• 제어로봇시스템학회논문지
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• 제7권7호
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• pp.567-573
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• 2001

A real-time HVAC system is proposed which implements real-time control of thermal environment for virtual reality. It consists of a pair of hot and cold loops that serve as thermal reservoirs, and a mixing box to mix hot and cold air streams flowing if from loops. Their flow rates are controlled in real-time to meet a set temperature and flow rate. A cascade control algorithm along with gain scheduling is applied to the system and test results shows that the closed-loop response approached set values within 3 to 4 seconds.

• 제어로봇시스템학회논문지
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• 제7권2호
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• pp.146-151
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• 2001

The importance of sensing the force and torque with arbitrary direction and magnitude is becoming more crucial for robotic applications and manufacturing automations. Recently, several designs of a multi-axis force-torque sensor have been tried to sense this force and torque. This paper deals mainly with the signal processing of a six-axis force-torque sensor using cross-shaped elastic structures with circular holes. In this paper, we show principle of sensing force and torque, the signal processing methodology, and efficient methods of seeking strain gage positions in the sensor structure. The validity of the proposed method is shown via experiments.

• 로봇학회논문지
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• 제14권3호
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• pp.157-162
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• 2019

The video game Tetris is one of most popular game and it is well known that its game rule can be modelled as MDP (Markov Decision Process). This paper presents a DQN (Deep Q-Network) based game agent for Tetris game. To this end, the state is defined as the captured image of the Tetris game board and the reward is designed as a function of cleared lines by the game agent. The action is defined as left, right, rotate, drop, and their finite number of combinations. In addition to this, PER (Prioritized Experience Replay) is employed in order to enhance learning performance. To train the network more than 500000 episodes are used. The game agent employs the trained network to make a decision. The performance of the developed algorithm is validated via not only simulation but also real Tetris robot agent which is made of a camera, two Arduinos, 4 servo motors, and artificial fingers by 3D printing.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2012년도 춘계학술대회 논문집
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• pp.267-268
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• 2012

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2008년도 춘계학술대회 논문집
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• pp.469-470
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• 2008

• TTA 저널
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• 통권146호
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• pp.12-13
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• 2013

올해는 뱀의 해이다. '뱀'하면 대부분 사람들은 징그럽고 혐오스러운 모습을 떠올린다. 꿈틀거리는 커다란 몸뚱이, 소리없이 발밑을 '쓱'하고 스쳐 가는 듯한 촉감, 미끈하고 축축할 것만 같은 피부, 무서운 독을 품은채 허공을 날름거리는 기다란 혀, 사람을 노려보는 듯한 차가운 눈초리${\cdots}$. 게다가 아담과 이브를 에덴동산에서 쫓겨나게 한 장본인으로 교활함의 대명사가 돼버린 뱀은 분명 두렵고 꺼리게 되는 동물이다. 그러나 긴 몸을 꿈틀거리며 옆으로 미끄러지듯이 움직이는 뱀의 특이한 걸음걸이는 과학자들에게 오히려 아이디어의 대상이다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2011년도 한국컴퓨터종합학술대회논문집 Vol.38 No.1(C)
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• pp.225-227
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• 2011

최근 스마트폰 및 게임, 로봇, 어플리케이션 등 다양한 분야에서 음성 기반 채팅 시스템이 활용되고 있다. 하지만 그 성능은 아직 만족스럽지 못하다. 본 논문은 다양한 시스템 발화를 위해 문장의 내용어, 카테고리, 발화시간, 화자 정보 등을 이용하여 직전 발화와 현재 발화를 비교한다. 동일한 발화일 경우 해당 카테고리 내 다른 문장을 발화하여 발화의 다양성을 확보하고, 적용 카테고리가 아닐 경우 댓구를 이용하여 대화를 다른 주제로 유도한다. 실험 결과 중복 발화에 대해 다양한 응답을 확인 할 수 있었다.

• 제어로봇시스템학회논문지
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• 제15권10호
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• pp.1029-1038
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• 2009

This paper proposes a gain switching algorithm for joint position control of a hydraulic humanoid robot. Accurate position control of the lower body is one of the basic requirements for robust balance and walking control. Joint position control is more difficult for hydraulic robots than it is for electric robots because of an absence of reduction gear and better back-drivability of hydraulic joints. Backdrivability causes external forces and torques to have a large effect on the position of the joints. External ground reaction forces therefore prevent a simple proportional-derivative (PD) controller from realizing accurate and fast joint position control. We propose a state feedback controller for joint position control of the lower body, define three modes of state feedback gains, and switch the gains according to the Zero Moment Point (ZMP) and linear interpolation. Dynamic equations of hydraulic actuators were experimentally derived and applied to a robot simulator. Finally, the performance of the algorithm is evaluated with dynamic simulations.

• 로봇학회논문지
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• 제15권3호
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• pp.212-220
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• 2020

This paper deals with a strategy of gain optimization for the kinematic control algorithm of a wire-driven surgical robot. The proposed controller consists of the closed-loop inverse kinematics with the back-calculation method. The closed-loop inverse kinematics has 18 PID control gains, and the back-calculation method has 6 gains. An efficient strategy is designed to optimize 18 values first and then the remaining 6 values. The optimal gain sets are searched under the step input with performance indices. In this gain optimization, the objective function is defined as the minimum value of signal-to-noise ratio of the performance indices for 6 DoF (Degree-of-Freedom) motion that is based on the Taguchi method, and the constraints are applied to obtain stable responses for each motion evenly. The gain sets obtained are verified by simulations using the test trajectories. In comparative results, the optimal gain value based on the performance index combined with ISE (integral of square error) and settling time showed the best control performance.

• 로봇학회논문지
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• 제17권2호
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• pp.221-229
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• 2022

Sliding mode control (SMC) is a robust control method to control a robot arm with nonlinear properties. A high switching gain of SMC causes chattering problems, although the SMC allows the adequate control performance by giving high switching gain, without the exact robot model containing nonlinear and uncertainty terms. In order to solve this problem, SMC with sliding perturbation observer (SMCSPO) has been researched, where the method can reduce the chattering by compensating the perturbation, which is estimated by the observer, and then choosing a lower switching control gain of SMC. However, optimal gain tuning is necessary to get a better tracking performance and reducing a chattering. This paper proposes a method that the Q-learning automatically tunes the control gains of SMCSPO with an iterative operation. In this tuning method, the rewards of reinforcement learning (RL) are set minus tracking errors of states, and the action of RL is a change of control gain to maximize rewards whenever the iteration number of movements increases. The simple motion test for a 7-DOF robot arm was simulated in MATLAB program to prove this RL tuning algorithm. The simulation showed that this method can automatically tune the control gains for SMCSPO.