基于cmos攝像頭的智能車路徑識別與方向控制畢業(yè)論文外文翻譯

1、英文原文 英文原文Route Identification and Direction Control of Smart Car Based on CMOS Image SensorAbstractThis paper is designed for the 2nd Freescale Cup National Undergraduate Smart Car Competition. With MC9S12DG128 single c

2、hip and smart car model supplied by the committee, a CMOS image sensor is applied to detect the black track on white raceway, which extends the detection range and is helpful to predict the forward path. In this paper,

3、 ten-line pixels in an image are analyzed to locate the black track, and the PD algorithm based on PID is employed to control the direction and angle of the steering gear respectively. By repeated testing, the smart car

4、can run stably on the given raceway at a high speed.Keywords: route identification, direction control, smart car, MC9S12DG128 single chip, image sensor, PID algorithm.1. IntroductionThe rules of 2nd Freescale Cup Nationa

5、l Undergraduate Smart Car Competition [1] may be summarized as follows: the raceway consists of a lot of white boards on which a black track is attached; the smart car designed by participants runs along the black track

6、;every car runs two circles in this game and the best times of two circles will be the final score of this car, and apparently the team whose car takes the best times will bear the palm. According to the rules, we should

7、 ensure that the car can distinguish the black track from white board in order to make the smart car run stably. There are two common methods for route identification: one is using infrared diode as the sensor, and anot

8、her is using CCD/CMOS image sensor [2]. This paper using CMOS image sensor as route identification sensor, the reasons for which are as follows: (1) The range which is covered by a infrared diode sensor is much smaller

9、than a CMOS image sensor covers, and only we can do is to use several diode sensors, but the maximum number of diode sensors used in the smart car is 16; (2) The working voltage of a CMOS image sensor(3.3V) is less than

10、 a CCD(12V) or 16 infrared diodes. Apparently, using CMOS image sensor can not only reduce the power consumption but also extend the visible range of the smart car, and also enable the car to predict the forward path.

11、This paper presents a systemic solution for identifying the raceway and controlling the direction of smart car. 2. CMOS cameraThere are several kinds of CMOS image sensors in the market. In comparison with other CMOS im

12、age sensors, the OV6130 CMOS image sensor [3] made by OmniVision Technologies Inc. is the best choice for us to design a CMOS camera for smart car whether from the viewpoint of cost and performance or power consumption.

13、 The OV6130 is a black and white sensor which has a 1/4 inch CMOS imaging device containing approximately 101,376 pixels (352×288). This sensor includes a 356×292 resolution image array, an analog signal proc

14、essor, dual 8-bit A/D converters, analog (a) Smart car ready to scan the raceway(b) Captured image by CMOS cameraFigure 3 Comparison between original image and captured image3. Route identificationRoute identification ai

15、ms at helping the smart car to recognize the forward track by a method which picks up the black line from the image captured by CMOS camera,and in fact, this method works well in the following cases:straight line, curvin

16、g line and snake line. By repeated testing, we decide to analyze 10 lines of a whole image to predict the forward condition of smart car. Figure 4 illustrates how we analyze the 10-line pixels of an image. Figure 4 Rout

17、e identification diagramThe detailed algorithm is introduced as follows:Step 1: Calculate coordinates of the black pixel for each line ready to be analyzed. As is illustrated in figure 4, the lines (L0, L1, …, L8, L9) a

18、re to be analyzed, and the white points (P0, P1, …, P8, P9) are black pixels for each line. The origin O is superposed by P9, which means there is no black pixel in line L9. Assumed that P(x) and P(y) indicate x-coordi

19、nate and y-coordinate of point P,respectively, here both P9(x) and P9(y) equal 0.The key of this step is to find the black pixel of each line. Here, by taking the following datum which shows the gray values of all pixel

20、s in a line as example, we introducea new approach: 195 210 207 215 208 228 236 243 238 234 238 235 231 233 230 235 230 222 196 207 204 208 209 129 160 65 17 15 19 18 79 151 172 153 173 150 147 159 141 153 147 154 137