In recent years, the realization of human-coexistence robot for housework and care is expected. The human-coexistence robot, works around human, frequent contact with the human being is expected. Therefore, it is necessary to perform risk assessment beforehand. However, the statistics data of the past accident does not exist because the human-coexistence robot is a new device. In addition, it is also difficult to identify the working method and environment. Therefore, quantitative risk assessment cannot be performed by the conventional technique of risk assessment. In this research, the quantitative exposure frequency evaluation technique is proposed to estimate the collision risk of the human-coexistence robot that works under the environment for human activity. In this study, it is assumed that highly developed human-coexistence robot has the same as the human form, and works with the same way as human beings. And quantitative exposure frequency evaluation is performed by analyzing the movement of the human being who imitated the robot. For verification of the proposed method, the authors have performed an evaluation experiment on exposure frequency of human-type robot to perform the cleaning work, and show the experimental results.

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The current stage of the development of academic systems for education in robotics may be considered to be at a threshold. In order to formulate academic systems for robotics education, researchers need to evaluate the effectiveness and influence of robotics education, objectively or quantitatively, in their research studies. In this study, the authors quantitatively evaluated the educational effects of a robotics classroom for elementary school children in the 5th and 6th grades using psychological measure and statistical analysis. Pre- and post-questionnaire surveys were conducted using a questionnaire that had been used and validated in other studies, and two-factor analysis of variance was applied to the results of the questionnaire. From the results, the following findings are reported. (1) The interest in making things by the group who had little experience making things increased more than the group who did have such experience. (2) The boys group generally had more interest in making things than the girls group. (3) Participants in the 5th grade had greater willingness to do something creative, and in acquiring the skill to use a tool, than did those in the 6th grade. (4) Participants in the 6th grade wanted to create something more specific than did those in the 5th grade. Overall, it was quantitatively shown that the children participating in the robotics classroom originally had a high interest in making things. Moreover, their interest in making things improved through their participation in the robotics classroom.

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There was not a guideline available for researchers, developers or users for robots or heavy construction machines on the evaluation of radiation tolerance and management method of robots and heavy construction machines using semiconductors, like as CPUs on the shelf, under radiation condition, when Fukushima daiichi NPPs accidents occurred on March 11th, 2011. The evaluation and the management method became necessary, in order to deploy robots like as QUINCE developing for big city accidents or unmanned heavy construction machines for landside disaster. According to “radiation tolerance data base on parts or materials” developed in 1980's to 1990's by Japan Atomic Energy Agency (JAEA), a guideline, for robots and unmanned heavy construction machines, was tentatively developed.

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This paper describes a novel method of traversable region estimation in a variety of scenes. Our estimation targets are traversable areas where human selects in common sense: e.g. the side of a sidewalk and an animal trail. In the proposed method, a scene image is first segmented into categories such as roads and grass, and then traversable regions are estimated by considering the layout of the categories. In the region estimation, human common sense modeled using learning data is used. In learning phase, the operator instructs possible paths, for instance, “moving on the sidewalk along guardrails”, “crossing a crosswalk”, and “avoiding obstacles”. Specifically, the operator draws polygonal lines as paths on each image. In this phase, one important thing for the operator is to consider traffic rules, object avoidance, accident prevention and so on. Since traversable regions are estimated based on a learning result including operators' common sense, they contain generalized common sense which is suitable for mobile robots moving in everyday environments. Experiment shows that this method is successful in generating the correct estimation in a variety of scenes.

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