Complex Workpiece Positioning System With Nonrigid Registration Method for 6-DoFs Automatic Spray Painting Robot

Abstract

To reduce the labor effort in hazardous environments like spray painting, an automatic car part spray painting machine has been set up. This article presents an intelligent location procedure for this machine. The location procedure assumes that the car part is placed with a tiny arbitrary pose, furthermore some kinds of parts under inspection undergoes through the deformation and thus it contains a nonrigid model. To tackle this problem, a workpiece positioning system works though multicamera is established first. Subsequently, a novel modified iterative closest point (ICP) algorithm is proposed which registers the nonrigid shape to the undeformed source shape in the training library. The modified ICP combines the ideas from traditional ICP and deformation estimation from bounded biharmonic weights. It solves a nonlinear cost function by using Levenberg–Marquardt algorithm. As a result, it estimates the transformation of the target point cloud with regards to the source point cloud. Additionally, it improves the accuracy of traditional ICP and increases its scope to nonrigid shapes. By employing these results in our location procedure, it can estimate the 6-DOF pose of the car part to be painted in addition to that it also estimates the deformation compared to the source cloud in the training library. This information is subsequently used to modify the guidance trajectory of the spray gun. In this article, a test case of front bumper is given, as it is commonly made of soft plastic materials and undergoes deformation under the influence of the external force. All the stated results support the efficacy of our algorithm.