In this paper, we designed and implemented a moving object prediction and grasping system that enables a robot manipulator using a two-finger gripper to grasp moving objects on a conveyor and a circular rotating platform. There are three main parts: (i) moving object recognition, (ii) moving object prediction, and (iii) system realization and verification. In the moving object recognition, we used the instance segmentation algorithm of You Only Look At CoefficienTs (YOLACT) to recognize moving objects. The recognition speed of YOLACT can reach more than 30 fps, which is very suitable for dynamic object recognition. In addition, we designed an object numbering system based on object matching, so that the system can track the target object correctly. In the moving object prediction, we first designed a moving position prediction network based on Long Short-Term Memory (LSTM) and a grasping point prediction network based on Convolutional Neural Network (CNN). Then we combined these two networks and designed two moving object prediction networks, so that they can simultaneously predict the grasping positions and grasping angles of multiple moving objects based on image information. In the system realization and verification, we used Robot Operating System (ROS) to effectively integrate all the programs of the proposed system for the camera image extraction, strategy processing, and robot manipulator and gripper control. A laboratory-made conveyor and a circular rotating platform and four different objects were used to verify that the implemented system could indeed allow the gripper to successfully grasp moving objects on these two different object moving platforms.

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38400-902 -Uberlandia -MG -Brazil d b M : This paper presents an application of fuzzy logic t o control the speed of a permanent magnet synchronous machine (PUSU). Based on the analysis of the PUSH transient response and fuzzy logic, a fuzzy controller is developed. The fuzzy controller generates the variations of the reference current vector of the PnSU speed control based on the speed error and its change. The analysis of a sliding mode control is used to tune the fuzzy logic controller. The results of applying the fuzzy logic controller to a PUSH are compared to those obtained by the application of a conventional PI system. The fuzzy controller provided a better response than the PI control.