• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.282-287
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• 2008

In distributed mobile robot systems, autonomous robots accomplish complicated tasks through intelligent cooperation with each other. This paper presents behavior learning and online distributed evolution for cooperative behavior of a group of autonomous robots. Learning and evolution capabilities are essential for a group of autonomous robots to adapt to unstructured environments. Behavior learning finds an optimal state-action mapping of a robot for a given operating condition. In behavior learning, a Q-learning algorithm is modified to handle delayed rewards in the distributed robot systems. A group of robots implements cooperative behaviors through communication with other robots. Individual robots improve the state-action mapping through online evolution with the crossover operator based on the Q-values and their update frequencies. A cooperative material search problem demonstrated the effectiveness of the proposed behavior learning and online distributed evolution method for implementing cooperative behavior of a group of autonomous mobile robots.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.11
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• pp.522-531
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• 2001

Recently conventional artificial intelligence(AI) approaches have been employed to build action selectors for the autonomous mobile robot(AMR). However, in these approaches, the decision making process to choose an action from multiple competence modules is still an open question. Many researches have been focused on the reactive planning systems such as the biological immune system. In this paper, we attempt to construct an action selector for an AMR based on the artificial immune network and internet. The information from vision sensors is used for antibody. We propose a learning method for artificial immune network using evolutionary algorithm to produce antibody automatically. The internet environment for an AMR action selector shows the usefulness of the proposed learning artificial immune network application.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2953-2955
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• 1999

In this paper, the concept of evolvable hardware and evolutionary robotics are introduced and constructing the mobile robot controller without human operator is suggested. The robot controller is evolved to avoid obstacles by genetic learning which determines the weights between sensor inputs and motor outputs. Genetic algorithms which is executed in a computer(PC) searches the best weights by interacting with robot performance under it's environment. The experiment is done by real mobile robot Khepera and a simple GA.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.248-253
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• 2015

In order to adapt to human's life and perform missions, a humanoid robot needs a height at least similar with children's. In this paper, we proposed a humanoid robot which is like a child who is taller than 1m. We presented showing the humanoid robot's kinematics, designing of a three-dimensional model, developing mechanisms, and the hardware structures using servo motors and compact size PC. Through this process, we designed and manufactured child humanoid robot 'CHARLES(Cognitive Humanoid Autonomous Robot with Learning and Evolutionary Systems)' that is robot is 1m 10cm tall and 8.16kg in weight. For robot's walking, we applied to ZMP-based walking technique and the creation algorithm is applied for walking patterns. Through experiments, we analyzed walking patterns according to the creation and changing parameter values.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.449-450
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• 2007

Evolvable hardware(EHW) is a dynamic field that brings together reconfigurable hardware, artificial intelligence, fault tolerance and autonomous systems. This paper gives an introduction to the field. The features that can be used to identify and classify evolvable hardware are the evolutionary algorithm, the implementation and the genotype representation. Evolvable hardware (EHW) is hardware that can change its own circuit structure by genetic learning to achieve maximum adaptation to the environment. In conventional EHW, the learning is executed by software on a computer.

• Electronics and Telecommunications Trends
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• v.36 no.1
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• pp.81-88
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• 2021

Post COVID-19, the medical legacy system will be transformed for utilizing medical resources efficiently, minimizing medical service imbalance, activating remote medical care, and strengthening private-public medical cooperation. This can be realized by achieving an entire medical paradigm shift and not simply via the application of advanced technologies such as AI. We propose a medical system configuration named "Medical AI Hub" that can realize the shift of the existing paradigm. The development stage of this configuration is categorized into "AI Cooperation Hospital," "AI Base Hospital," and "AI Hub Hospital." In the "AI Hub Hospital" stage, the medical intelligence in charge of individual patients cooperates and communicates autonomously with various medical intelligences, thereby achieving synchronous evolution. Thus, this medical intelligence supports doctors in optimally treating patients. The core technologies required during configuration development and their current R&D trends are described in this paper. The realization of the central configuration of medical AI through the development of these core technologies will induce a paradigm shift in the new medical system by innovating all medical fields with influences at the individual, society, industry, and public levels and by making the existing medical system more efficient and intelligent.