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Examines the thirteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Examines the twelfth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Some areas of the apparel industry, such as folding and sewing, are still labor intensive. The purpose of this paper is to present a new fuzzy visual servoing strategy for the folding of fabric strips by robotic manipulators.

Three stages of the folding task are distinguished experimentally, the initial laying, the true folding, and the final laying. An indirect visual servoing fuzzy system, employing two cameras, is developed to guide the robot along each of the stages.

The proposed scheme manages to successfully fold some of the tested materials. The experimental results are promising and well within the limitations posed by both the employed equipment and the nature of the handled materials.

This study is limited to rectangular strips of fabrics and does not consider the speed of the process.

The resulting system provides a stepping stone for the introduction of automation to currently labor‐intensive areas of the apparel industry.

The separate folding stages reduce the complexity of the overall system and the introduced visually extracted features allow a closer monitoring of the process.

The purpose of this paper is to develop an efficient method for solving a vehicle scheduling problem (VSP) in 2D industrial environments. An autonomous vehicle is requested to serve a set of work centers in the shop floor providing transport and delivery tasks while avoiding collisions with obstacles during its travel. The objective is to find a minimum in length, collision‐free vehicle routing schedule that serves timely as many as possible work centers in the shop floor.

First, the vehicle's environment is mapped into a 2D B‐Spline surface embedded in 3D Euclidean space using a robust geometric model. Then, a modified genetic algorithm is applied on the generated surface to search for an optimum legal route schedule that satisfies the requirements of the vehicle's mission.

Simulation experiments show that the method is robust enough and can determine in a reasonable computation time a solution to VSP under consideration.

There is a gap in the literature for methods that face VSP in shop‐floor environments. This paper contributes to filling this gap by implementing a practical method that can be easily programmed and included in a modern service delivery system.

The paper aims to propose an approach to intelligent evaluation of the tensile test. A robotized system is used that performs the fabrics tensile test and estimates the extensibility of the samples using a feed‐forward neural network while trying to imitate the human expert estimation.

The specifications of the tensile test are derived by an extensive observation of the respective experts' estimation performance. The fabric sample size and the experimental conditions are specified. Linguistic values of the term “fabric extensibility” are extracted through a knowledge acquisition process. The tensile test is performed by a robot manipulator with a simple gripper and the experimental measurements (force, strain) are fed online into a neural network. The network is trained according to the extensibility estimations of the experts. The trained network is tested in estimating unknown fabric's extensibility.

The results demonstrate that the system is capable of estimating the extensibility of new fabrics.

This work can be integrated in the robotized sewing process with intelligent control where the fabric's extensibility in terms of linguistic values is necessary. The proposed system initiates a new approach, in which the fabric properties are expressed and used in a way that will facilitate the introduction of the artificial intelligence methods into the clothing industry.

The purpose of this paper is to present a flexible automation system for the manipulation of fabrics lying on a work table and focuses on the design of a robot control system based on visual servoing and fuzzy logic for handling flexible sheets lying on a table. The main contribution of this paper is that the developed system tolerates deformations that may appear during robot handling of fabrics due to buckling without the need for fabric rigidization.

The vision system, consisting of two cameras, extracts the features that are necessary for handling the fabric despite possible deformations or occlusion from the robotic arm. An intelligent controller based on visual servoing is implemented enabling the robot to handle a variety of fabrics without the need for a mathematical model or complex mathematical/geometrical computations. To enhance its performance, the conventional fuzzy logic controller is tuned through genetic algorithms and an adaptation mechanism and the respective performance is evaluated. The experiments show that the proposed robotic system is flexible enough to handle various fabrics and robust in handling deformations that may change fabric's shape due to buckling.

The experiments show that the proposed robotic system is flexible enough to handle various fabrics and robust in handling deformations that may change fabric's shape due to buckling.

It is not possible to cover all the aspects of robot handling of flexible materials in this paper, since there are still several related issues requiring solutions. Considering the future research work, the proposed approach can be extended to sew fabrics with curved edges and correcting the distortions presented during robot handling of fabrics.

The paper includes implications for robot handling a variety of fabrics with low and medium bending rigidity on a working table. The intent of this paper deals with buckling in context of achieving a successful seam tracking and not the correction strategy against folding or wrinkling problems.

This paper fulfils an identified need to study the fabrics' behavior towards robot handling on a working table.