Robot control for handling fabrics during sewing methods based on computational intelligence and visual feedback (Zacharia, 2008)
The objective of this thesis is the development of control strategies for robot handling of flexible sheets towards the sewing. Besides the difficulties that emerge when handling rigid materials using robots, flexible materials pose additional problems due to due to their unpredictable behavior. In particular, fabrics present low resistance in bending that leads to the appearance of deformations that change their shape and present non-linearity and anisotropy, which poses difficulty in modeling them for real-time applications. The research for this thesis has been focused on the development of control strategies based on Artificial Intelligence techniques (Fuzzy Logic, Genetic algorithms and Neural Networks) and Visual Servoing. The intelligent control systems with artificial vision enable robot to perform skilful tasks related to sewing fabrics in realistic environments towards higher flexibility and automation. The control strategies that have been developed are based on Artificial Intelligence techniques (Fuzzy Logic, Genetic algorithms and Neural Networks) and Visual Servoing. The basic goals of this thesis are the minimization of the total time for robot sewing fabrics and the constraint for the stitching errors inside the acceptable limits. In the context of this thesis, a complete intelligent system has been developed for the handling of fabrics towards sewing. This system is comprised of a robot, two cameras and a sewing machine and a wide range of fabric pieces that was used for experimental purposes. The sewing process is decomposed into preprocess planning and on-line handling subtasks (transferring towards the needle, stitching process and the rotation around the needle). A fuzzy control system was developed for robot handling fabrics on a worktable using a wide range of fabrics. Special emphasis was also given on the development of a system capable of tolerating deformations that may appear on the fabric towards robot handling. Next, optimization methods concerning the handling subtasks were developed in the direction of minimizing the total time for robot sewing fabrics considering the maximum allowable error limits. The parameters were tuned using Genetic Algorithms as an off-line process and a Supervisory fuzzy system in an on-line process. Fabrics comprised of straight edges were used for the experimental verification of the system. The next step concerns the development of a control strategy for robot sewing fabric comprised of curved edges with arbitrary curvatures. The proposed method combines the dominant point detection approach with a micro-Genetic Algorithm for the polygonal approximation of the curved edges. The optimization problem aims at the minimization of the polygonal edges that approximate the curved edges without exceeding the maximum acceptable error limits. In addition, an adaptive neuro-fuzzy system for robot sewing fabrics of curved edges is developed, which has learning capabilities. The system was trained through experiments with various fabrics of different curvatures and is capable to respond to new fabrics, which had not been included in the training process. The construction of the proposed neuro-fuzzy system is based on the use of a novel clustering method. The proposed clustering method is based on the development of a Genetic Algorithm with variable-length chromosomes that has the advantage of flexibility as far as the number of the resulting clusters is concerned. The contribution of the proposed method is twofold. On the one hand, the method evolves automatically the appropriate number of cluster centers, as well as the partitioning of the data, without a priori assumption on the cluster centers. On the other hand, it searches for candidate cluster centers in the universe of discourse and not only among data. The proposed approach is general and it is not limited to the construction of the neuro-fuzzy system. It is also worth noting that all developed control strategies have been applied to fabrics of different shape (with or without curvatures), color and properties.