• International Journal of Control, Automation, and Systems
• /
• 제6권2호
• /
• pp.253-262
• /
• 2008

This paper presents a framework for decentralized control of self-organizing swarm systems based on the artificial potential functions (APFs). The framework explores the benefits by associating agents based on position information to realize complex swarming behaviors. A key development is the introduction of a set of association rules by APFs that effectively deal with a host of swarming issues such as flexible and agile formation. In this scheme, multiple agents in a swarm self-organize to flock and achieve formation control through attractive and repulsive forces among themselves using APFs. In particular, this paper presents an association rule for swarming that requires less movement for each agent and compact formation among agents. Extensive simulations are presented to illustrate the viability of the proposed framework.

• International Journal of Control, Automation, and Systems
• /
• 제1권3호
• /
• pp.301-307
• /
• 2003

A control system design based on coupled nonlinear oscillators (CNOs) for a self- organized swarm system is presented. In this scheme, agents self-organize to flock and arrange group formations through attractive and repulsive forces among themselves using CNOs. Virtual agents are also used to create richer group formation patterns. The objective of the swarm control in this paper is to follow a moving target with a final group formation in the shortest possible time despite some obstacles. The simulation results have shown that the proposed scheme can effectively construct a self-organized multi-agent swarm system capable of group formation and group immigration despite the emergence of obstacles.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2003년도 ICCAS
• /
• pp.2475-2480
• /
• 2003

A control system design based on coupled nonlinear oscillators (CNOs) for a self-organized swarm system is presented. In this scheme, agents self-organize to flock and arrange group formations through attractive and repulsive forces among themselves using CNOs. Virtual agents are also used to create richer group formation patterns. The objective of the swarm control in this paper is to follow a moving target with a final group formation in the shortest possible time despite some obstacles. The simulation results have shown that the proposed scheme can effectively construct a self-organized multi-agent swarm system capable of group formation and group immigration despite the emergence of obstacles.

• International Journal of Control, Automation, and Systems
• /
• 제2권3호
• /
• pp.333-342
• /
• 2004

This paper presents a framework for the self-organization of swarm systems based on coupled nonlinear oscillators (CNOs). In this scheme, multiple agents in a swarm self-organize to flock and arrange themselves as a group using CNOs, which are able to keep a certain distance by the attractive and repulsive forces among different agents. A theoretical approach of flocking behavior by CNOs and a design guideline of CNO parameters are proposed. Finally, the formation scenario for cooperative multi-agent groups is investigated to demonstrate group behaviors such as aggregation, migration, homing and so on. The task for each group in this scenario is to perform a series of processes such as gathering into a whole group or splitting into two groups, and then to return to the base while avoiding collision with agents in different groups and maintaining the formation of each group.

• Advances in robotics research
• /
• 제2권1호
• /
• pp.1-11
• /
• 2018

To monitor the environment and determine the source of a pollutant gradient using a multiple robot swarm, we propose a hybrid algorithm that combines two bio-inspired algorithms mimicking chemotaxis and pheromones of bacteria. The algorithm is implemented in virtual robot agents in a simulator to evaluate their feasibility and efficiency in gradient maps with different sizes. Simulation results show that the chemotaxis controller guided robot agents to the locations with higher pollutant concentrations, while the pheromone marked in a virtual field increased the efficiency of the search by reducing the visiting redundancy. The number of steps required to reach the target point did not increase proportionally as the map size increased, but were less than those in the linear whole-map search method. Furthermore, the robot agents could function with simple sensor composition, minimum information about the map, and low calculation capacity.

• 한국콘텐츠학회논문지
• /
• 제14권11호
• /
• pp.549-557
• /
• 2014

근래에 게임이론 분야에서 진화계산법을 사용한 교섭게임 분석은 중요한 이슈 중에 하나이다. 본 논문에서는 이질적인 두 인공 에이전트 간의 공진화를 활용하여 교섭게임을 관찰한다. 두 인공 에이전트를 모델링하기 위해 사용된 전략은 진화전략의 종류인 입자군집최적화와 차분진화알고리즘이다. 교섭게임에서 각 전략이 최선의 결과를 얻기 위한 알고리즘 모수들을 조사하고 두 전략의 공진화를 관찰하여 어느 알고리즘이 교섭게임에 더 우수한지 관찰한다. 컴퓨터 시뮬레이션 실험 결과 입자군집최적화 전략이 차분진화알고리즘 전략보다 교섭게임에서 더 우수한 성능을 보임을 확인하였다.

• 한국멀티미디어학회논문지
• /
• 제6권2호
• /
• pp.332-339
• /
• 2003

경험적 탐색(Modem Heuristics) 방법을 이용하여 복잡한 문제들의 근사해를 구하는 것이 가능하여졌다. 최근 제시된 Particle Swarm Optimization은 경험적 탐색 방법중의 하나로써 조류나 어류 등의 생물의 무리가 각각의 개체가 가지고 있는 정보를 공유해가며 먹이를 찾아가는 과정을 모의한 것이다. 그러나, 다양한 문제들의 근사해를 구하기 위해 Particle Swarm Optimization 방법을 이용하여 왔지만 해를 탐색하는 과정을 보여주기 위한 시도는 이루어지지 않았다. 본 논문에서는 Particle Swarm Optimization의 탐색과정을 가시화 하는 것을 목적으로 한다. 가시화 하는 작업을 통해 그 탐색 능력을 시각적으로 파악하는 것이 가능하며 해결방법에 관한 이해를 돕고 교육적 효과도 기대 가능하다.

• Advances in aircraft and spacecraft science
• /
• 제1권2호
• /
• pp.219-232
• /
• 2014

This paper reports on an ongoing study investigating the feasibility of using an evolutionary method to develop the rules governing Self-Organised (SO) systems for use in swarms of unmanned aerial vehicles. In general, it is difficult to design swarm systems that follow explicit global behaviour. Unlike optimising a predefined objective function, the solution to the problem is the emergent behaviour in the SO systems which results from simultaneous interactions among agents and between agents and their environment. In this study, evolutionary algorithms are used to investigate their control and effectiveness in synthesising the weighting of different rules on SO emergent behaviour. Both homogeneous swarms and heterogeneous swarms were considered though the results provided are for a case study investigating the simplest problem a homogeneous swarm without mutation. Though simple this study does indicate the potential of the approach.

• 한국군사과학기술학회지
• /
• 제26권4호
• /
• pp.361-370
• /
• 2023

Swarm has recently become a critical component of offensive and defensive systems. Multi-agent reinforcement learning(MARL) empowers swarm systems to handle a wide range of scenarios. However, the main challenge lies in MARL's scalability issue - as the number of agents increases, the performance of the learning decreases. In this study, transfer learning is applied to advanced MARL algorithm to resolve the scalability issue. Validation results show that the training efficiency has significantly improved, reducing computational time by 31 %.

• Industrial Engineering and Management Systems
• /
• 제8권3호
• /
• pp.181-193
• /
• 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.