Recently, in the case of the root industry, although it is a basic industry that forms the basis of manufacturing competitiveness, there continues to be a shortage of manpower due to reasons such as dangerous working environments, industrial economic difficulties, and low wage systems. In addition, the demand for automation of production lines using robots is increasing due to a shrinking labor market due to a decrease in the working population due to aging, higher wages, shorter working hours, and limitations of foreign workers. In this study, a system was developed to automate the injection molding process for producing ball valves for automobiles by applying robot system. The applied process flow consists of alignment and insertion of insert parts, and removal, transfer, and loading of the product after injection molding, which is currently performed manually. Through the application of the developed robot automation system, the cycle time was improved by more than 30% and the defect rate was reduced by more than 70%.

As the importance of artificial intelligence grows rapidly and emerges as a leader in technology, it is becoming an important variable in the next-generation industrial system along with the robot industry. In this study, a safety system was developed using deep learning technology to provide worker safety in a robot workplace environment. The implemented safety system has multiple cameras installed with various viewing directions to avoid blind spots caused by interference. Workers in various scenario situations were detected, and appropriate robot response scenarios were implemented according to the worker's risk level through IO communication. For human detection, the YOLO algorithm, which is widely used in object detection, was used, and a separate robot class was added and learned to compensate for the problem of misrecognizing the robot as a human. The performance of the implemented system was evaluated by operator detection performance by applying various operator scenarios, and it was confirmed that the safety system operated stably.

Path planning is necessary for mobile robots to perform precise and rapid tasks. A collision avoidance function must be included so that the robot can move safely during work, and it must be able to create an optimal path to reduce work execution time and save energy. In this paper, we propose a smart route generation algorithm that searches for global route with an algorithm that can speed up route search and integrates the TEB algorithm that can search for regional optimum routes in real time according to the situation. The performance of the proposed algorithm was verified through actual driving experiments of mobile robots.

In modern society, the delivery service market has grown explosively due to rapid changes in social structure and the recent COVID-19 pandemic. Therefore, various problems such as injury to workers and an increase in human accidents are occurring due to the loading and unloading of parcels. In order to solve this problem, domestic company n is developing a “robot-based cargo loading and unloading system”. In developing a new technology system, quantitative reliability targets should be set for efficient operation and development. In this paper, reliability analysis was conducted through field data for the pneumatic gripper of the “robot-based cargo loading system”. The reliability of the failure data was analyzed to estimate the distribution parameters and MTTF. Random data was derived for the probability of occurrence of a failure with the estimated value. By repeating the simulation to predict the number and year of failures according to the estimated parameters of the probability distribution, it was proposed as a method that reflects realistic probabilities rather than calculating with simple arithmetic using the average MTTF previously used in the field.

Background: Previous robot-mediated gait training has been proven several limitations such as pointless repeated motion training, decreased presence, etc. In this research, adult stroke patients were participated in robot-mediated gait training accompanied with or without virtual reality program. Objectives: Exploring whether the results indicated virtual reality system has contribution to muscle strength and balance ability. Design: A case series research, cross-over trial. Methods: Eleven participants (male 4, female 7) with adults diagnosed as stroke from medical doctor ware engaged. The participants received 2 treatment sessions of identical duration, robot-assisted gait training with virtual reality and robot-assisted gait training with screen-off randomly crossed over include 1-day for each person of wash-out period. The parameter was muscle activity, the researchers assessed sEMG (surface electromyography). Results: The result showed less muscle activities during training in robotassisted gait training with virtual reality circumstances, and these indicated muscles were gluteus medius muscle, vastus medialis muscle, vastus intermedius and vastus lateralis muscle, semimembranosus muscle, gastrocnemius- lateral head, and soleus muscle (P<.05). Conclusion: In this study, we analyzed the outcome of muscle activity for clinical inference of robot-assisted gait training with virtual reality (VR). Less muscle activity was measured in the treatment accompanied by VR, therefore, a more systematic, in-depth and well-founded level of follow-up research is needed.

This paper deals with the dynamic control of redundant robot manipulator. Traditionally, the kinematic control schemes for redundant robot manipulator were developed from the point of speed and used under the assumption that the dynamic control of manipulator is perfect. However, in reality, the precise control of redundant robot manipulator is very difficult due to their dynamics. Therefore, the kinematic controllers for redundant robot manipulator were employed in the acceleration dimension and may be combined with the computed torque method to achieve the accurate control performance. But their control performance is limited by the accuracy of the manipulator parameters such as the link mass, length, moment of inertia and varying payload. Hence in this paper, the proportional and derivative control gains of the computed torque controller are optimized by the genetic algorithm on the typical payloads, and the neural network is applied to obtain the proper control gains for arbitrary loads. The simulation results show that the proposed control method has better performance than the conventional control method for redundant robot manipulator.

A deburring system using the joint of revolute robot manipulator with a tool holder was developed for deburring automation. The tool holder composed of the plunger with spring was developed for freedom of three degree operation. The tool holder was applied for compensation of position errors during trajectory tracing or deburring the workpiece of ununiform. To reduce interacting forces between the high stiffness tool at the end effecter and the workpiece during deburring operation, it was developed to operate flexibly to the direction of tangent line on the revolute axis and to the direction of axis. In this paper, using the deburring system attached at the revolute robot manipulator, deburring experiments were performed. According to the experimental results, the good performance of the proposed deburring system using the revolute robot manipulator was shown.

