Drape is the ability of a fabric to hang in folds when suspended under its own weight as shown in the skirt folds (Sanad, Cassidy, Cheung, & Evans, 2013). Drape characteristics of a fabric is closely related to the physical and mechanical properties including bending rigidity, weight, and shear rigidity of the fabric. Fiber type, yarn structure, fabric weave structure, and finishing methods also affect the fabric drape. Occasionally, fabric drape is subjectively evaluated by the staffs in the case of apparel sectors. Since the staff’s evaluation might involve some degree of inconsistencies, partly due to the personal preference, and fashion trend changes, or lack of reproducibility, many research reports have been published regarding the methods to measure the fabric drape characteristics objectively and accurately. A pioneering method regarding objective tests to measure fabric bending length, as a measure for fabric draping, was developed (Peirce, 1930). Research by Schwarz (1939) showed technical evaluation method for fabrics treated with finishing agents. Chu, Cummings, and Teixeira (1950) developed a drape meter to study the factors affecting the fabric drape, based on an optical system to cast the image of round fabric specimen on the ground glass. Generally accepted test methods have enabled researchers determine the fabric drape with improved reproducibility to mostly acceptable degrees (Cusick, 1968). However, the three dimensional shape of the folded structure often deforms with time or with subtle vibration around the fabric specimen during the drape measurement. Due to the uneven and complex nature of fabrics, the overall shape of the fabric specimen on the drape tester often becomes unstable. Since the fabric drape coefficient is more or less unstable due to the structural or physical factors of fabric specimens, such as bending and shear hysteresis, it is also important to consider the instabilities during the drape measurement procedure. Niwa and Morooka (1976) reported mechanical values contributing to the stability of the drape coefficient, and found that the larger the hysteresis per unit weight in bending deformation of fabric is, the larger the instability of the drape coefficient becomes for the fabric specimens used for men’s suit in the study. There is a need to understand the fundamental mechanisms of how draping may generate pleasing forms. Mizutani, Amano, and Sakaguchi (2005) devised a new apparatus for measuring the changes during the whole process of drape formation, using a type of drape elevator. They considered the generation of nodes and the developing process in relation to the mechanical properties of the fabric specimens. Mah and Song (2010) investigated fabric drape employing three-dimensional body scanning system. Laser scanners, in the system, project a horizontal line of light on the object, moving vertically along the length of the draped specimen. The scanned image can be rotated, resized and sliced. Recently three-dimensional scanning systems saw notable developments in the hardware and computing power. Henry, Krainin, Herbst, Ren, and Fox (2010) relied on depth sensor cameras for dense 3D modeling of indoor environments. RGB-D cameras rely on either structured light patterns combined with stereo sensing, or time-of-flight laser sensing, allowing relatively fast image acquisition, which capture RGB images along with per-pixel depth information. Commercially available RGB-D camera, such as one of the Prime Sense products based on light coding technology of pseudo-random infrared patterns, allows for the frame acquisition rate of 5 to 20 per second, depending on the configuration of the supporting computer system. The acquisition rate seems to be reasonable for the static fabric drape measurement. Therefore, the RGB-D sensor enables relatively rapid acquisition of the three dimensional information of the fabric drape. In this study, an RGB-D sensor was employed for three-dimensional scanning of fabric drape with drape elevator method proposed by Mizutani et al. (2005), and the drape measurement data were compared with the conventional drape test. Fabric specimens including cotton, linen, silk, wool, polyester, and rayon were investigated for the fabric drape and other physical/mechanical parameters. The results from the study suggest that the drape measurement method using the RGB-D sensor allows relatively rapid acquisition of three-dimensional drape information during the formation of fabric drape in the course of measurement process. It is suggested, however, that further in-depth study would be necessary due to the instability of the depth measurement around the sharp edges of the fabric folds. Future application of improved RGB-D sensor system in terms of the depth sensitivity is also suggested for the comparative study of the drape properties of fabrics employing both the RGB-D system and the conventional drape tester.

The purpose of our sensor system is to transparentize the large hydraulic manipulators of a six-ton dual arm excavator from the operator camera view. Almost 40% of the camera view is blocked by the manipulators. In other words, the operator loses 40% of visual information which might be useful for many manipulator control scenarios such as clearing debris on a disaster site. The proposed method is based on a 3D reconstruction technology. By overlaying the camera image from front top of the cabin with the point cloud data from RGB-D (red, green, blue and depth) cameras placed at the outer side of each manipulator, the manipulator-free camera image can be obtained. Two additional algorithms are proposed to further enhance the productivity of dual arm excavators. First, a color correction algorithm is proposed to cope with the different color distribution of the RGB and RGB-D sensors used on the system. Also, the edge overlay algorithm is proposed. Although the manipulators often limit the operator’s view, the visual feedback of the manipulator’s configurations or states may be useful to the operator. Thus, the overlay algorithm is proposed to show the edge of the manipulators on the camera image. The experimental results show that the proposed transparentization algorithm helps the operator get information about the environment and objects around the excavator.

Multi-floor navigation of a mobile robot requires a technology that allows the robot to safely get on and off the elevator. Therefore, in this study, we propose a method of recognizing the elevator from the current position of the robot and estimating the location of the elevator locally so that the robot can safely get on the elevator regardless of the accumulated position error during autonomous navigation. The proposed method uses a deep learning-based image classifier to identify the elevator from the image information obtained from the RGB-D sensor and extract the boundary points between the elevator and the surrounding wall from the point cloud. This enables the robot to estimate the reliable position in real time and boarding direction for general elevators. Various experiments exhibit the effectiveness and accuracy of the proposed method.

Obstacle avoidance is one of the most important parts of autonomous mobile robot. In this study, we proposed safe and efficient local path planning of robot for obstacle avoidance. The proposed method detects and tracks obstacles using the 3D depth information of an RGB-D sensor for path prediction. Based on the tracked information of obstacles, the paths of the obstacles are predicted with probability circle-based spatial search (PCSS) method and Gaussian modeling is performed to reduce uncertainty and to create the cost function of caution. The possibility of collision with the robot is considered through the predicted path of the obstacles, and a local path is generated. This enables safe and efficient navigation of the robot. The results in various experiments show that the proposed method enables robots to navigate safely and effectively.