Conventional strain sensors measure strains exerted on solid metals and have been widely applied. Stretch measurements of flexible objects require strain sensors with wide dynamic range (stretch exceeding 100%) that can also measure areal changes. Flexible strain sensors are expected to realize a wide range of technologies, such as human interfaces, smart clothes, skin-motion monitoring, and robotic skin. Recently, carbon nanotubes (CNTs) have been assembled into stretchable conductors, and are potential base materials for various flexible sensors. Herein, we construct a flexible stretching sensor from urethane elastomer and conductive electrodes from singlewalled CNTs. This sensor is extremely thin (thickness: 150 µm), and characterized by high elasticity (up to 100%), low stress (0.8 MPa at 100%), durability (1000 cycles at 50%), light weight (approx. 1.1 g/cm 3 ), and sensitivity (1 pF/mm 2 ). The strain sensor is tested on a cloth fabric, and is confirmed to measure the stretch area of flexible materials.Index Terms-Capacitive sensor, thin film sensor, strain sensor, carbon nanotubes.
The purpose of this study is to develop partner robots that can obtain and accumulate human-friendly behaviors. To achieve this purpose, the entire architecture of the robot is designed, based on a concept of structured learning which emphasizes the importance of interactive learning of several modules through interaction with its environment. This paper deals with a trajectory planning method for generating hand-to-hand behaviors of a partner robot by using multiple fuzzy state-value functions, a self-organizing map, and an interactive genetic algorithm. A trajectory for the behavior is generated by an interactive genetic algorithm using human evaluation. In order to reduce human load, human evaluation is estimated by using the fuzzy state-value function. Furthermore, to cope with various situations, a self-organizing map is used for clustering a given task dependent on a human hand position. And then, a fuzzy state-value function is assigned to each output unit of the self-organizing map. The robot can easily obtain and accumulate human-friendly trajectories using a fuzzy state-value function and a knowledge database corresponding to the unit selected in the selforganizing map. Finally, multiple fuzzy state-value functions can estimate a human evaluation model for the hand-to-hand behaviors. Several experimental results show the effectiveness of the proposed method.
This paper is concerned with stochastic inverse methodology arising in electromagnetic imaging. Nondestructive testing using guided microwave covers wide range of industrial applications including early detection of anomalies in supraconducting materials. Our focus in this paper is in the identification of electromagnetic material parameters and emphasis is on one spatial dimensional scattering problems on dielectric slabs.
We have developed a universal robot hand with tactile and other sensors. An array-type tactile sensor is crucial for dexterous manipulation of objects using a robotic hand, since this sensor can measure the pressure distribution on finger pads. The sensor has a very high resolution, and the shape of a grasped object can be classified by using this sensor. The more the number of measurement points provided, the higher the accuracy of the classification, but with a corresponding lengthening of the measurement cycle. In this paper, the problem of slow response time is resolved by using software for an array-type tactile sensor with high resolution that emulates the human sensor system. The validity of the proposed method is demonstrated through experiments.
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