PID Control line follower
Follow line behaviour in a Formula-1 car, using images as input It has two controllers, for rotation and speed.
Image Processing
1. Colour filtering
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The red line present on the formula track is to be followed.
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A “mask” is developed using the RGB (or hsv) values of the input image are thresholded such that only red colour is highlighted.
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Overlaying this mask over the input image gives us a red coloured blob representing the line to be followed.
2. Finding a single point to be tracked
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A single point (or a line) is considered as an ideal “set” point which represents the direction in which the car should be going to find this point, the concept of moments in OpenCV is used
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Definition of moments in image processing is borrowed from physics. Assume that each pixel in image has weight that is equal to its intensity. Then the point you defined is centroid (a.k.a. center of mass) of image.
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Assume that I(x,y) is the intensity of pixel (x,y) in image. Then m(i,j) is the sum for all possible x and y of: I(x,y) * (x^i) * (y^j).
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The error value is calculated by the difference in x and y values of the set point and the centre of the frame at the bottom of the input image.
input image along with red line and set point
PID control
1. P control
- Generates a signal that is directly proportional to the error, with the multiplying factor being Kd. However, the value signal often ends up oscillating around the desired value.
2. I control
- The offset error accumulated by the P control can be removed using the I control (Integrator). integral error = previous integral error + current error
3. D control
- The overshooting of the desired signal can be reduced by using D control (differentiator). The change in error is multiplied by Kd to give the requiered output.
video by MATLAB explaining PID
this video demonstrates the use of PD control