Recently, education game contents with advanced mobile and Web technology have been widely researched. However, considering of interaction involved for children, it is necessary user friendly interfaces and interesting interactions. In addition, it is important to design to encourage the user’s learning motivation as a way to the effect of educational game contents. Therefore, in this study, we designed and implemented the educational game contents system using inter-working between KINECT camera, PC, and android platform based humanoid robot. The designed system consisted of KINECT system for capturing hand gestures, game contents system for playing games, and android humanoid robot system for interacting with users. This system can lead to interests and willingness of users through handy interaction such as hand gestures and interesting quiz game contents. To improve effectiveness and userability, we plant to advance designed educational game contents system that can be operated on a smart TV system including a built-in camera as a further study.

과거 실제 공간에서 발생되는 결과를 가상공간에서 입력 받아 수행되는 게임 시스템이 개발 되었다. 개발된 시스템에 스마트 디바이스를 결합한 새로운 게임 시스템을 개발 하였다. 가상공간에서 벌어지는 다양한 내용들이 실제 공간에서 로봇 구동과 조화 되는 시스템이 개발 되면 게임의 흥미는 매우 높을 것으로 기대 한다. 제안 시스템은 가상공간의 유닛들과 스마트 디바이스 결합 로봇의 조화 구성을 하였다. 해당 로봇은 실제 공간에서 구동되고, 가상현실에서는 그래픽 유닛이 구동 된다. 그리고 가상현실과 실제가 결합하여 새로운 결과를 유추 한다. 본 논문에서는 스마트 디바이스를 결합해 가상공간의 내용이 실제 공간에 구현되는 전체 시스템을 소개 한다. 본 논문에서 소개하는 내용은 가상공간의 유닛들이 실제 로봇으로 구현하는 하드웨어 시스템과 가상공간에서 구현되는 내용을 로봇과 통신하는 소프트웨어 시스템이다. 본 논문에서 중요 요점은 제안하는 서버 시스템과 클라이언트 시스템 그리고 스마트 디바이스와 결합한 로봇 MCU를 참조 한다면 가상공간과 실제 공간이 결합 되는 다양한 게임을 만들 수 있다고 판단 한다

Driving mechanism, the central part of a robot, was designed in this study. Power for the motive drive was acquired by directly connecting the motor shaft in worm shape of the low-end DC motor, car window motor, to a decelerator. The decelerator consists of a worm gear to receive power from the motor shaft, a pinion gear to be connected in line with the worm gear, and an output shaft to be engaged to the pinion gear. Motion driving is achieved by the power from the motor shaft with the designed gears, transferred to the deceleration mechanism and to the output gear

Recently, robot-assisted joint replacement surgeries are on the rise. Robot-assisted surgery can make more accurate outcome, and thus it will be more popular. For the accurate result, secure fixation of bone is necessary, but there are numerous difficulties for the secure fixation. Detecting bone motion is necessary to prevent some errors. Currently, optical sensor and location sensor are in use; however, these sensors can cause interventional problem for the friction between mechanical devices. This study shows how to compose the bone motion detecting device using electromagnetic sensor, how to commercialize the bone motion detecting device.

최근 자동화생산 및 장치산업에서 로봇시스템의 사용이 증가하고 있다. 그러나 로봇 오퍼레이터, 프로그래머, 공무요원 등 간의 커뮤니케이션 부족으로 휴먼에러가 발생하여 사람의 안전재해사고 뿐 아니라 로봇시스템 가동율에서도 나뱉 영향을 주고 있다. 따라서 본 연구에서는 여유컴포넌트 마코프모형을 기초로 휴먼에러를 고려한 마코프모형을 제안한다.

This study presents intelligent deburring system which can transfer the exper's skill to deburring robot through neural network. The expert's skill is expressed as associate mapping between the characteristics of the burr and human expert's action. Under the fundamental idea that the state of the deburring process can be extracted via the visual sense of the human, we employ vision system for the perception and identification of the changing burr. From the demonstration of human experts, force data are measured and fitted impedance model. Finally the characteristics of the burr and coressponding force are associated by the neural network which is trained through many demonstrations. The proposed method is verified in the deburring process of welding burr.

This paper presents a control and operation system for a remotely operated vehicle (ROV). The ROV used in the study was equipped with a manipulator and is being developed for underwater exploration and autonomous underwater working. Precision position and attitude control ability is essential for underwater operation using a manipulator. For propulsion, the ROV is equipped with eight thrusters, the number of those are more than six degrees-of-freedom. Four of them are in charge of surge, sway, and yaw motion, and the other four are responsible for heave, roll, and pitch motion. Therefore, it is more efficient to integrate the management of the thrusters rather than control them individually. In this paper, a thrust allocation method for thruster management is presented, and the design of a feedback controller using sensor data is described. The software for the ROV operation consists of a robot operating system that can efficiently process data between multiple hardware platforms. Through experimental analysis, the validity of the control system performance was verified.

In this paper, we introduce a target position reasoning system based on Bayesian network that selects destinations of robots on a map to explore compound disaster environments. Compound disaster accidents have hazardous conditions because of a low visibility and a high temperature. Before firefighters enter the environment, the robots notify information in advance, such as victim’s positions, number of victims, and status of debris of building. The problem of the previous system is that the system requires a target position to operate the robots and the firefighter need to learn how to use the robot. However, selecting the target position is not easy because of the information gap between eyewitness accounts and map coordinates. In addition, learning the technique how to use the robots needs a lot of time and money. The proposed system infers the target area using Bayesian network and selects proper x, y coordinates on the map based on image processing methods of the map. To verify the proposed system, we designed three example scenarios based on eyewetinees testimonies and compared time consumption between human and the system. In addition, we evaluate the system usability by 40 subjects.