The International Organization for Standardization (ISO) has issued ISO 13482, which is a new global safety standard for personal care robots and is the key to these safety for human. However, it is a conceptual standard, which does not have concrete standard values. Moreover, when protective measures for risk reduction are implemented using the control system, it has to be developed based on functional safety standard, which is difficult to interpret. Therefore it is very difficult to understand concretely how to design and evaluate personal care robots in order to meet this standard. At such times, we acquired the certification of ISO 13482 in the robotic care equipment which is integration of an electric care bed and an electric reclining wheelchair. This paper reports a concrete example of how to develop and evaluate robotic care equipments based on ISO 13482.
This paper reports a robot which interacts with children semi-autonomously in a science room of an elementary school to help children's understanding of science classes. The robot asked children questions related to their science classes during breaks between science classes; children could freely interact with it during experiment periods. We implemented a personal identification function to the robot by using a face recognition system with robot's camera and a human tracking system with environmental sensors. Still, speech recognition is difficult in noisy elementary school environment; therefore we decided to take over speech recognition function by the operator. In this study our result did not show significant effects of the robot existence for helping children's understanding, but we found that children who joined to the robot's quiz more than a certain time increased understanding toward a specific unit.
This paper presents a multi-robot control method considering a mobile ad-hoc network connectivity and inter-robot collision avoidance. A method I studied previously was based on a receding horizon control (RHC) method, and was decentralized one by some kind of serialization. However, the previous method remains some disadvantages; especially, no-scalability and huge computations. Because these disadvantages are critical flaws for multi-vehicle systems, I strive to improve the method in this paper. By a distributed priority variable, several vehicles (but not all vehicles) can make their decisions at same time in the proposed method. We show the proposed method in this paper is less computation than the previous method.
Robot software platforms have entered to the next stage in the business viewpoint. Robot software components are becoming readily available. Henceforth, the comfortable developing environment for non-experts is essential for spreading robot-services, which integrates various complex Robot technologies with each application field. In this paper, we propose a robot-software-framework that is useful to a variety of stakeholders who participate in the robot service development．Specifically, our proposal concept is comprised of three principal layers: execution environment integration, API integration and component technologies integration. Additionally, we present a methodology in order to achieve our concept. Along this methodology，we re-position RSI's existing technologies, also develop new component technologies necessary, prototype a verification system and show the effectiveness of our concept, finally．
Robotic drivers are used in vehicle performance tests such as fuel consumption. A test vehicle is driven on a dynamometer for driving test cycles with a defined set of time and speed. To accurately compare fuel consumption of various vehicles, it is necessary to approach closer the target speed by better control performance. However, it is difficult to realize better control performance because a vehicle is a controlled system that has dead times and large model errors. In this paper, we designed a control system that realizes better control performance for the controlled system with dead times and large model errors. First, we built the driver model from the vehicle characteristics. We then designed a control system that permits the modeling error. The speed control performance of the proposed control system was confirmed by vehicle running tests with robotic driver.