This paper will discuss the calculation of inverse kinematic which will be used to control the 6-DOF articulated robot. This robot consists of 6 Dynamixel MX-28 smart servo with OpenCM 9.04 microcontroller. The articulated robot has been simplified to 4-DOF because there are no obstacles in the work area and no special movements are required. The calculation method uses the intersection point equation between the ball and the line, so that it can make it easier to determine the point in calculating the kinematic inverse. The experiment is carried out using the desired position as input for the kinematic inverse to produce the angle of each joint. From the angle of each joint obtained, it will be entered into forward kinematic so that the end-effector position will be obtained. The desired position will be compared with the end-effector position, and then how much difference will be calculated. From the experimental results, it was found that the inverse kinematic method which has been inverted by the forward kinematic produces the same final position.
Keywords: 6-DOF manipulator, Articulated robot, inverse kinematics and forward kinematics, Dynamixel MX-28, OpenCM 9
This study aims to increase the processing time of detecting non-rice objects based on the you only look once v3-tiny (YOLOv3-tiny) model. The system was developed on the Raspberry Pi 4 embedded system with the Movidius neural compute stick 2 (NCS 2) implementation approach. Data object in the form of gravel on a bunch of rice in the video. The video data was obtained using a webcam with a resolution of 1920 x 1080 pixels with a total of 2759 frames. From the test results, frames per second (FPS) have increased by 1.27x in the Movidius NCS 2 implementation compared to processing using the central processing unit (CPU) from the Raspberry Pi 4. The object detection processing on video data is complete at 1871.408 seconds with 1.474 FPS using the CPU from the Raspberry Pi 4 and finished at 1477.141 seconds with 1.868 FPS using Movidius NCS 2. From these differences, it can be seen that the application of Movidius NCS 2 succeeded in increasing the object detection processing in this study by 26.69% with the YOLOv3-tiny model approach on the Raspberry Pi 4 embedded system.
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