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 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.

This paper proposed a method of cooperative control of three mobile robots for carrying an object placed on a floor together. Each robot moves to the object independently from its location to a pre-designated location for grasping the object stably. After grasping the common object, the coordination among the robots has been achieved by a master-slave mode. That is, a trajectory planning has been done for the master robot and the distances form the master robot to the two slave robots have been kept constant during the carrying operation. The localization for mobile robots has been implemented using the encoder data and inverse kinematics since the whole system does not have the slippage as much as a single mobile robot. Before the carrying operation, the lifting operations are implemented using the manipulators attached on the top of the mobile robots cooperatively. The real cooperative lifting and carrying operations are implanted to show the feasibility of the master-slave mode control based on the kinematics using the mobile manipulators developed for this research.

Abstract It is essential to estimating positions of multiple robots in order to perform cooperative task in common workspace. Accordingly, we propose a new approach of cooperative localization for multiple robots utilizing correlation among GPS errors in common workspace. Assuming that GPS data of individual robot are correlated strongly as the distance among robots are close, it is confirmed that the proposed method provides improved localization accuracy. In addition, we define two operational parameters to apply proposed method in multiple robot system. With mentioned two parameters, we present a practical solution to accumulated position error in traveling long distance.