Urchin-structured zinc oxide(ZnO) nanorod(NR) gas sensors were successfully demonstrated on a polyimide(PI) substrate, using single wall carbon nanotubes(SWCNTs) as the electrode. The ZnO NRs were grown with ZnO shells arranged at regular intervals to form a network structure with maximized surface area. The high surface area and numerous junctions of the NR network structure was the key to excellent gas sensing performance. Moreover, the SWCNTs formed a junction barrier with the ZnO which further improved sensor characteristics. The fabricated urchin-structured ZnO NR gas sensors exhibited superior performance upon NO2 exposure with a stable response of 110, fast rise and decay times of 38 and 24 sec, respectively. Comparative analyses revealed that the high performance of the sensors was due to a combination of high surface area, numerous active junction points, and the use of the SWCNTs electrode. Furthermore, the urchin-structured ZnO NR gas sensors showed sustainable mechanical stability. Although degradation of the devices progressed during repeated flexibility tests, the sensors were still operational even after 10000 cycles of a bending test with a radius of curvature of 5 mm.

The key motivation of this study is for a style of the sensor arrangement to have an effect on the localization performance of mobile robots in case of using sonar sensors. In general robot platforms with sonar sensors, sonar sensors are supposed to be radially arranged on their rotational axis of mobile robots. However, relevant limits to several functions required for their autonomous navigation occur unexpectedly, because a sonar sensor generally has the negative nature of its wide beam width together with the specular reflection. We present a new strategy of the sonar sensor arrangement capable of enhancing the localization performance. Sonar sensors are intended to be arranged nonradially (twistedly expressed in this paper) on their rotational axis. The localization scheme called STARER: Sonar-Twisted ARrangement localizER is based on the extended Kalman filter (EKF) with occupancy grid maps. Experimental results demonstrate the validity and robustness of the proposed method for the localization of mobile robots.