No abstract

Based on the partial feedback linearization, a novel nonlinear controller is analyzed and designed for three dimensional motion of overhead crane. Three control inputs composed of b ridge moving, trolley travelling, and cargo hoisting forces are used to drive five state variables consisted of bridge motion, trolley movement, cargo hoisting displacement, and two cargo swing angles. The control scheme is constituted by linearly combining two components: one is got from the nonlinear feedback of actuated states and the other is received from that of un-actuated states. To verify the quality of control process, the numerical simulation is executed. The received results show that the proposed controller asymptotically stabilizes all system states.

In this paper, an Internet-based snow-cover monitoring system is developed which analyzes visual image of drifted snow and measures the amount of snowing. The system is composed with a reference ruler, a CCD camera, and a PC with an image processing unit. The snow-depth is estimated based on the position of the lowest uncovered marker on the ruler which is extracted from the captured image. An image processing algorithm is devised to obtain proper resolution and good accuracy of the uncovered marker position in spite of the week intensity of ambient light and disturbances from the light rays of outdoors. The system also acts as monitoring server, which transfers the raw images in real-time to a remotely located client using TCP/IP. With bi-direction communication through Internet, users can adjust the orientation of the camera view. Experimental results show that this system improves reliability and accuracy of measurement of drifted snow.

No abstract

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.