Field Coverage And Shooting Path Optimization For Visual Measurement

In the era of industrial intelligence,the application of visual measurement to detect products has become an indispensable part of industrial production,and its high-precision and high-efficiency dimensional measurement scheme has greatly improved the production efficiency.Shooting imaging refers to the acquisition of information on the surface of objects by using camera and lens based on the optical principle.In visual measurement,the shooting and imaging of the workpiece area to be measured is usually considered as the first step of the measurement workflow,and the integrity and efficiency of the shooting are the basis of the subsequent measurement work.In order to solve the problems of low efficiency and incomplete field of view coverage discrimination between the workpiece area to be measured and the camera lens field of view,and to realize the automation of shooting and imaging operations,this paper will study field of view coverage and shooting path oriented to visual measurement.The main content of this paper is as follows:1.This paper proposes a shooting path solution based on cluster partitioning and improved ant colony algorithm.Aiming at the problem of shooting location of the area under test of the workpiece,this scheme proposed a shooting location method of rectangular clustering partition and external spiral traversal based on improved K-means algorithm,which could reduce the number of shots as far as possible on the premise of ensuring the integrity of the shooting of the area under test of the workpiece.Secondly,this paper improved the pheromone updating rules in the ant colony algorithm,proposed a dynamic pheromone updating ant colony algorithm,and combined with the idea of divide-and-conquer strategy,applied it to the shooting path planning problem.Experimental results show that,compared with other heuristic algorithms,the ant colony algorithm with dynamic pheromone updating under division-and-conquer strategy can greatly shorten the path planning time and obtain better path values.2.The objective is to focus on the solution of the path optimization problem.According to the characteristics of the different size of the camera lens and the size of the workpiece to be measured,this paper proposes a path optimization scheme based on double layer ant colony and variable camera lens field of view on the basis of the ant colony algorithm based on cluster partitioning and dynamic pheromone updating.In this scheme,the concept of two-layer ant colony is introduced.The lower ant colony is used for path search in each cluster partition,and the upper ant colony is used for path search between cluster partitions.The lower ant colony can obtain the path in the region and feedback it to the upper ant colony by constantly adjusting the field of view of the camera lens,so as to realize the global search of the whole path.Experimental results show that this scheme can obtain better path values.3.Integrating the shooting method and path planning scheme in the industrial vision scene,this paper designed and built the shooting path planning system of industrial vision measurement based on Qt framework.The system realized the automation of shooting and imaging operations through the visual interface operation.