Research On The Key Technology Of Mechanical Parts Grasping By Robot Arm Based On Binocular Vision

With the continuous development of industrial technology,the application scope of industrial automation robots is expanding gradually.Since the concept of intelligent manufacturing was proposed,modern industries have raised higher requirements for the capabilities of robots,which needs to possess higher levels of intelligence and flexibility.Therefore,the integration of vision technology with industrial robots has become an important direction for advancing robot technology development.Among them,binocular stereo vision technology has the characteristics of being low cost,high recognition accuracy,and high speed,and can obtain depth information based on binocular images,showing great potential for applications.However,there are some problems at the same time.The commonly used stereo matching algorithms are sensitive to factors such as illumination and texture,which are prone to errors;the existing vision-based part grasping schemes are often unable to accomplish complex grasping tasks in the case of multiple types of parts and multiple targets,as well as the low efficiency of binocular vision systems for accurate recognition and localization,which need to be further optimized.To address these problems,this work studies and proposes improvement plans for key issues in the grasp system based on binocular vision.These mainly include research on image preprocessing algorithms,object detection algorithms,and stereo matching algorithms.The specific contents are as follows:(1)Image pre-processing algorithm research and designComparing Gaussian filtering,adaptive Wiener filtering and bilateral filtering,the bilateral filtering is selected as the noise reduction algorithm according to the experimental results;the Retinex algorithm is improved for the problem of uneven illumination in real situations to enhance the illumination evaluation of local areas.The final experiments show that the improved Retinex algorithm has significant improvement compared with the original algorithm,and the enhancement effect is improved by about 8%according to the analysis of the mean and variance of grayscale.(2)Improvement and validation of object detection algorithmsTo address the lack of detection and recognition of multiple types of objects in the current implementation plan,the YOLOv5 convolutional neural network,which is widely used in various applications,was selected as the object detection algorithm.To address the problem of messy background information in actual industrial environments,Transformer encoder blocks and Convolutional block attention module(CBAM)modules were introduced to improve the network algorithm.After experimental verification with an actual dataset,the detection accuracy of the improved network was increased by over 5%.(3)System calibration and improvement of stereo matching algorithmsWe completed the system calibration through experiments.To mitigate the low efficiency and accuracy issues in current binocular vision positioning technology,we researched and improved the key stereo matching algorithm based on the SGM stereo matching algorithm.The Census cost function was introduced to enhance the algorithm’s adaptability to weak texture and uneven illumination conditions.The improved algorithm demonstrated an operation efficiency and matching accuracy enhancement of approximately 22.6%and 13.8%,respectively.(4)Construction of a system simulation platform and prototype testingThe robot simulation environment was constructed using the ROS system to simulate the robot’s movement.Hardware selection was completed for the binocular camera,industrial robot,and control cabinet,and the system platform was built.Relevant program designs for image acquisition,processing,target positioning,and robot arm control were implemented.Finally,testing was completed in an actual environment.The results showed that the system’s efficiency in grasping parts reached 10 pieces/minute,with a success rate of over 95%,and it was able to accomplish complex grabbing tasks in a multi-part and multi-target scenario.This paper thoroughly studied the key issues in the binocular vision grasping system and verified the feasibility and practicality of the improvement scheme through prototype testing.These achievements are expected to not only provide important technical support for the intelligent development of industrial robots but also have a positive impact on the advancement of intelligent manufacturing.