• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.263-266
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• 1997

로봇 축구 경기를 위해서는 경기장의 임의의 시작점에서 목표점으로 장애물을 피해 갈 수 있는 능력이 필요하다. 이러한 경로 계획을 학습하기 위해서 다양한 상황을 모두 고려할 경우 학습량이 급격히 증가한다. 그러나 많은 실제적인 학습 문제에 있어서는 가능한 모든 학습 데이터를 사용하지 않고도 원하는 학습 효과를 가져올 수 있음이 알려져 있으며, 이러한 경우 데이터를 스스로 선별하여 학습하는 능동적 학습 방법이 효과적이다. 본 논문에서는 진화 알고리즘을 사용하여 실시간에 경로 계획을 하기 위한 새로운 능동적 학습 방법을 제시한다. 제안되는 방법은 두 개의 진화 알고리즘으로 구성되는데 하나는 주어진 시작점-목표점간의 최적 경로를 찾는데 사용되고 또 다른 하나의 진화 알고리즘은 유용한 시작점-목표점들의 쌍을 탐색하는데 사용된다. 이 방법은 계산 시간의 여유가 있을 때 다양한 문제를 스스로 제시하고 해결하는 법을 학습해 놓고 후에 실제 문제가 주어질 때 기존의 문제와 가장 유사한 문제를 찾아 실시간에 해결함으로써 기존의 진화 알고리즘에 의한 경로 계획법들이 갖는 실시간성에서의 단점을 개선할 수 있다. 실험을 통하\ulcorner 제안된 두 가지 진화 알고리즘의 성능을 실험적으로 검토한다.

• Journal of the Korea Society for Simulation
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• v.27 no.2
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• pp.101-114
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• 2018

Several different approaches and mechanisms are introduced to solve the UAV path planning problem. In this paper, we designed and implemented an agent-based simulation software using the Repast platform and Java Genetic Algorithm Package to examine an evolutionary path planning method by implementing and testing within the Repast environment. The paper demonstrates the life-cycle of an agent-based simulation software engineering project while providing a documentation strategy that allows specifying autonomous, adaptive, and interactive software entities in a Multi-Agent System. The study demonstrates how evolutionary path planning can be introduced to improve cognitive agent capabilities within an agent-based simulation environment.

• Journal of Advanced Navigation Technology
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• v.13 no.5
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• pp.646-655
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• 2009

This paper presents a new search strategy based on models of evolution in order to solve the problem of collision-free robotic path planning. We designed the robot path planning method with genetic algorithm which has become a well-known technique for optimization, intelligent search. Considering the path points as genes in a chromosome will provide a number of possible solutions on a given map. In this case, path distances that each chromosome creates can be regarded as a fitness measure for the corresponding chromosome. The effectiveness of the proposed genetic algorithm in the path planning was demonstrated by simulation. The proposed search strategy is able to use multiple and static obstacles.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.341-342
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• 2008

GA(Genetic Algorithm)는 NP-Complete 도메인이나 NP-Hard 도메인 내의 문제들에 대해서 최적의 해를 찾기 위해서 많이 사용되어 지는 진화 컴퓨팅 방법 중 하나이다. 모바일 로봇 기술 중 경로계획은 NP-Complete 도메인 영역의 문제 중 하나로 이를 해결하기 위해서 Dijkstra 등의 그래프 이론을 이용한 연구가 많이 연구되었고 최근에는 GA등 진화 컴퓨팅 기법을 이용하여 최적의 경로를 찾는 연구가 많이 수행되고 있다. 그러나 모바일 로봇이 처리해야 될 공간 정보 크기가 증가함에 따라 기존 GA의 개체의 크기가 증가되어 게산 복잡도가 높아져 시간 지연등의 문제가 발생할 수 있다. 이는 모바일 로봇의 잠재적 오류로 발생될 수 있다. 공간 정보에는 동적이 장애물들이 예측 불허하게 나타 날 수 있는데 이것은 전역 경로 계획을 수립할 때 또한 반영되어야 된다. 본 논문에서는 k-means 클러스터링 기법을 이용하여 장애물 밀집도 및 거리 정보를 기반으로 공간정보를 k개의 군집 공간으로 재분류하여 이를 기반으로 N*M개의 그리드 개체 집단을 생성하여 최적 경로계획을 수립하는 GA를 제시한다.

