Research On Intelligent Recognition Algorithm Of Stacking Robot Based On Deep Learning

With the development of industrial automation and warehousing logistics automation,palletizing robot is more and more widely used in various production links.The main control methods of palletizing robot include teaching programming,off-line programming and template matching algorithm,etc.the technology is relatively mature.At present,it is widely used in limited working environment,and can accurately estimate and recognize the target's pose.However,when the objects are covered by each other,stacked irregularly and illuminated in complex outdoor environment,the existing methods are easy to produce deviation error,which is difficult to meet the needs of industrial automation.In order to solve the problem of automatic operation of palletizing robot in complex scene,a target pose detection algorithm based on visual depth learning is designed.Combined with the specific working conditions,hand eye calibration of large field of view industrial camera and palletizing robot and the establishment of target data set are completed.The algorithm of image processing and data enhancement is verified,which provides experimental basis for the follow-up deep learning training.Aiming at the problems of large parameters and complex computation of mainstream target detection algorithms,the lightweight pruning strategy is used to simplify the target detection network,and a lightweight target detection algorithm YOLO-Slim is designed;The multi-scale fusion is carried out by using the feature extraction network of the lightweight target detection algorithm YOLO-Slim and the single depth estimation algorithm.The loss function design is defined through ablation test,and the monocular depth location recognition(mdlr,monocular depth location recognition)algorithm is constructed,and the end-to-end training of the fusion network is realized;The mdlr algorithm and image processing algorithm are combined to realize the attitude estimation of the target,and the fusion point laser rangefinder is used to correct the attitude estimation.In order to verify the accuracy and robustness of mdlr algorithm,image anti noise and quantitative ranging experiments are designed.The experimental results show that the modified monocular depth positioning recognition algorithm is less affected by ambient light,and has high recognition accuracy and fast calculation speed for occluded and irregular flexible packaging targets,which meets the needs of real-time detection of edge computing platform,Through the robot arm equipped with ROS system,the compatibility of vision system and manipulator operating system is verified,and the corresponding human-computer interaction interface is designed.Finally,deployment test is carried out in the industrial scene of palletizing robot to verify the accuracy of pose estimation and recognition of visual pose detection system.The experiments show that,within the range of calibration,the accuracy of pose estimation and recognition of the system can meet the design requirements,and basically meet the application of palletizing robot in complex working scene.It provides a theoretical basis for the subsequent intelligent algorithm research of palletizing robot based on monocular vision pose detection.