• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.40 no.5
• /
• pp.312-321
• /
• 2003

In this paper, we implement robot which are ability to recognize obstacles and moving automatically to destination. we present two results in this paper; hardware implementation of image processing board and software implementation of visual feedback algorithm for a self-controlled robot. In the first part, the mobile robot depends on commands from a control board which is doing image processing part. We have studied the self controlled mobile robot system equipped with a CCD camera for a long time. This robot system consists of a image processing board implemented with DSPs, a stepping motor, a CCD camera. We will propose an algorithm in which commands are delivered for the robot to move in the planned path. The distance that the robot is supposed to move is calculated on the basis of the absolute coordinate and the coordinate of the target spot. And the image signal acquired by the CCD camera mounted on the robot is captured at every sampling time in order for the robot to automatically avoid the obstacle and finally to reach the destination. The image processing board consists of DSP (TMS320VC33), ADV611, SAA7111, ADV7l76A, CPLD(EPM7256ATC144), and SRAM memories. In the second part, the visual feedback control has two types of vision algorithms: obstacle avoidance and path planning. The first algorithm is cell, part of the image divided by blob analysis. We will do image preprocessing to improve the input image. This image preprocessing consists of filtering, edge detection, NOR converting, and threshold-ing. This major image processing includes labeling, segmentation, and pixel density calculation. In the second algorithm, after an image frame went through preprocessing (edge detection, converting, thresholding), the histogram is measured vertically (the y-axis direction). Then, the binary histogram of the image shows waveforms with only black and white variations. Here we use the fact that since obstacles appear as sectional diagrams as if they were walls, there is no variation in the histogram. The intensities of the line histogram are measured as vertically at intervals of 20 pixels. So, we can find uniform and nonuniform regions of the waveforms and define the period of uniform waveforms as an obstacle region. We can see that the algorithm is very useful for the robot to move avoiding obstacles.

• Journal of Animal Environmental Science
• /
• v.17 no.2
• /
• pp.71-80
• /
• 2011

Trickling filter has been extensively studied for the domestic wastewater treatment especially for the small scale plants in rural area. The purpose of this research is to survey the physical and microbial characteristics of wood chip media and the removal efficiency of animal wastewater using wood chip trickling filter system. The trickling filtration system comprises a filtration bed packed with wood chip media having a particle dia. of 5~7cm. The method comprises natural air from the bottom of the bed. The system also comprises a control mechanism including a time a constant discharge pump for controlling supply of the wastewater into the bed. The following conclusions were obtained from the results of this research. 1. The specific surface area of wood chip was 0.4123 $m^2$/g, pore volume was 0.0947 $cm^3$/g, density was 0.49 g/$cm^3$. It has forms of parallelogram and oblong which have numerous small pore space. This wood chip has been good condition for microorganism's habitat, having very larger specific surface area by complex the three dimension structure of cellulose at wood's major ingredients. 2. The total counts of in attached aerobic microbes were ranged from $10^6$ to $10^8$ CFU/g, and anaerobes microbial numbers were from $10^4$ to $10^7$. The aerobic microbial numbers appeared to be much more than those of anaerobic microbial numbers. 3. The average efficiency of $BOD_5$ and CODcr were 74.5% and 51.5%, respectively. The removal efficiency of T-N and T-P were 61.4%, 56.2%, respectively. But SS removal levels remain 19.3%.