• The Journal of Korea Robotics Society
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• v.15 no.3
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• pp.256-268
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• 2020

This study is about a design method for deriving task safety scenarios for the application of collaborative robots. A five-step process for deriving task safety scenarios for collaborative robots has been proposed, which focuses on the type of collaboration between human and collaborative robot. The three types of collaboration were classified according to the collaboration workspace and the worktime of human and collaborative robot. Based on these three types of collaboration, task safety scenarios include scenarios that predict risk from unintended use during work. Collaboration with collaborative robot is a human-centered process because human actions can create dangerous situations. Besides, we improved the understanding of this design methodology by presenting examples of the application of task safety scenarios according to the process for each type of collaboration.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.4_1
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• pp.545-551
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• 2023

In this paper, a robot automation methodology for chemical drum assembly in semiconductor industries are presented. Robot automation is essential to resolve safety issues in which operators are directly or indirectly exposed to chemicals or fumes in assembling dispense heads on chemical drums. However, the chemical drum assembling process involves complex and difficult tasks, such as mating male/female keycodes and fastening screws with large-diameter, which may be very difficult to be performed by a single-arm robot with a commercial rigid F/T sensor. In order to solve the problems, a method for assembling a chemical drum using dual-arm collaborative robot system, compliance F/T sensor, robot vision and gripper is presented.

• The Journal of Korea Robotics Society
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• v.14 no.4
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• pp.311-317
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• 2019

Direct teaching is an essential function for collaborative robots for easy use by non-experts. For most robots, direct teaching is implemented only in joint space because the realization of Cartesian space direct teaching, in which the orientation of the end-effector is fixed while teaching, requires a measurement of the end-effector force. Thus, it is limited to the robots that are equipped with an expensive force/torque sensor. This study presents a Cartesian space direct teaching method for torque-controlled collaborative robots without either a force/torque sensor or joint torque sensors. The force exerted to the end-effector is obtained from the external torque which is estimated by the disturbance observer-based approach with the friction model. The friction model and the estimated end-effector force were experimentally verified using the robot equipped with joint torque sensors in order to compare the proposed sensorless approach with the method using torque sensors.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.423-424
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• 2013

• Journal of Drive and Control
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• v.21 no.3
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• pp.36-45
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• 2024

Collaborative robots, designed for direct interaction with humans have limited adaptability to environmental changes. This study addresses this limitation by implementing a barista robot system using AI technology. To overcome limitations of traditional collaborative robots, a model that applies a real-time object detection algorithm to a 6-degree-of-freedom robot arm to recognize and control the position of random cups is proposed. A coffee ordering application is developed, allowing users to place orders through the app, which the robot arm then automatically prepares. The system is connected to ROS via TCP/IP socket communication, performing various tasks through state transitions and gripper control. Experimental results confirmed that the barista robot could autonomously handle processes of ordering, preparing, and serving coffee.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.1
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• pp.135-141
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• 2016

Recently, the needs for the development and application of marine robots are increasing as marine accidents occur frequently. However, it is very difficult to acquire the information by utilizing marine robots in the marine environment. Therefore, the needs for the researches of sensor networks which are composed of underwater, surface and aerial robots are increasing in order to acquire the information effectively as the information from heterogeneous robots has less limitation in terms of coverage and connectivity. Although various researches of the sensor network which is based on marine robots have been executed, all of the underwater, surface and aerial robots have not yet been considered in the sensor network. To solve this problem, a collaborative control method based on the acoustic information and image by the sonars of the underwater robot, the acoustic information by the sonar of the surface robot and the optical image by the camera of the static-floating aerial robot is proposed. To verify the performance of the proposed method, the collaborative control of a MUR(Micro Underwater Robot) with an OAS(Obstacle Avoidance Sonar) and a SSS(Side Scan Sonar), a MSR(Micro Surface Robot) with an OAS and a BMAR(Balloon-based Micro Aerial Robot) with a camera are executed. The test results show the possibility of real applications and the need for additional studies.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.6_2
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• pp.953-959
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• 2021

In this paper, the numerical inverse kinematics analysis is presented for a collaborative robot with an offset wrist. Robot manipulators with offset wrist are widely used in industrial applications, due to many advantages over those with wrist center and those with three parallel axes such as simple mechanical design, light weight, and so on. There may not exist a closed-form solution for a robot manipulator with offset wrist. A simple numerical method is applied to solve the inverse kinematics with offset wrist. Singularity is analyzed using Jacobian matrix and the numerical inverse kinematics algorithm is implemented on the real-time controller.

• Journal of the Korea Safety Management & Science
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• v.23 no.4
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• pp.93-104
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• 2021

In this paper, we propose a method for diagnosing overload and working load of collaborative robots through performance analysis of machine learning algorithms. To this end, an experiment was conducted to perform pick & place operation while changing the payload weight of a cooperative robot with a payload capacity of 10 kg. In this experiment, motor torque, position, and speed data generated from the robot controller were collected, and as a result of t-test and f-test, different characteristics were found for each weight based on a payload of 10 kg. In addition, to predict overload and working load from the collected data, machine learning algorithms such as Neural Network, Decision Tree, Random Forest, and Gradient Boosting models were used for experiments. As a result of the experiment, the neural network with more than 99.6% of explanatory power showed the best performance in prediction and classification. The practical contribution of the proposed study is that it suggests a method to collect data required for analysis from the robot without attaching additional sensors to the collaborative robot and the usefulness of a machine learning algorithm for diagnosing robot overload and working load.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.425-426
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• 2013

• The Journal of Korea Robotics Society
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• v.11 no.4
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• pp.242-247
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• 2016

A robot manipulator handling a heavy weight requires high-capacity motors and speed reducers, which increases the cost of a robot and the risk of injury when a human worker is in collaboration with a robot. To cope with this problem, we propose a collaborative manipulator equipped with a counterbalance mechanism which compensates mechanically for a gravitational torque due to the robot mass. The prototype of the manipulator was designed on the basis of a four-bar linkage structure which contains active and passive pitch joints. Experimental performance evaluation shows that the proposed robot works effectively as a collaborative robot.