Considering that robot-assisted activities spread to ordinary homes, this study was conducted as a part of acquiring the information on the relationship between users' characteristics, reactions from a robot, and the effects of those reactions. I compared the influence of users' personality traits on psychological stress relief effects obtained by interacting with tolerant, rejective, and emotional reaction robots to approach from users. As a result, I confirmed that agreeableness and neuroticism personality traits have significant influences on relaxing the fatigue obtained by tolerant reactions from a robot and disarming the anger obtained by emotional reactions from a robot, respectively. I also confirmed that improving the vigor obtained by rejective reactions from a robot could not be expected independently of users' personality traits. These results suggest that it should avoid using the robot with a high ratio of rejective reactions in any robot-assisted activities. Besides, it is better to use the robot with a high ratio of tolerant reactions if a user with high agreeableness needs effects of relaxing the fatigue, and use the robot with a high ratio of emotional reactions if a user with high neuroticism needs effects of disarming the anger.
Space recognition is one of the most important capabilities for intelligent autonomous robots that work in unknown environments. For realizing the space recognition, Growing Neural Gas (GNG) based approaches have been proposed by many researches since the GNG can learn a space structure and generate a topological structure simultaneously. However, GNG cannot preserve the space information if the input vector is composed of multiple properties. For solving this problem, this paper proposes GNG with Different Topologies (GNG-DT) that has the multiple topological structures according to the number of properties. In addition, the learning result of GNG-DT does not depend on the scale in the input vector. Finally, we conduct on several experiments for evaluating our proposed method by comparing to other conventional approaches, and discuss the effectiveness of our proposed method.
In this paper, we propose a data-driven design of feedback/feedforward controllers to achieve a desired tracking performance for discrete-time constrained linear systems. The proposed methods can handle any time-domain linear constraints on the input, output, and (multiple) internal state variables. Based on the data-driven prediction of such signals, the design problems are reduced to quadratic optimization problems. The effectiveness of the methods is investigated through two numerical simulations of designing a feedback and feedforward controller, respectively.
For a safety diagnosis of RC structures, a microwave radar method has advantages on a nondestructive inner inspection. In this report, novel processing techniques for the microwave radar to improve an inspection accuracy on a buried object survey has been proposed. Specifically, precise and accurate estimation techniques for a propagation time and a reflection phase shift have been considered, based on the time-frequency analysis methods. Also, in order to improve an accuracy of a back projection on a migration process, the improved microwave propagation path model in which an air layer and a substrate layer are newly including has been proposed. Performances of the propagation time and the reflection phase shift estimation method have been confirmed experimentally. Additionally, theoretical propagation times derived from the proposed propagation path model have corresponded to an experimental one well, and validities of proposed both techniques have been shown.