Vehicle navigation system based on photoelectric sensing and path memory
Abstract:? According to the technical requirements of the first Freescale Cup National Invitational Tournament for Smart Cars for College Students, this paper develops a forward-looking unmanned vehicle navigation system based on double-row arrangement and analog photoelectric sensors, puts forward a steering and driving control algorithm based on path memory, and summarizes the experience in designing and manufacturing smart cars.
Key words:? Unmanned vehicle; Navigation; Photoelectric sensing; Path memory
introduce
In response to the call of the Ministry of Education to strengthen the cultivation of college students' innovative consciousness, cooperative spirit and innovative ability, the Department of Automotive Engineering of Tsinghua University actively teamed up to participate in the first "Freescale Cup" national smart car invitational tournament for college students. Preparations began from June 5438+February 2005, which lasted for eight months. Six generations of path identification schemes based on photoelectric sensors are developed, a simulation research platform for smart cars is developed, and a steering and driving control strategy based on path memory algorithm is proposed. Power management, noise suppression and drive optimization are also studied. Through a large number of simulation tests, road tests and basic performance tests, an intelligent car navigation system based on photoelectric sensors and path memory is developed. This paper will introduce the overall scheme, path identification scheme selection, steering and drive control, and path memory algorithm of smart car.
Overall scheme of smart car
The intelligent car system takes Freescale MC68S9 12DP256 as the core, and consists of power module, sensor module, DC motor drive module, steering motor control module, control parameter selection module, single chip microcomputer module and so on. As shown in figure 1. The working voltage of the smart car system consists of+1.6V, +5V and 7.2V, of which 7.2V supplies power to the driving motor and steering gear, 5V supplies power to the speed sensor, MCU and photoelectric sensor receiving tube, and 1.6V supplies power to the LED. In order to facilitate the adjustment of online control parameters, a control parameter selection module is also set up, which can call different programs or control parameters through the setting of several keys to meet the requirements of different field conditions.
Figure 1 Overall structure of smart car
The working mode of smart car is: photoelectric sensor detects trajectory information, speed sensor detects current speed, battery voltage monitoring circuit detects battery voltage, and inputs these information into single chip microcomputer for processing. The control algorithm sends control commands to the racing car, and the steering gear and driving motor control the trajectory and speed of the racing car in real time.
In order to achieve good results in the smart car competition, it is very important to optimize the chassis parameters and the reliability of hardware equipment of the model car. Among them, the optimization of front wheel positioning parameters, the increase of steering arm and the adjustment of chassis center of gravity have great influence on the mechanical properties of the car model. See [1] for the optimization of chassis parameters, which is not detailed in this article.
Path Identification Scheme Selection and Circuit Design
First of all, we need to determine the path identification scheme, which mainly has the following problems.
* photoelectric identification or camera identification;
* How are the sensors arranged? How big the interval is, what shape, single row or double row;
* The sensor can detect the forward distance;
* sensor signal is digital signal or analog signal;
* How to realize it on the circuit.
Because the photoelectric identification scheme is simple and reliable, this paper adopts photoelectric identification scheme.
Digital photoelectric identification and analog photoelectric identification
The organizing committee of the competition requires that the maximum number of sensors is 16, excluding 1 speed sensor, the number of sensors that can be used to detect the path is 15, and the total width of sensors allowed to be arranged is 25cm. If the digital photoelectric sensors are evenly distributed, the detection accuracy of the road can only reach about 17mm, and it is difficult for the car to achieve high control accuracy and response speed in the process of moving forward. The disadvantage of digital photoelectric sensor is that it loses a lot of information in path detection.
Theoretically, analog photoelectric sensor can greatly improve the accuracy of path detection. The analog photoelectric sensor emits and receives light in a cone-shaped space with a certain cone angle, and its voltage has a quantitative relationship with the horizontal distance of the sensor from the black path marking line: the closer to the black line, the lower the voltage, and the farther away from the black line, the higher the voltage (the specific correspondence is related to the photoelectric cell model and the height from the ground), as shown in Figure 2.
