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.

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.

Collaborative Robot (Cobot) that can collaborate with humans by fusion with many advanced technologies among industrial robots in the industrial field are attracting attention. In this study, the engineers of Small and Medium Enterprises can directly participate in the cobot design, and ultimately, the possibility of deriving the shape design of the differentiated cobot was studied. The method applied to derive the shape design of differentiated cobot is ‘Morphological Analysis’. First, the design elements of the form of cobots were derived as ‘Link’ and ‘Joint’. In addition, by analyzing the image form of the Link and Joint of the existing cobot, a new form element of the Link and Joint was proposed. In order to quantitatively identify the most discriminating cobot shape design, FGI (Focus Group Interview) was conducted to derive image types of 4 Link and 3 Joint. Then, the most important ‘Shape Combination’ was carried out in morphological analysis, and 12 new cobot shape designs were drawn. Through this, the applicability of the morphological analysis method in the derivation of differentiated cobot shape design was examined.

Cobots are industrial robots with greatly enhanced safety functions that enable them to work in the same space with workers without protector. Cobots are regulated by the Industrial Safety and Health Act and must be certified according to the manufacturing stage, installation stage and usage stage. The ISO 10218-2 standard applied in the installation phase is difficult to apply in the field. Therefore, it is necessary to develop a risk assessment method based on ISO 12100 standard. This paper proposes a new methodology that combines ‘JSA’ and ‘What-if’, which reflects the human error and the lack of known risk factors. Accordingly, a new risk assessment template was proposed and the effectiveness of the developed new template was examined. The current cobot safety regulations need to be unified with safety inspections scheme, and robot safety experts and infrastructures need to be expanded and Robot safety regulations should be unified to ‘Robot Act’. Based on this research, risk assessment methods suitable for the field need to be developed additionally, and robot safety regulation needs to be transformed to promote the industry.

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.

This paper presents cable-hydraulic driven 3DoF (Degree-of-Freedom) manipulator for cooperative robot with high output/low inertia and enhancing lager workspace of hydraulic manipulator. Hydraulic actuation could be solution to design more higher output manipulator than the one of electric motor actuation due to install actuation source and robot joint separated. In spite of this advantage, the conventional hydraulic driven manipulator using cylinder or vane actuator is not suitable for the candidate of cooperative robot because smaller workspace owing to small RoM (Range of Motion) hydraulic actuator. In this paper, we propose 3DoF manipulator with cable-hydraulic actuation which is more larger ratio of payload-to-weight than the one of conventional cooperative manipulator and larger workspace than the one of existing hydraulic driven manipulator. The performance of proposed manipulator was demonstrated by the experiments for confirming overall workspace task, high payload operation task under worst situation and comparing repeatability between developed manipulator and existed cooperative robots. The results of experiments showed that the appropriate performance of proposed manipulator for cooperative robot.