基于改进 YOLOv2 的无标定 3D 机械臂自主抓取方法.

This paper proposed an uncalibrated 3 D robotic arm grabbing method based on improved YOL0v2 in a multi-object environment. Firstly, in order to reduce the depth learning algorithm YOL0v2 detection multi-obj ect bounding box overlapping rate and 3D distance calculation error, it proposed an improved algorithm for YOL0v2. It used this algorithm to detect and identify the target object in the image, obtained the pos ition information of the target object in the RGB image, and then used the K-means + + clustering algorithm to quickly calculate the distance from the target object to the camera according to the depth image information, and estimate the target object size and pose. Simultaneously, it used the improved YOL0v2 to get the bounding box of the gripper and calculated the distance from the robot to the target object. Then the system estimated the distance between the fixture, camera and obj ect in the manipulator coordinate system. Finally, the system used the PID algorithm to control the gripper to grab the object according to the size and posture of the object and the distance from the object to the gripper. The detected boundary boxes of the target obj ect was more accurate based on the improved YOL0v2 than on old one. It also enhanced the distance from the fixture to the object and the size of the object as well as the accuracy of the pose estimation. In addition, in order to avoid complicated calibration, this paper proposed a non-calibration method. The learning scheme was different from the traditional uncalibrated estimation method based on Jacobian matrix, because it had good universality. A simulation experiment shows that the proposed method can accurately classify and locate the objects in the image. The Universal Robot 3 robotic arm uses this framework to verify the effectiveness of capturing objects in a cluttered environment. [ABSTRACT FROM AUTHOR]