Research on tracking strategy of the hottest doubl

Research on the tracking strategy of double row sensors

Abstract: This paper takes the tracking strategy as the main research object, and takes the smart car with double row sensors as an example to make optimized tracking strategies for different road conditions such as straight line, big turn, s-turn, etc. Experiments have proved that this strategy closely combines the characteristics of dual row infrared, gives full play to the advantages of dual row, and enables the intelligent vehicle to achieve stability first, and pursue the requirements of extreme speed. It has strong adaptability and can play well on all kinds of tracks

key words: double row; Sensors; Path identification; Tracking strategy

advantages of dual row sensors

at present, most intelligent vehicles use the road detection method of single row sensors, which can obtain less road information, and can not distinguish the state of intelligent vehicles from the state of roads, causing trouble in control. In order to make up for the shortcomings, a road detection method with large forward-looking single row sensors has been formed. This method has a longer detection distance and can judge the direction of the road earlier. To a certain extent, it makes up for the shortcomings of low detection accuracy, but it can not effectively distinguish the state of intelligent vehicles from the state of the road

the car models of the race can use cameras or sensors to detect the road information. Our car models use the double row infrared tracking method. Using the large forward-looking double row sensors can get more track information, and adopt strategies earlier to form a better driving track. It is an alternative to complex camera solutions

the ability to achieve stable control and accelerate smoothness in the straight; Advance in a small curve in the s-turn to reduce the travel route and the number of steering gear adjustments. Realize the effect of turning in advance and turning inward in the big turn. Especially in terms of turning, the ability to extend the physical recognition distance is achieved through the joint prediction of the front and rear rows of curves, so as to make an early action, reduce the negative impact caused by the close detection distance, and achieve the above effect

sensor array layout

in Figure 1, the sensor position is only indicated by the receiving tube

Figure 1 sensor array layout

description of layout mode

· the front row sensors extend far, and a large offset will be generated on the front row sensors after the center of the car deviates from the black line

· the extension distance of the rear sensors is relatively close, and a small offset will be generated on the rear sensors after the center of the car deviates from the black line

· the front and rear sensors are used to control the trolley with different sensitivity when the trolley is offset

· most users of industrial and mining enterprises do not have a strong sense of standards. In order to make the front and rear rows reflect a clearer division of labor and collect more distant information, we tilt the front row sensors at an angle of about 45o, so that the forward-looking distance of the front row is larger, which can better reflect the advantages and characteristics of the front row

straight line recognition method and control strategy

straight line recognition method

(1) double row infrared is arranged in this way. For five of the straight lines, which are coherent with the current hot graphene, the structure capacity is generally 1000 lb. the following five physical methods can be used for different methods, and the application time of each method is listed below

the first straight case (Figure 2)

the first straight case in Figure 2

the combination of front and rear sensors that are most likely to detect a black line when turning left after a big turn. It is suitable for turning left at 90 ° and 180 °. Get the turning information in advance, turn the steering gear to the left at a small angle, and take acceleration action at this time to make up for the lack of foresight. In this case, when the s-turn of the track occurs, it does not meet the second recognition method of the straight track, so it will not accelerate

the second straight road situation (Figure 3)

the second straight road situation in Figure 3

this situation is the reconfirmation of the first situation. After turning a big turn in 2017 and passing the first situation, you can experience this situation again, and you can confirm that the straight road ahead is correct, and continue to improve the acceleration ability of the car. The control program is switched from the curve program to the straight-line stability program

the third straight case (Figure 4)

Figure 4 the third straight case

at this time, linear stability control is adopted. Since the first two cases have been clearly identified as straight lanes, this case only increases the success rate of straight lane recognition

the fourth straight road situation (Figure 5)

the fourth straight road situation in Figure 5

is similar to the second situation. For the reconfirmation of the fifth situation, turn right to a big turn and pass the fifth situation, and then experience this situation. It can be confirmed that the straight road ahead is correct, and continue to improve the acceleration ability of the trolley. The control program is switched from the curve program to the straight-line stability program

the fifth straight case (Figure 6)

the fifth straight case in Figure 6

after turning the right big turn, the combination when the front and rear sensors detect the black line is the most likely to occur when coming out of the turn. It is suitable for turning right at 90o turn and 180o turn. Get the turning information in advance, turn the steering gear to the right at a small angle, and take acceleration action at this time to make up for the lack of foresight. When the s-turn of the track appears, it does not meet the second recognition method of the straight track, so it will not accelerate

(2) straight lane recognition, program assisted confirmation

after entering the curve, oscillation will occur with the progress of the trolley, so that the above five conditions may not be met when turning out. In order to improve the success rate of straight line recognition, a second straight line discrimination method is added. Both of them work at the same time. After meeting the first one, it is confirmed to be a straight path after 15ms at most

the program is executed circularly, and our program execution frequency is 2KHz. Using the method of timed interruption (15ms), count the three sensors (numbered 3, 4 and 5) in the middle of the front row with three counters respectively. If one of the sensors detects a black line each time the program is executed, the corresponding counter will be increased by 1. After calculation, the maximum value that can be counted within 15ms is 31. We set the maximum count value. If the required count value is reached within 15ms, it is considered as a straight channel. Switch the straight channel program and clear the counter; If the required count value is not reached within 15ms, the counter is cleared and counted again. For example, the speed of the trolley is 2m/s, and the trolley travels 3cm. We only need to judge the straight path within 2~2.5cm. Therefore, set the maximum count value as 20~25, that is, it is considered as a straight path, and jump out of the curve procedure

of course, a more rigorous method can also be used to judge, just adjust the timing interrupt time and count value. This condition can always be satisfied after entering the straight path, so as a supplement to the first straight path discrimination method, it ensures the stable and reliable identification of the straight path

straight line stability control strategy

after the car comes out of the corner, due to the insensitive response of the steering gear, the smart car will oscillate, and then it can reach stability. In order to reduce the oscillation as soon as possible, the following methods are used to control the action of the car after coming out of the corner:

set the flag bit in the curve strategy, identify the flag bit after entering the straight line program, and adopt the correction setting for the formula controlling the steering of the steering gear. The formula is: q=k1q1+k2q2; Where q is the control quantity finally sent to the steering gear, Q1 is the return angle value of the front photoelectric sensor, and Q2 is the return angle value of the rear infrared sensor. K1 and K2 are the weighted proportional values of the front and rear sensors respectively. Generally, K1 and K2 are

1, and the assignment will be changed when necessary

when the car enters the straight path from the curve and successfully identifies the straight path, reduce the value of K1. Because the rear sensor is very close to the front wheel (steering wheel) of the car, and the car center deviates from the black line, there will be no large displacement in the horizontal position of the rear sensor (relative to the front sensor), so the number of steering gear adjustments of the car on the straight line will be significantly reduced, and the stability of the straight line will be good. At the same time, according to the combination of different sensors in the front and rear rows, different rotation strategies are given (reflected in the form of a list in the program), so as to further improve the stability control ability of the straight line

References:

1. Shaobeibei, development method of embedded application of single chip microcomputer [m], Tsinghua University, 2004

2. Zhuo Qing, et al., learn to make smart cars, Beijing University of Aeronautics and Astronautics Press, March 2007 (end)