Tableware Tidying-Up Robot System for Self-Service Restaurant-Detection and Manipulation of Leftover Food and Tableware.

In this study, an automated tableware tidying-up robot system was developed to tidy up tableware in a self-service restaurant with a large amount of tableware. This study focused on sorting and collecting tableware placed on trays detected by an RGB-D camera. Leftover food was also treated with this robot system. The RGB-D camera efficiently detected the position and height of the tableware and whether there was leftover food or not by image processing. A parallel arm and robot hand mechanism was designed to realize the advantages of a low cost and high processing speed. Two types of rotation mechanisms were designed to realize the function of throwing away leftover food. The effectiveness of the camera detection system was verified through the experiments of tableware and leftover food detection. The effectiveness of the prototype robot and the rotation assist mechanism was verified through the experiments of grasping tableware, throwing away leftover food by two types of rotating mechanisms, collecting multiple tableware, and the sorting of overlapping tableware with multiple robots.

Fluid Pressure Monitoring-Based Strategy for Delicate Grasping of Fragile Objects by A Robotic Hand with Fluid Fingertips.

In this study, we propose a strategy for delicately grasping fragile objects using a robotic gripper with highly deformable fluid fingertips. In an earlier study, we developed a soft fingertip, referred to as a fluid fingertip, which was fabricated from a deformable rubber bag filled with incompressible fluid. The proposed strategy detects the preferable grasping point where fracturing of the target object is avoided while the applied force or pressure from the fluid fingertip is substantially transmitted to the target. In this grasping point, the behavior of the fluid pressure changes with respect to the pushing distance when pushing at a constant speed. The other features of the strategy determining the grasping point are as follows: (1) the threshold for the detection of the grasping point is fixed or constant with respect to the type of target object; (2) information regarding the deformation and stiffness of the fingertips and target object is not required. The detection of the grasping point through behavioral changes in the pressure is performed by comparing the fitting accuracies of fitting models utilizing information of the fluid pressure and pushing distance. The validity of the proposed approach is verified through several experiments.

Fast modified Self-organizing Deformable Model: Geometrical feature-preserving mapping of organ models onto target surfaces with various shapes and topologies.

BACKGROUND AND OBJECTIVE: This paper proposes a new method for mapping surface models of human organs onto target surfaces with the same genus as the organs. METHODS: In the proposed method, called modified Self-organizing Deformable Model (mSDM), the mapping problem is formulated as the minimization of an objective function which is defined as the weighted linear combination of four energy functions: model fitness, foldover-free, landmark mapping accuracy, and geometrical feature preservation. Further, we extend mSDM to speed up its processes, and call it Fast mSDM. RESULTS: From the mapping results of various organ models with different number of holes, it is observed that Fast mSDM can map the organ models onto their target surfaces efficiently and stably without foldovers while preserving geometrical features. CONCLUSIONS: Fast mSDM can map the organ model onto the target surface efficiently and stably, and is applicable to medical applications including Statistical Shape Model.

An informationally structured room for robotic assistance.

The application of assistive technologies for elderly people is one of the most promising and interesting scenarios for intelligent technologies in the present and near future. Moreover, the improvement of the quality of life for the elderly is one of the first priorities in modern countries and societies. In this work, we present an informationally structured room that is aimed at supporting the daily life activities of elderly people. This room integrates different sensor modalities in a natural and non-invasive way inside the environment. The information gathered by the sensors is processed and sent to a centralized management system, which makes it available to a service robot assisting the people. One important restriction of our intelligent room is reducing as much as possible any interference with daily activities. Finally, this paper presents several experiments and situations using our intelligent environment in cooperation with our service robot.

Floor sensing system using laser reflectivity for localizing everyday objects and robot.

This paper describes a new method of measuring the position of everyday objects and a robot on the floor using distance and reflectance acquired by laser range finder (LRF). The information obtained by this method is important for a service robot working in a human daily life environment. Our method uses only one LRF together with a mirror installed on the wall. Moreover, since the area of sensing is limited to a LRF scanning plane parallel to the floor and just a few centimeters above the floor, the scanning covers the whole room with minimal invasion of privacy of a resident, and occlusion problem is mitigated by using mirror. We use the reflection intensity and position information obtained from the target surface. Although it is not possible to identify all objects by additionally using reflection values, it would be easier to identify unknown objects if we can eliminate easily identifiable objects by reflectance. In addition, we propose a method for measuring the robot's pose using the tag which has the encoded reflection pattern optically identified by the LRF. Our experimental results validate the effectiveness of the proposed method.

Estimation of brain internal structures by deforming brain atlas using finite element method.

This paper presents a method for estimating the internal structures of a patient brain by deforming a standard brain atlas. Conventional deformation methods need several landmarks from the brain surface contour to fit the atlas to the patient brain shape. However, since the number and shapes of small sulci on the brain surface are different from each other, the determination of the accurate correspondence between small sulcus is difficult for experienced neurosurgeons. Moreover, the relationship between the surface shape and internal structure of the brain is unclear. Therefore, even if the deformed atlas is fitted to the patient brain shape exactly, the use of the deformed atlas does not always guarantee the reliable estimation of the internal structure of the patient brain. To solve these problems, we propose a new method for estimate the internal structure of a patient brain by the finite element method (FEM). In the deformation, our method select the landmarks from the contours of both the brain surface and the detectable internal structures from MR images.