Ar 이온 식각법을 이용하여 (001) SrTiO3(STO) 단결정 기판 위에 200nm 높이의 계단형 모서리를 제작하였다. 계단식은 입사하는 Ar 이온 빔에 대한 Ar 이온 입사각과 마스크 회전각을 조절함으로써 38˚-70˚의 넓은 범위로 제어할 수 있었다. 초전도 YBa2Cu3O7-δ박막은 계단이 있는 STO 기판 위에 펄스레이저 증착법을 이용하여 증착하였으며, 박막의 두께는 계단 높이에 대한 박막의 두께비가 0.5-1.2가 되도록 하였다. 계단형 모서리 조셉슨 접합의 임계전류밀도와 IcRn값은 77K에서 각각 104A/cm2, 70-200μV이었다.

본 연구에서는 금속 칼날 마스크와 펄스형 레이저 증착장치를 이용하여 YBa2Cu3O7-δ/SrTiO3/YBa2Cu3O7-δ 다층박막 형태의 In-situ SIS ramp dege 형태의 접합을 제작하였으며, 이의 특성을 조사하였다. 접합은 RSJ 형태의 전류-전압 특성ㅇ르 나타내고 있으며, 온도 변화에 따른 접합의 normal resistance는 약 18 Ω으로 온도에 무관하게 일정한 값을 나타내었다. 접합형태를 이용하여 감도(transfer function, dV/dφ)가 약 22μV/φ0인 dc-SQUID센서를 제작하였으며, in-situ SIS ramp edge 형태의 접합이 센서로의 응용가능성을 충분히 가지고 있음을 보여 주었다.

Based on the stability criteria of ZMP (Zero Moment Point), this paper proposes an adjusting algorithm that modifies walking trajectory of a bipedal robot for stable walking by analyzing ZMP trajectory of it. In order to maintain walking balance of the bipedal robot, ZMP should be located within a supporting polygon that is determined by the foot supporting area with stability margin. Initially tilting imposed to the trajectory of the upper body is proposed to transfer ZMP of the given walking trajectory into the stable region for the minimum stability. A neural network method is also proposed for the stable walking trajectory of the biped robot. It uses backpropagation learning with angles and angular velocities of all joints with tilting to get the improved walking trajectory. By applying the optimized walking trajectory that is obtained with the neural network model, the ZMP trajectory of the bipedal robot is certainly located within a stable area of the supporting polygon. Experimental results show that the optimally learned trajectory with neural network gives more stability even though the tilting of the pelvic joint has a great role for walking stability.

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.