Overview of Example Code

This page explains the overview and features of the self driving code provided in aim_line_follow.py

Inside of aim_line_follow.py, there is a function that receives vector information from the simulated Pixy camera and returns speed and steer values. The vector information is received through nxp_cup_interfaces that contains the PixyVector message and is nearly identical to the vector information sent by the Pixy over I2C.

The source code uses a simple algorithm to extract speed and steer values from the supplied vector data to drive the car. We expect contestants to improve upon this algorithm and show us how fast their simulated NXP Cup car can go!

Key Components

Important Variables and Functions

There are several important variables and functions that are used in the code. A key description of them is given below:

Variable/Function	Purpose
PixyVector	It is imported from "nxp_cup_interfaces.msg" library. It contains information of pixy vectors obtained from Pixy Camera from nxp_track_vision.py_
__init__()	Initialization function for LineFollow class. The code in this function may not be edited
self.start_time	A custom timer corresponding to the simulation timer
self.restart_time	It is used when no vectors are found to start the timer
self.linear_velocity	The class variable that will store the linear velocity by which the car will move in simulation
self.angular_velocity	The Class variable will store the angular velocity by which the car will steer in simulation
self.speed_vector	Class variable to convert self.linear_velocity into Vector3() object, which is suitable for message transferring to Gazebo
self.steer_vector	Class variable to convert self.angular_velocity into Vector3() object, which is suitable for message transferring to Gazebo
self.cmd_vel	Variable that stores speed and steer information together and pass this information via messages for the nxp-cup car to move
get_num_vectors()	Class method to calculate the number/s of Pixy Vectors obtained. The output of this function will be 0,1 or 2 only.
listener_callback()	Class method where all the necessary algorithms are applied to calculate linear velocity and steer for the nxp-cup car.
frame_width	Frame width of QT viewer showing the output of simulated Pixy Camera
frame_height	Frame height of QT viewer showing the output of simulated Pixy Camera
current_time	Get current time using datetime() library. This along with self.start_time is used to calculate the time difference at ant given point of simulation

Data Structure of PixyVector

This data structure contains the head and tail points of two vectors from the simulated pixy camera.

The two vectors are defined by msg.m0_ __ and _ msg.m1__.

# Vector 0 head and tail points
msg.m0_x0    # Tail of vector @ x cordinate
msg.m0_y0    # Tail of vector @ y cordinate
msg.m0_x1    # Head of vector @ x cordinate
msg.m0_y1    # Head of vector @ y cordinate

# Vector 1 head and tail points
msg.m1_x0    # Tail of vector @ x cordinate
msg.m1_y0    # Tail of vector @ y cordinate
msg.m1_x1    # Head of vector @ x cordinate
msg.m1_y1    # Head of vector @ y cordinate

If-Else Block

The if-else block statements within listener_callback() performs different instructions depending on how many vectors are found.

Case: 0 vectors found

If no vectors are found (case 0), then a timer will start and tell the vehicle to stop after a certain amount of time

Case: 1 vector found

If one vector is found, the algorithm finds the gradient of the vector and stores that in steer, and the speed

Case: 2 vectors found

If two vectors are found, then the example algorithm will find the offset in the x direction of the average of the two head points of each vector. This will give us a steering values that will steer the cup car in the correct direction. This value is stored in steer. The speed value will be calculated and is stored in speed.