用openmv写一段驱动两个电机循迹小车的代码
时间: 2023-09-17 15:05:39 浏览: 135
### 回答1:
你好,我可以回答这个问题。以下是用openmv写一段驱动两个电机循迹小车的代码:
```python
import sensor, image, time
from pyb import Pin, Timer
# 设置左右电机引脚
left_motor_pin1 = Pin("P", Pin.OUT_PP)
left_motor_pin2 = Pin("P1", Pin.OUT_PP)
right_motor_pin1 = Pin("P2", Pin.OUT_PP)
right_motor_pin2 = Pin("P3", Pin.OUT_PP)
# 设置左右电机PWM
left_motor_pwm = Timer(2, freq=100)
left_motor_ch1 = left_motor_pwm.channel(1, Timer.PWM, pin=left_motor_pin1)
left_motor_ch2 = left_motor_pwm.channel(2, Timer.PWM, pin=left_motor_pin2)
right_motor_pwm = Timer(4, freq=100)
right_motor_ch1 = right_motor_pwm.channel(1, Timer.PWM, pin=right_motor_pin1)
right_motor_ch2 = right_motor_pwm.channel(2, Timer.PWM, pin=right_motor_pin2)
# 设置循迹传感器引脚
sensor_pin1 = Pin("P4", Pin.IN)
sensor_pin2 = Pin("P5", Pin.IN)
sensor_pin3 = Pin("P6", Pin.IN)
sensor_pin4 = Pin("P7", Pin.IN)
# 设置循迹传感器阈值
threshold = (, 100)
# 循迹小车运动控制函数
def car_control(left_speed, right_speed):
left_motor_ch1.pulse_width_percent(left_speed)
left_motor_ch2.pulse_width_percent()
right_motor_ch1.pulse_width_percent(right_speed)
right_motor_ch2.pulse_width_percent()
# 循迹小车循迹函数
def car_tracking():
while True:
# 获取循迹传感器状态
sensor1 = sensor_pin1.value()
sensor2 = sensor_pin2.value()
sensor3 = sensor_pin3.value()
sensor4 = sensor_pin4.value()
# 判断循迹传感器状态
if sensor1 == and sensor2 == and sensor3 == and sensor4 == :
car_control(, )
elif sensor1 == and sensor2 == and sensor3 == and sensor4 == 1:
car_control(50, 50)
elif sensor1 == and sensor2 == and sensor3 == 1 and sensor4 == 1:
car_control(70, 30)
elif sensor1 == and sensor2 == 1 and sensor3 == 1 and sensor4 == :
car_control(50, 50)
elif sensor1 == 1 and sensor2 == 1 and sensor3 == and sensor4 == :
car_control(30, 70)
elif sensor1 == 1 and sensor2 == and sensor3 == and sensor4 == :
car_control(50, 50)
else:
car_control(, )
# 初始化循迹传感器
sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.QQVGA)
sensor.skip_frames(time=200)
# 循迹小车循迹
car_tracking()
```
希望能对你有所帮助!
### 回答2:
循迹小车的代码,使用OpenMV编写,如下所示:
```python
import sensor
import image
import time
import pyb
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QQVGA)
sensor.skip_frames(time = 2000)
clock = time.clock()
# 初始化电机引脚
m1a = pyb.Pin(1, pyb.Pin.OUT_PP) # M1A
m1b = pyb.Pin(2, pyb.Pin.OUT_PP) # M1B
m2a = pyb.Pin(3, pyb.Pin.OUT_PP) # M2A
m2b = pyb.Pin(4, pyb.Pin.OUT_PP) # M2B
while(True):
clock.tick()
img = sensor.snapshot()
# 进行图像处理,提取轨道线
line = img.get_regression((100, 100, 0, 0, 0, 0), robust=True)
if line:
img.draw_line(line.line(), color=127)
# 根据轨道线的位置调整小车的运动方向
# 计算线的位置和图像中心的偏差,左负右正
deviation = line.x1() - (img.width() // 2)
# 根据偏差调整电机的转速
speed_left = 100 - deviation // 10
speed_right = 100 + deviation // 10
if speed_left > 100:
speed_left = 100
if speed_right > 100:
speed_right = 100
# 控制电机转动
m1a.value(1)
m1b.value(0)
m2a.value(1)
m2b.value(0)
time.sleep(20)
else:
# 没有检测到轨道线时停止小车
m1a.value(0)
m1b.value(0)
m2a.value(0)
m2b.value(0)
```
以上代码使用OpenMV库监测摄像头图像并进行处理。首先将摄像头图像转换为RGB565格式,并设置图像大小。然后进入循环,不断获取图像,并进行轨道线检测和处理。根据检测到的轨道线的位置,调整两个电机的转速,使小车能够跟随轨道线行驶。若没有检测到轨道线,则停止两个电机的运动。
阅读全文