This study was conducted to investigate the movement and home range of the red-tongued viper snake (Gloydius ussuriensis) from June 2006 to June 2009. This snake species inhabits an islet on Jeju Island, Gapado. A total of 132 individual snakes were marked during the study. Among the marked individuals, the number of snakes recaptured more than once was 22 (16.8 %) and the number of individuals recaptured more than twice was eight (6.1 %), indicating a relatively low recapture rate. The durations from capture to recapture varied from 1 to 710 days. However, the capture points were not much different, indicating that the moved distance of snakes and the interval between capture-recapture were not correlated. The home ranges of the Red-tongued viper snakes calculated from data of the snakes which were captured more than three times using the MCP(minimum convex polygon) method were 8∼167 ㎡(64.0±57.0 ㎡), suggesting that this snake is relatively sedentary. Home range size differences between female (Mean=62.0 ㎡) and male (Mean=66.0 ㎡) snakes were not significant. In the red-tongued viper population of Gapado, there was no statistically significant relationship between body size and home range size although it was positively correlated (r=0.675). Our results provide valuable data to understand life patterns of the red-tongued viper snakes and will be useful when conducting further ecological studies on other snake species.

Many intelligent robots have to be given environmental information to perform tasks. In this paper an intelligent robot, that is, a cleaning robot used a sensor fusing method of two sensors: LRF and StarGazer, and then was able to obtain the information. Throughout wall following using laser displacement sensor, LRF, the working area is built during the robot turn one cycle around the area. After the process of wall following, a path planning which is able to execute the work effectively is established using flow network algorithm. This paper describes an algorithm for minimal turning complete coverage path planning for intelligent robots. This algorithm divides the whole working area by cellular decomposition, and then provides the path planning among the cells employing flow networks. It also provides specific path planning inside each cell guaranteeing the minimal turning of the robots. The proposed algorithm is applied to two different working areas, and verified that it is an optimal path planning method.