Figure 2? Schematic diagram of the relationship between sensor voltage and offset distance
Therefore, as long as the characteristic relationship between the sensor voltage and the offset distance is mastered, the distance between each sensor and the black marker line can be determined according to the sensor voltage (instead of just roughly judging whether the sensor is online), and then the position of the longitudinal axis of the car body relative to the path marker line can be obtained, so as to obtain the continuously distributed path information.
According to the real vehicle test, the accuracy of path detection can be improved to 65438±0mm, so that the information collected by the sensor can ensure that the single chip microcomputer can obtain accurate trajectory information, thus providing a guarantee for improving the accurate control of the vehicle.
Double-row arrangement and prospective design
In this paper, an intelligent vehicle performance simulation platform is developed [2], and the sensor layout is deeply studied [3]. Because steering gear, motor and vehicle are all high-order inertial delay links, it takes a certain time from input to output. The sooner we know the information of the road ahead, the more we can reduce the lag from input to output. Detecting the trajectory at a certain distance in front of the car is called forward looking. In a certain forward-looking range, the larger the forward-looking sensor scheme, the higher its limit speed, and the higher the tracking accuracy of the guide line during its high-speed driving, and the better the overall response performance of the system. Therefore, the path identification module is designed to form an angle with the ground, the front sensor is used to look forward, and the rear sensor identifies the starting point of the track, and calculates the deviation slope between the longitudinal axis of the car body and the center line of the track, so as to better adjust the attitude of the vehicle.
In order to ensure that the photoelectric sensor still has enough luminous intensity when the ground clearance is as large as possible, this paper adopts the control mode of large current pulse triggering luminescence.
According to the experimental test, when the LED emits light, the current is about 0.5A. If the 15 sensor is used and the instantaneous current is 7.5A, such a large current will definitely affect the battery voltage, which is not conducive to the normal work of the whole system. Therefore, the lighting time of the front and rear sensors is staggered, and the lighting is controlled by two sets of trigger circuits. In this way, the influence of infrared light emitting tube on battery voltage is effectively reduced.
Steering and driving control and path memory algorithm
Drive motor control
In this paper, a toothed disc is added to the motor output shaft, and the rotation of the motor output shaft drives the toothed disc to rotate. Place the light-emitting tube and light-receiving tube of the opposite optical coupler on both sides of the code wheel. When the code wheel rotates, the transmission of light will be hindered because the teeth on the code wheel pass through the light emitted by the LED. So the resistance at both ends of the receiving tube will change greatly, so the voltage at both ends of the sampling resistor in the circuit will also change greatly. Using the pulse capture port on the processor to collect the number of voltage pulses per unit time, the motor speed can be obtained, and then the vehicle speed can be obtained.
The motor is driven by Freescale MC33886. The difference is that this paper adopts three pieces of MC33886? Parallel connection, on the one hand, can reduce the on-resistance and improve the motor driving ability, and the heating situation of MC33886 has also been greatly improved; On the other hand, reduce MC33886? Influence of internal overcurrent protection circuit on motor starting and braking.
The motor adopts PID closed-loop control, which can adjust the duty cycle of PWM in time according to different load conditions, so that the vehicle can track the target speed quickly.
In order to improve the speed as much as possible, the highest target speed is set on the straight road, and the vehicle speed is controlled at a constant speed. Adjust the speed to the corner limit speed when entering the corner, and accelerate ahead of time when leaving the corner.
Steering control
According to the current layout of double-row analog photoelectric sensors, the offset between the longitudinal axis of the car body and the center line of the track can be obtained, and the slope of the center line relative to the longitudinal axis of the car body can be obtained, so as to know the posture of the car body in the current state and then carry out steering control.
