel obstacle avoidance method for moving objects using a collision index, collision possibility measure, is described. Our method has been tested on the SNU mobile robot. The experimental results show that the robot successfully navigates to its target while avoiding moving objects.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2002.12a
• /
• pp.89-92
• /
• 2002

최근 들어 급증하고 있는 해양 충돌 사고 증가의 원인은 선박을 조종하는 항해사의 잘못된 판단 에 의한 부주의가 대부분이다. 이러한 문제를 해결하기 위한 가장 적극적인 방법은 선박에 자동화 및 지능화를 부여하여 항해사의 실수를 최소화하는 것이다. 대표적인 연구는 선박의 자율운항시스템(autonomous navigation system)이 있는데, 이는 선박운항에 있어 항해계획을 수립하고 현재의 선박의 상태를 파악하여 선박을 적절히 제어하는 항해 전문가시스템이다. 선박 자율운항시스템은 실세계의 선박에 장착되어 실험하여야하나, 선박은 고가의 운송수단이고, 자율운항시스템을 장착하기 위한 하부장치 인터페이스를 설계 및 구현에 많은 시간이 소요되므로 실제 선박을 모방하는 선박시뮬레이터를 이용하는 방법이 타당하다. 선박시뮬레이터는 선박의 물리적 운항특성을 모방하는 선박운동시뮬레이터와 선박 운항 주변에 변화하는 장애물을 시뮬레이터 하는 주변 객체시뮬레이터로 구성된다. 본 연구에서는 선박 운항 주변에 등장하는 장애물 변화를 시뮬레이션하고, 이에 기반한 ARPA RADAR를 모의 가동하는 주변객체시뮬레이터를 개발한다.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.6 no.4
• /
• pp.314-320
• /
• 2006

Navigation in environments that are densely cluttered with obstacles is still a challenge for Autonomous Ground Vehicles (AGVs), especially when the configuration of obstacles is not known a priori. Reactive local navigation schemes that tightly couple the robot actions to the sensor information have proved to be effective in these environments, and because of the environmental uncertainties, STSF(Space and Time Sensor Fusion)-based fuzzy behavior systems have been proposed. Realization of autonomous behavior in mobile robots, using STSF control based on spatial data fusion, requires formulation of rules which are collectively responsible for necessary levels of intelligence. This collection of rules can be conveniently decomposed and efficiently implemented as a hierarchy of fuzzy-behaviors. This paper describes how this can be done using a behavior-based architecture. The approach is motivated by ethological models which suggest hierarchical organizations of behavior. Experimental results show that the proposed method can smoothly and effectively guide a robot through cluttered environments such as dense forests.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2006.11a
• /
• pp.123-126
• /
• 2006

본 논문은 자율주행차량과 로봇의 안내를 위한 자계위치인식시스템을 제안한다. 자계위치인식시스템은 자성체로부터 발생되는 자계를 측정하여 위치를 인식한다. 이러한 자계위치인식시스템에서 지구자계는 기본적인 왜란으로 작용한다. 본 논문에서는 지구자계의 영향을 제거하기 위해서 다수의 1축 자계센서 열을 구성하였으며, 자계센서 출력의 선형구간을 이용하여 정밀한 위치인식시스템을 개발하였다. 본 논문에서 제안하는 자계위치인식시스템은 실험을 통하여 그 실용성을 검증하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.7 no.3
• /
• pp.176-181
• /
• 2007

This paper presents evolvable neural networks based on a developmental model for navigation control of autonomous mobile robots in dynamic operating environments. Bio-inspired mechanisms have been applied to autonomous design of artificial neural networks for solving practical problems. The proposed neural network architecture is grown from an initial developmental model by a set of production rules of the L-system that are represented by the DNA coding. The L-system is based on parallel rewriting mechanism motivated by the growth models of plants. DNA coding gives an effective method of expressing general production rules. Experiments show that the evolvable neural network designed by the production rules of the L-system develops into a controller for mobile robot navigation to avoid collisions with the obstacles.