No abstract

No abstract

Visual servoing has been widely employed in robotic control to increase the flexibility and precision of a robotic arm. When the end-effector of the robotic arm needs to be moved to a spatial point without a coordinate, the conventional visual servoing control method has difficulty performing the task. The present work describes space constraint challenges in a visual servoing system by introducing an assembly node and then presents a two-stage visual servoing control approach based on perspective transformation. A virtual image plane is constructed using a calibration-derived homography matrix. The assembly node, as well as other objects, are projected into the plane after that. Second, the controller drives the robotic arm by tracking the projections in the virtual image plane and adjusting the position and attitude of the workpiece accordingly. Three simple image features are combined into a composite image feature, and an active disturbance rejection controller (ADRC) is established to improve the robotic arm’s motion sensitivity. Real-time simulations and experiments employing a robotic vision system with an eye-to-hand configuration are used to validate the effectiveness of the presented method. The results show that the robotic arm can move the workpiece to the desired position without using coordinates.

When geometric moments are used to measure the rotation-angle of plane workpieces, the same rotation angle would be obtained with dissimilar poses. Such a case would be shown as an error in an automatic sorting system. Here, we present an improved rotation-angle measurement method based on geometric moments, which is suitable for automatic sorting systems. The method can overcome this limitation to obtain accurate results. The accuracy, speed, and generality of this method are analyzed in detail. In addition, a rotation-angle measurement error model is established to study the effect of camera pose on the rotation-angle measurement accuracy. We find that a rotation-angle measurement error will occur with a non-ideal camera pose. Thus, a correction method is proposed to increase accuracy and reduce the measurement error caused by camera pose. Finally, an automatic sorting system is developed, and experiments are conducted to verify the effectiveness of our methods. The experimental results show that the rotation angles are accurately obtained and workpieces could be correctly placed by this system.

To achieve an automatic unloading of a reactor during the sherardizing process, it is necessary to calculate the pose and position of the reactors in an industrial environment with various amounts of luminance and floating dust. In this study, the defects of classic image processing methods and deep learning methods used for locating the reactors are first analyzed. Next, an improved You Only Look Once(YOLO) model is employed to find the region of interest of the handling hole and a handling hole corner detection method based on the image morphology and a Hough transform is presented. Finally, the position and pose of the reactors will be obtained by establishing a 3D handling hole model according to the principle of a binocular stereo system. To test the performance of the proposed method, a set of experimental systems was set up and experiments were conducted. The results indicate that the proposed location method is effective and the precision of the position recognition can be controlled to within 4.64 mm and 1.68 ° when the cameras are approximately 5 m away from the reactor, meeting the requirements.