Calibration method suitable for visual servo plug-pull operation
A technology of visual servoing and calibration method, which is applied in the directions of manipulators, program-controlled manipulators, manufacturing tools, etc., can solve the problems of low servo alignment control accuracy, poor plug-in operation effect, etc., and achieve good visual servo effect, improved operation accuracy, The effect of prolonging the service life
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specific Embodiment approach 1
[0040] A calibration method suitable for visual servo plugging and unplugging operations in this embodiment, the method includes the following steps, such as figure 1 Shown:
[0041] Step 1. Calibration preparation:
[0042] Set a plug clamping device for the end joint of the robot arm, and set a calibration plate on the two symmetrical clips of the plug clamping device, as shown in figure 2 As shown, a camera is also installed on the robotic arm of the robot, and the camera is aimed at the target, and the socket and plug clamping device are included in the field of view of the camera; where, the target refers to the socket where the pin is inserted;
[0043] Step 2, using the camera to collect images including pins, sockets and plug holding devices; wherein, during the image collection process, a laser light source is used to provide illumination, and the laser light source is aligned with the plug holding device and the socket;
[0044] Step 3: Use the deep learning algor...
specific Embodiment approach 2
[0052] The difference from Embodiment 1 is that this embodiment is a calibration method suitable for visual servo plugging and unplugging operations. In the first step, a camera is also installed on the robotic arm of the robot. Specifically, a camera is installed on the end joint of the robot arm. A camera is installed.
specific Embodiment approach 3
[0053] The difference from the specific embodiment 1 or 2 is that in this embodiment, a calibration method suitable for visual servo plugging and unplugging operations, in the step 3, when using the deep learning algorithm to perform feature recognition, it uses the neural network built by pyTorch. For the identification of the socket position and the position of the plug holder. PyTorch belongs to a neural network architecture, similar to building blocks, and uses it to build neural networks. PyTorch is a GPU- and CPU-optimized tensor library for deep learning. It is a python-based scientific computing package that mainly has the following two functions:
[0054] A deep learning research platform that provides maximum flexibility and speed as an alternative to the numpy library when using GPUs.
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