• 로봇학회논문지
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• 제17권3호
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• pp.264-272
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• 2022

Human-robot cooperative tasks are increasingly required in our daily life with the development of robotics and artificial intelligence technology. Interactive reinforcement learning strategies suggest that robots learn task by receiving feedback from an experienced human trainer during a training process. However, most of the previous studies on Interactive reinforcement learning have required an extra feedback input device such as a mouse or keyboard in addition to robot itself, and the scenario where a robot can interactively learn a task with human have been also limited to virtual environment. To solve these limitations, this paper studies training strategies of robot that learn table balancing tasks interactively using deep reinforcement learning with human's facial expression feedback. In the proposed system, the robot learns a cooperative table balancing task using Deep Q-Network (DQN), which is a deep reinforcement learning technique, with human facial emotion expression feedback. As a result of the experiment, the proposed system achieved a high optimal policy convergence rate of up to 83.3% in training and successful assumption rate of up to 91.6% in testing, showing improved performance compared to the model without human facial expression feedback.

• 센서학회지
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• 제33권1호
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• pp.18-23
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• 2024

Humanoid robots, designed to interact in human environments, require stable mobility to ensure safety. When a humanoid robot falls, it causes damage, breakdown, and potential harm to the robot. Therefore, fall detection is critical to preventing the robot from falling. Prevention of falling of a humanoid robot requires an operator controlling a crane. For efficient and safe walking control experiments, a system that can replace a crane operator is needed. To replace such a crane operator, it is essential to detect the falling conditions of humanoid robots. In this study, we propose falling detection methods using Convolution Neural Network (CNN) model. The image data of a humanoid robot are collected from various angles and environments. A large amount of data is collected by dividing video data into frames per second, and data augmentation techniques are used. The effectiveness of the proposed CNN model is verified by the experiments with the humanoid robot MAX-E1.

• 한국콘텐츠학회논문지
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• 제5권5호
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• pp.85-91
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• 2005

최근 홈네트워크 시스템에 홈로봇이 적용되어 보다 다양한 서비스를 제공하고 있다. 홈로봇은 무선 홈 네트워크 시스템을 기반으로 자율주행을 하며 기본적으로 홈디바이스의 제어뿐만 아니라 홈모니터링을 통한 방범 방재 서비스 및 각종 엔터테인먼트 서비스를 수행한다. 하지만 기존의 홈로봇은 모든 기능을 로봇에 탑재하여 로봇 단말의 크기가 커지고 수행할 콘텐츠나 어플리케이션의 관리가 용이하지 못하며 새로운 기능 추가에 많은 어려움을 겪고 있다. 또한 로봇의 위치인식 기능에 있어서도 많은 개선점이 필요한 실정이다. 본 논문에서는 로봇의 복잡한 연산처리를 외부 디지털 디바이스에게 분담하여 로봇의 자원을 효율적으로 이용하고 새로운 기능에 대한 추가도 용이하도록 하며 RFID를 통해 로봇의 위치를 인식함으로써 보다 개선된 홈로봇 시스템을 제안한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.502-504
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• 2004

With rapidly growing of computer and internet technology, Internet-based tote-operation of robotic systems has created new opportunities in resource sharing, long-distance learning, and remote experimentation. In this paper, remote control system of a mobile robot through the internet has been designed. The internet users can access and command a mobile robot in the real time, receiving the robot's sensor data. The overall system has been tested and its usefulness shown through the experimental results.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.139-142
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• 1996

This paper presents a method of controlling contact force for deburring tasks. The cope with the nonlinearities and time-varying properties of the robot and the environment, a neural network control theory is applied to design the contact force control system. We show that the contact force between the hand and the contacting surface can be controlled by adjusting the command velocity of a robot hand, which is accomplished by the modeling of a robot and the environment as Mass-Spring-Damper system. Simulation results are shown.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1998년도 춘계학술대회 논문집
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• pp.227-232
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• 1998

We developed a Off-Line Graphic Simulator which can simulate a robot model in 3D graphics space in Windows 95 version. 4 axes SCARA robot was adopted as an objective model. Forward kinematics, inverse kinematics and robot dynamics modeling were included in the developed program. The interface between users and the off-line program system in the Windows 95's graphic user interface environment was also studied. The developing language is Microsoft Visual C++. Graphic libraries, OpenGL, by Silicon Graphics, Inc. were utilized for 3D graphics.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 하계종합학술대회 논문집(5)
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• pp.161-164
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• 2001

In this paper, we has been studied self controlled mobile robot system with CCD camera. This system consists of TMS320F240 digital signal processor, step motor, RF module and CCD camera. we used wireless RF module for movable command transmitging between robot and host PC. This robot go straight until 95 percent filled screen from input image. And the robot recognizes obstacle about 95 percent filled something, so it could avoid the obstacle and conclude new path plan.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.3083-3085
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• 1999

This paper presents an algorithm that the mobile robot track accurately a moving object with information from a CCD camera mounted on mobile robot. Singular Value Decomposition is adapted to remove the measurement noise of a Raw data of CCD. The mobile robot estimate the trajectory using Kalman filter and track the path of a moving object with a servo motor. Computer simulation results are showed that the efficient tracking system for the mobile robot is designed properly.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.540-543
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• 1997

In this paper, a precise trajectory tracking method for mobile robot using a vision system is presented. In solving the problem of precise trajectory tracking, a hierarchical control structure is used which is composed of the path planer, vision system, and dynamic controller. When designing the dynamic controller, non-ideal conditions such as parameter variation, frictional force, and external disturbance are considered. The proposed controller can learn bounded control input for repetitive or periodic dynamics compensation which provides robust and adaptive learning capability. Moreover, the usage of vision system makes mobile robot compensate the cumulative location error which exists when relative sensor like encoder is used to locate the position of mobile robot. The effectiveness of the proposed control scheme is shown through computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1917-1920
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• 2003

Automatic tracking of a moving object such as a person is a demanding technique especially in surveillance. This paper describes an experimental system for tracking a moving object on the ground by using a visually controlled aerial robot. A blimp is used as the aerial robot in the proposed system because of its locality in motion and its silent nature. The developed blimp is equipped with a camera for taking downward images and four rotors for controlling the progression. Once a camera takes an image of a specified moving object on the ground, the blimp is controlled so that it follows the object by the employment of the visual information. Experimental results show satisfactory performance of the system. Advantages of the present system include that images from the air often enable us to avoid occlusion among objects on the ground and that blimp’s progression is much less restricted in the air than, e.g., a mobile robot running on the ground.