Vision modeling optimization of freely placed and moving objects

For the intelligent operations using robots and vision sensors, it is a seemingly simple but in fact complex problem to design an efficient and accurate visual feature matching program. To determine the optimum identification pattern of freely placed and moving objects, the orthogonal experimental approach was employed. Firstly, a complex part that had eight visual features was selected as the experimental parts. Secondly, the experiment scheme was planned as a L12(2 ) orthogonal array. Thirdly, the robotic handling experiments were performed. Finally, the experimental data were analyzed and the results indicated that different identification patterns of the shape and alignment would lead to different success rates, and the maximal deviation of success rates reached 60%. Furthermore, according to the variance analysis the optimum identification pattern was determined. Introduction The rapid and effective identification of identical objects that are randomly placed in a moving platform with the help of vision equipment is still a challenging task for robotic operations. In addition to the objects’ properties, the challenge becomes further complicated owing to the dynamic working environment with the production line. Panagiota et al. [1, 2] presented a method for the identification of multiple randomly placed objects using a 2D vision system combined with data from computer-aided design files for the generation of 3D coordinates. João et al. [3] investigated the application of computer vision and machine learning approaches to solve a problem of feeding cork strips into drillers that move on a conveyor belt. Ashutosh et al. [4] proposed an algorithm for enabling a robot to grasp an object that it has never seen before. Their learning algorithm neither tries to build, nor requires, a 3D model of the object. Instead it predicts, directly as a function of the images, a point at which to grasp the object. Yuichi [5] developed a vision based human–robotic interaction framework for the modeling and localization of industrial objects typically found in an assembly task. This paper presents the orthogonal experimental approach to determine the optimum identification pattern of shape and alignment of the freely placed and moving objects for robotic handling. Experimental Setup As shown in Fig. 1, the experimental platform includes an industrial robot of Mitsubishi RV-7F-D series, an embedded vision sensor of COGNEX insight, and a conveyor. And the experimental part is a flange part with a center hole, a hexagonal bulge and four threaded holes. This experimental platform could simulate the recognition and picking up of the objective part from the various parts freely placed on the moving conveyor belt. Obviously, in this dynamic working environment the orientation and shape of the part is random, and the area that the part moves into the real time vision is also random. Therefore, if the visual feature matching pattern is designed unreasonably, the success rate of identification is likely to be very low. In other words, owing to the Advances in Engineering Research (AER), volume 102 403 Second International Conference on Mechanics, Materials and Structural Engineering (ICMMSE 2017) This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Copyright © 2017, the Authors. Published by Atlantis Press. complex working conditions and the objects’ properties, the vision identification pattern should be optimized. Fig. 1 Experimental setup. Orthogonal Experiment Orthogonal arrays (often referred to Taguchi Methods) are often employed in industrial experiments to study the effect of several control factors [6]. With respect to the experimental part, it has ten features of four holes and six corners that are marked by 1 to 10 in Fig. 2. Different combinations of these features could be the identification pattern of shape and alignment.