• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.428-430
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• 2001

다수의 자율이동로봇으로 구성되는 다개체 시스템에서의 협조행동을 위해서 각 개체는 주변환경의 인식뿐만 아니라 환경변화에 적응할 수 있는 추론능력이 요구된다. 이에 본 논문에서는 강화학습을 이용하여 동적으로 변화하는 환경 하에서 개체들이 스스로 학습하고 대처할 수 있는 협조행동 방법을 제시한다. 제안한 방법을 먹이와 포식자 문제에 적용하여 포식자 로봇간의 협조행동을 구현하였다. 여러 대로 구성된 포식자 로봇은 회피가 목적인 먹이로봇을 추적하여 포획하는 것이 임무이며 포식자 로봇들 간의 협조행동을 위해 각 상태에 따른 최적의 행동방식을 찾는데 강화학습을 이용한다.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1892-1893
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• 2011

본 논문에서는 Centroidal Voronoi Tessellation을 이용하여 군집로봇의 협조탐색을 위한 공간분할기법을 제안한다. 탐색공간은 Centroidal Voronoi Tessellation을 이용하여 분할한다. 전역 경로 계획 및 군집 로봇 간의 충돌 회피는 포텐셜 필드를 이용한다. 탐색공간에 밀도 함수를 사용하여 공간분할의 유동성을 부여한다. 마지막으로, 군집로봇의 협조탐색의 가능성을 시뮬레이션을 통하여 확인한다.

• Journal of the Korea Society for Simulation
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• v.15 no.3
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• pp.61-67
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• 2006

This paper provides a systematic way of integrating human intelligence and autonomous precision of robots to achieve the highest possible performance of a cooperating robot system. Adjustable autonomy, which deals with the combination of human and robotic skills, has the potential to bridge the gap which leaves many tasks suited to robotics beyond the reach of existing technology. Especially we will show that relevant human assistance or intervention will increase system performance by improving the exception handling capability, simplifying autonomous operation, and boosting speed and reliability. To support the usefulness of our scheme, a series of experiments were conducted with three cooperating robots which work together to dock both ends of a long suspended beam into stanchions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.7
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• pp.1616-1623
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• 2005

In this paper, we propose that the synchronization method for mutual cooperative control in the chaotic mobile robot. In order to achieve the synchronization for mutual cooperative control in the chaotic mobile robot, we apply coupled synchronization technique and driven synchronization technique in the chaotic mobile robot without obstacle and with obstacle.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2886-2888
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• 2000

본 논문은 다수의 이동로봇들의 협조운동을 위해서 퍼지알고리즘을 적용하였다. 그리고. 보다 정확한 정보획득을 위해서 슈퍼바이저 시스템의 적용을 제안한다. 제안된 알고리즘에 대한 효과는 등간격원 모양으로 로봇들이 정렬할 때까지의 시간으로 나타내었으며, 슈퍼바이저 시스템을 적용한 것과 그렇지 않은것에 대한 비교를 컴퓨터 모의실험으로 그 결과를 보였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.4
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• pp.8-17
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• 1998

Autonomous mobile robot agents(AMRAs) are robots which perform a given task by the same distributed self-organizing rules. In this paper, we let them form an equi-distance circle for the task of cooperative behaviors. Here, we suggest an algorithm to perform the taqk and then utilize a fuzzy system to improve the algorithm. It is shown that various cooperative activities appear in the simulation and particularly chaotic behaviors also appear by increasing the robot speed. Moreover, we prove the characteristics of this chaotic behaviors by calculating Lyapunov exponent.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.12
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• pp.1231-1237
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• 2014

This paper proposes a reduced-order disturbance observer based coordinated tracking controller for uncertain heterogeneous multi-agent systems. To this end, first the control problem is converted as a robust control problem. Then, a dynamic coordinated controller is designed based on the recently proposed reduced-order disturbance observer. Simulation results are given to show the effectiveness of the proposed control scheme.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.809-811
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• 2000

협조행동이 요구되는 다수의 자율이동로봇 시스템에서 각 개체는 주변환경의 인식뿐만 아니라 지속적인 환경변화에 적응할 수 있는 고도의 추론능력을 요구하고 있다. 이에 본 논문에서는 강화학습을 이용하여 동적으로 변화하는 환경에서 스스로 학습하여 대처할 수 있는 협조행동 방법을 제시하였다. 강화학습은 동물의 학습방법 연구에서 비롯되었으며, 주어진 목표를 수행하는 과정에서 개체의 행동이 목표를 성취하도록 하였을 때는 그 행동에 보상을 주어 환경의 상태에 따른 최적의 행동방법을 찾아내도록 학습하는 방법이다. 따라서 본 논문에서는 포식자들이 협조행동을 통하여 능동적으로 움직이는 먹이를 잡는 까다로운 문제에 제안한 방법을 적용하여 그 성능을 검증하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.72-78
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• 2006

It is essential for robot to have the sensing and communication abilities in the swarm robot system. In general, as the number of robot goes on increasing, the limitation of communication capacity and information overflow occur in global communication system. Therefore a local communication is more effective than global one. In this paper, we propose the novel method for determining the optimal communication radius through the analyzing of the information propagation based on local communication. And we also propose a method of cooperative strategies and group behavior of swarm robot based on artificial immune system.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.131-137
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• 2006

In swarm robot systems, each robot must behaves by itself according to the its states and environments, and if necessary, must cooperates with other robots in order to carry out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, the new learning and evolution method based on reinforcement learning having delayed reward ability and distributed genetic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. Reinforcement learning having delayed reward is still useful even though when there is no immediate reward. And by distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the performance of evolution, selective crossover using the characteristic of reinforcement learning is adopted in this paper. we verify the effectiveness of the proposed method by applying it to cooperative search problem.