Here, it is assumed that the rotation angle obtained from the front row sensor signal is θ 1, and the rotation angle obtained from the vertical axis slope information obtained from the front row sensor signal is θ2. The formula for determining the final steering angle is:
θ=k 1θ 1+k2θ2
Using this control strategy, the weighted control of the actual attitude of the vehicle can be realized, the turning speed can be greatly improved, and the cumulative error of decision-making caused by the problem of detection accuracy can be reduced. In addition, the double combination of large forward-looking and double rows realizes the characteristics of early turning in normal corners and late turning in S corners.
In order to make the steering gear better respond to the given angle value, PID adjustment is adopted, and the parameters are adjusted by road test, so that the vehicle can maintain high stability at high speed.
Path memory algorithm
Because the competition rules require the vehicle to run twice on the runway, the information such as the number of pulses collected by the speed sensor and the steering angle of the steering gear can be recorded in the first lap of the vehicle to judge the information such as distinguishing straight, curve, S-bend, steering direction and turning radius. According to the data information recorded in the first lap, each road point in the second lap can be divided. Use the highest speed acceleration on the straight road, slow down in advance before entering the curve, and reduce to the limit maximum speed of the curve. For curves with different radii, choose different speeds. The advantage of path memory algorithm is that it can achieve the effect similar to CCD probe for complex S-bend, and the time can be greatly shortened by choosing a smaller turning angle. See [4] for the specific algorithm.
Experience and conclusion
After six rounds of development iterations, the development of smart car in this paper has developed from the initial small forward-looking single-row digital sensor to pulsed light, large forward-looking, double-row arrangement and analog sensor scheme. The control strategy is upgraded from simple PID control to path memory control, which greatly improves the navigation performance of the vehicle. Through the development process of smart cars, some experiences have been gained.
* At the beginning of development, it is necessary to actually test the photoelectric sensor, steering gear, drive motor, vehicle mechanical properties, steering and side slip characteristics, battery characteristics and other characteristics.
* According to the automobile theory, adjust the vehicle structure within the scope permitted by the rules to achieve better mechanical performance.
* The Organizing Committee has developed a simulation platform. We should make full use of this simulation tool to study the path identification scheme based on photoelectric sensors, and combine the choice of hardware and our own experience in control and electronics to determine the path identification scheme. The scheme of long forward distance is helpful to improve the passing speed of vehicles.
* PID can meet the requirements of vehicle control, and parameter setting needs to be combined with road test. Don't accelerate and decelerate too sharply. Smooth control can also achieve good results. Excessive acceleration will lead to overheating of the motor and driving chip, which will reduce the driving performance.
This paper introduces the overall scheme, path identification scheme selection, steering control and path memory algorithm of the champion car in the first smart car competition for college students. Because the forward-looking photoelectric sensor is large and needs large current, the battery power consumption is large. When the runway distance is long, the battery power of the vehicle drops rapidly, which reduces the racing performance of the vehicle. The fuzzy tracking algorithm of path memory algorithm also needs improvement. The camera path identification scheme can realize large foresight and low power consumption, which is the direction of future efforts.
References:
1., Li, Huang Kaisheng, "Chassis Analysis of Intelligent Model Cars", Electronic Products World, 2006 (11):150-153.
2. Zhou Bin, Jiang Yinan and Huang Kaisheng, "Intelligent Vehicle Simulation System Based on Virtual Instrument Technology", Electronic Products World, 2006(3)? : 132- 134
3. Li, Huang Kaisheng, "Research on the influence of photoelectric sensor layout on path identification in smart cars", Electronic Products World, 2006(9): 139- 140.
4. Zhou Bin, Liu Wang and Lin Xinfan. ,' Research on trajectory memory algorithm of smart car', Electronic Products World, 2006 (15):160-166.
5. Huang Kaisheng, Jin Huamin, Jiang Binan, "Technical Scheme Analysis of Intelligent Model Car in Korea", Electronic Products World, 2006(5): 150- 152
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