• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.5
• /
• pp.591-597
• /
• 2007

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 polygon based Q-learning algorithm and distributed genetic algorithms are proposed for behavior learning and evolution of collective autonomous mobile robots. 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.

• The Journal of Korean Association of Computer Education
• /
• v.8 no.6
• /
• pp.23-32
• /
• 2005

Dolittle is a educational programming language that helps students learning principles and concepts of computer science with programming. Learning programming with robot improve learning achievements robot motivate to be interest with programming. However, Dolittle robot programming is a different from Dolittle programming in process of interpretation and execution mechanism. Therefore, students have virtually to learn two languages to control robot and it would reduce the worth of Dolittle as educational programming language. In order to solve this problem, we tried to Unify Dolittle and robot control language using parser that Dolittle program with turtle object convert robot program. But this try couldn't overcome completely this problem because attributes of turtle object is different from robot. In this research we unified Dolittle programming and Dolittle robot programming as a way of adding new robot object in dolittle standard object group. it would improve educational effect of learning programming with robot in Dolittle.

• Journal of The Korean Association of Information Education
• /
• v.12 no.2
• /
• pp.195-202
• /
• 2008

The programming learning by using a robot may be one of the most appropriate learning methods for enabling students to experience the creative learning of future society by avoiding the existing stereotyped style educational environment, and understand and improve algorithm which is the basic fundamental of mathematics and science. This study proposed four types of items of errors which may occur during robot programming by elementary school students, and made elementary school students in the fifth and sixth grades learn robot programming after developing the curriculum for the robot programming. Then, the study collected and classified errors that had occurred during the process of learning, and conducted a comparative analysis of computer-based programming language which had been previously studied. This study identified that robot programming in elementary school was shown superior to existing computer-based programming language as a creative learning method and tool through the field experience.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.149.5-149
• /
• 2001

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent´s dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to choose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input-output pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless ...

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.24 no.3 s.174
• /
• pp.653-660
• /
• 2000

The automatization by industrial robot of today is merely rely on to the simple position repeating works, but requirements of research and development to the force control which would adapt positively to various restriction or contacting works to environment. In this paper, a learning control algorithm using, neural networks is proposed for the position and force control by a direct-drive robot. The proposed controller is the feedback controller to which the learning function of neural network is added on to and has a character of improving controller's efficiency by learning. The effectiveness of the proposed algorithm is demonstrated by the experiment on the hybrid position and force control of a parallelogram link robot with a force sensor.

• Journal of the Korean Society of Industry Convergence
• /
• v.18 no.4
• /
• pp.224-230
• /
• 2015

In this paper, it is presented a learning controller for repetitive walking control of biped walking robot. We propose the iterative learning control algorithm which can learn periodic nonlinear load change ocuured due to the walking period through the intelligent control, not calculating the complex dynamics of walking robot. The learning control scheme consists of a feedforward learning rule and linear feedback control input for stabilization of learning system. The feasibility of intelligent control to biped robotic motion is shown via dynamic simulation with 25-DOF biped walking robot.

• Proceedings of the KIEE Conference
• /
• 1996.07b
• /
• pp.1228-1230
• /
• 1996

A neural network(NN) mechanism is proposed to modify the gait of a biped robot that walks on sloping surface using sensory inputs. The robot starts walking on a surface with no priori knowledge of the inclination of the surface. By accumulating experience during walking, the robot improves its walking gait and finally forms a gait that is adapted to the surface inclination. A neural controller is proposed to control the gait which has 72 reciprocally inhibited and excited neurons. PI control is used for position control, and the neurons are trained by a reinforcement learning mechanism. Experiments of static gait learning and pseudo dynamic learning are performed to show the validity of the proposed reinforcement learning mechanism.

• Journal of Mechanical Science and Technology
• /
• v.20 no.7
• /
• pp.917-928
• /
• 2006

In recent decades, Artificial Neural Networks (ANNs) have become the focus of considerable attention in many disciplines, including robot control, where they can be used to solve nonlinear control problems. One of these ANNs applications is that of the inverse kinematic problem, which is important in robot path planning. In this paper, a neural network is employed to analyse of inverse kinematics of PUMA 560 type robot. The neural network is designed to find exact kinematics of the robot. The neural network is a feedforward neural network (FNN). The FNN is trained with different types of learning algorithm for designing exact inverse model of the robot. The Unimation PUMA 560 is a robot with six degrees of freedom and rotational joints. Inverse neural network model of the robot is trained with different learning algorithms for finding exact model of the robot. From the simulation results, the proposed neural network has superior performance for modelling complex robot's kinematics.

• Journal of Korean society of Dental Hygiene
• /
• v.18 no.5
• /
• pp.729-740
• /
• 2018

Objectives: The purpose of this study was to investigate the factors affecting robot utilization in the education of pre-dental hygienists. Methods: A self-reported questionnaire was completed by 238 dental hygiene students studying in the Daejeon, Chungcheong, and Jeolla provinces during the period March 1-31, 2017. Results: Future oral health education media had high selection of 'movies,' 'video,' '3D printer,' 'robot,' and 'drone' In general education and oral health education, robots were appropriate as educators, assistant teachers, and media. This group had high levels of interest, experience, attitude, and learning scope of robots. Robot utilization education showed a significant positive correlation with the 'interest,' 'experience,' 'attitude,' and 'learning' subfactors (p<0.01). Factors influencing robot utilization education were the relationships among actual experience of robot, learning of robot production, social influence of robot, emotional exchange with robot, and the predictive power was 25.5% (p<0.05). Conclusions: Oral health education curricula using robots should be developed considering the emotional exchange and social influence between educator and learner.

• Journal of Digital Contents Society
• /
• v.18 no.8
• /
• pp.1501-1507
• /
• 2017

Telepresence in robot assisted learning has preferred low-height, shorter than life-size robotic platforms for reasons such as operational stability, user convenience and psychological comfort in human robot interaction. If, however, the reason for using a telepresence robot is to display the authority of a social superior to a social inferior, one can hypothesize that a robotic platform which reflects real-life height advantage would be better suited for the stated purpose than conventional low-height platforms. In order to test the hypothesis, we examined whether the height of the robot had an effect on an instructor connected to a telepresence robot in robot-assisted learning with regard to controlling a large number of elementary school students. The pre-and post experiment demonstrates that the use of a life-size telepresence robot, compared to a child-size telepresence robot, failed to make a meaningful difference in the instructors' authority being accepted by the students. However, behavioral measures shows that a taller robot has more merits in controlling students.