• Journal of the Economic Geographical Society of Korea
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• v.17 no.2
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• pp.249-263
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• 2014

Established as the first science park in the world in the late 1950's, the Research Triangle Park(RTP) has not jut grown significantly but also has been successful in the transition from the exogenous development model to the endogenous development model. In this context, this paper attempts to explore the evolutionary path of the RTP by drawing upon the concept of triple-helix spaces of regional innovation. Firstly, the three research universities in the triangle area, as a knowledge space, played a fundamental role for forming the RTP. However, it is difficult to say that the regional universities, as opposed to the Silicon Valley and the Boston area, have had a significant impact on inducing the dynamics of the cluster evolution and the triple helix spaces. Secondly, it can be argued that the North Carolina's Board of Science and Technology, which was formed in 1961 but traced back to the 1950's in its origin, has been a centerpiece of a consensus space that makes a contribution to creating, sustaining and transforming the RTP as a triple-helix-based innovation cluster. Thirdly, there have been a plenty of agents to be an innovation space in the RTP. Particularly, the North Carolina Biotechnology Center(NCBC) and the Microelectronic Center of North Carolina(MCNC) have been the boundary permeable agents to make triple-helix agents interact. Today, the RTP has the triple-helix spaces with the structure that a consensus spaces is centered on out of the three, but all of those are inter-connected and influenced by each other. It can be claimed that the RTP today shows the dynamic structure of cluster evolution in a way in which the existing industry sectors have adapted to the changes in external environment and the new industry sectors have emerged at the same time.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.12
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• pp.1321-1330
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• 2012

This paper describes an advanced formation algorithm of clustered multiple robots for their navigation using flexible formation method for collision avoidance under static environment like narrow corridors. A group of clustered multiple robots finds the lowest path cost for navigation by changing its formation. The suggested flexible method of formation transforms the basic group of mobile robots into specific form when it is confronted by particular geographic feature. In addition, the proposed method suggests to choose a leader robot of the group for the obstacle avoidance and path planning. Firstly, the group of robots forms basic shapes such as triangle, square, pentagon and etc. depending on number of robots. Secondly, the closest to the target location robot is chosen as a leader robot. The chosen leader robot uses $A^*$ for reaching the goal location. The proposed approach improves autonomous formation characteristics and performance of all system.

• Korean Chemical Engineering Research
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• v.56 no.6
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• pp.804-810
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• 2018

Most biofuel companies are in a small scale with short experience of operating the entire supply chain. In order to compete with existing fossil fuel competitors, renewable companies should be more responsive to demand. It is financially important to reflect this in the decision supporting system of the company. This paper addresses an evolutionary algorithm based methodology for the distribution problem of renewable energies. A numerical example was presented to illustrate the applicability of the proposed methodology with some remarks.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.6
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• pp.831-836
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• 2010

In real system application, the path planning and following system for the coordinated control of heterogeneous marine robots based on the underwater acoustic communication has the following problems: surface and underwater robots have different maneuvering properties, an underwater robot requires more effective operating, it has a limited communication range because of the transmission loss (TL) of acoustic wave, it has a communication error because of the Doppler distortion of acoustic wave, and further, it requires an easy design procedure in terms of its structures and parameters. To solve these problems, an intelligent path planning algorithm using the evolution strategy (ES) and the fuzzy logic controller (FLC) based on system modeling, is proposed. To verify the performance of the proposed algorithm, the path planning and following of an underwater robot is performed according to the maneuvering of a surface robot. Simulation results show that the proposed algorithm effectively solves the problems.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.166-169
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• 2003

일정한 시간이 지나면 제품으로서의 가치가 사라지게 되는 단일기간 재고품목들은 생산된 직후 전량 각 고객들에게 주어진 납기에 맞추어 효율적인 분배가 요구된다. 본 연구에서는 고객들은 다수 종류의 제품을 주문할 수 있으며 제품종류별 분리배송을 허용하는 상황에서 생산비, 수송비, 납기위반비, 차량고정비를 최소화하기 위한 생산순서 및 차량경로를 수립함을 목적으로 한다. 이에 대한 해법으로써 진화개미해법을 개발하였다. 개발된 해법의 성능평가를 위해 각 고객의 위치, 주문 제품 종류, 주문량들을 다르게 하여 구축한 실험문제에 대하여 유전알고리듬해법과 비교실험을 수행하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.2
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• pp.397-406
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• 2000

Genetic algorithm is used as a means of search, optimization md machine learning, its structure is simple but it is applied to various areas. And it is about an active and effective controller which can flexibly prepare for changeable circumstances. For this study, research about an action base system evolving by itself is also being considered. There is to have a problem that depended entirely on heuristic knowledge of expert forming membership function and control rule for fuzzy controller design. In this paper, for forming the fuzzy control to perform self-organization, we tuned the membership function to the most optimal using a genetic algorithm(GA) and improved the control efficiency by the self-correction and generation of control rules.