Many robots are pervading environments of human daily life. It is crucial for those robots to estimate the current self-positions. This is called robot localization. In this paper, we propose a method using a recurrent convolutional neural network (RCNN), which is known as one of deep learning, to achieve robot localization. RCNN is a neural network model that has a convolutional architecture known as CNN with recurrent nodes. We train RCNN to estimate the current position of a robot from the view images of the first person perspectives. Our experimental results show that the estimation error decrease when the successive view images are given and it can estimate the current position accurately.
This study proposed a self organizing method to localize self position and to define the final destination for area search of swarm-robot without any bird's-eye camera or landmark. Simulation results showed that robots searched an area with only ID and distance, and they covered the area uniformly.
In this paper, we propose a method to index for present condition of utilizing information technology of prefectural medical associations. Additionally, we report, as a case study, the results of the application of the proposed method to survey and analysis of Hokkaido Medical Associations and indexing them.
The method involves surveying the present situation of each medical association in terms of the three points of view, i.e., infrastractures (I), skill (S) and mind (M). We propose four indices to verify the present situation of medical associations, i.e., ISM index, ISM±index, l-u index and l-u ratio index.
The proposed method was applied to carry out survey of Hokkaido Medical Associations. The result reveals wide applicabilty of the method.