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Human can recognize and handle (pick and place) easily the objects with a variety of different shapes, colors, sizes, and humans' eyes are adaptable to various light environments with a certain tolerance. However, it is difficult for robots to recognize deformable objects such as cloth, string, etc., especially if an object is unique. Additionally, there have been difficulties for robots with vision sensors (cameras) to accurately detect and handle objects under various light environments. This paper proposes a cloth handling system that recognizes an unique cloth appeared in front of a robot by a photo-model-based approach. The photo-model-based approach has been adopted since the photo-model can be made at once by taking a photo of the unique cloth. In proposed cloths' pose estimation method, a photo-model projected from 3D to 2D is used, where this system does not need defining the object's size, shape, design, color and weight. It detects the cloth through model-based matching method and Genetic Algorithm (GA). The handling performance by the proposed method with dual-eyes cameras has been verified, revealing that the proposed system has leeway to recognize and handle the unique cloth in lighting varieties from 100 lx to 1300 lx. In addition, 3D recognition and handling accuracy have been confirmed to be practically effective by conducting the recognition/handling experiments under different light conditions.Keywords : Photo-model-based cloth recognition, Handling, Visual servoing, Genetic Algorithm, Dual-eyes cameras, Illumination IntroductionFrom the instant of birth, human beings are thought to be talented at managing their activities under such variability as climates, light environments, temperatures, etc. While human beings can conduct intended tasks in pending circumstances, an automated robot is not adept at being similarly adaptable. Therefore, the researchers have tried to improve the abilities of automated robots.Nowadays, industrial robots have been utilized to perform a wide variety of tasks instead of human workers. These automated robots are required to handle a wide variety of deformable objects including cloths, strings, ropes, electric cables and so on. Of course, handling deformable objects is difficult than handling rigid objects. A robot control technology using visual information, called as visual servoing, has been playing an important role in the applications where deformable things are recognized and handled by a robot.Each item of the deformable target objects has various possibilities of the poses (positions and orientations) to be recognized and handled, requiring ability with respect to both vision-based recognition and visual servoing. In (MaitinShepard J et al., 2010), the cloth-grasping points are detected using four cameras without using other sensors for a towel folding application by robots. The main task in (Maitin-Shepard J et al., 2010) is to detect the corners of cloth instead of recognition a whole cloth. The recognition of cloth shape based on strategic observ...

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Human can recognize easily the different kinds of object. However, it is difficult for robots recognize the objects such as cloth, string, moving objects and etc. This paper is to develop the cloth recognition scheme for visual servoing to apply it in industries where workers recognize clothes and package them to send internet users who have purchased. In this study, the 7-link manipulator equips with two cameras that detect position and orientation (pose) of cloth using 3D-MoS (Move on Sensing). The proposed system introduces a new model generating method for model-based cloth recognition that utilizes a photograph of cloth as a model to recognize the cloth, which enable that the robotic system does not need to prepare a definition of the target cloth as a preparation of the recognition. It has been confirmed that cloth can be recognized by the system through 1000 times recognition experiment.