This project aims to build an autonomous rc car using supervised learning of a neural network with a five hidden layers.I have modified a remote controlled car to remove the dependency on the RF remote controller. A Raspberry Pi controls the DC motors via an L293D Motor Driver IC. You can find a post explaining this project in detail here. Here's a video of the car in action.
We will be referring the DC motor controlling the left/right direction as the front motor and the motor controlling the forward/reverse direction as the back motor. Connect the BACK_MOTOR_DATA_ONE and BACK_MOTOR_DATA_TWO GPIO pins(GPIO17 and GPIO27) of the Raspberry Pi to the Input pins for Motor 1(Input 1, Input 2) and the BACK_MOTOR_ENABLE_PIN GPIO pin(GPIO22) to the Enable pin for Motor 1(Enable 1,2) in the L293D Motor Driver IC. Connect the Output pins for Motor 1(Output 1, Output 2) of the IC to the back motor.
Connect the FRONT_MOTOR_DATA_ONE and FRONT_MOTOR_DATA_TWO GPIO pins(GPIO19 and GPIO26) of the Raspberry Pi to the Input pins for Motor 2(Input 3, Input 4) in the IC. Connect the Output pins for Motor 2(Output 3, Output 4) of the IC to the front motor.
The PWM_FREQUENCY and INITIAL_PWM_DUTY_CYCLE represent the initial frequency and duty cycle of the PWM output.
We have created five class labels namely forward, reverse, left, right and idle and assigned their expected values. All class labels would require a folder of the same name to be present in the current directory.
The input images resize to the dimension of the IMAGE_DIMENSION tuple value during training.
All these values are configurable in configuration.py.
The images for training are captured using interactive_control_train.py, the car is controlled using the direction arrows and all the images are recorded in the same folder along with the corresponding key press. At the command prompt, run the following command:
python interactive_control_train.py
Data cleaning is done using Renaming Images.py file.
After cleaning the data we call train.py. Here I have used 5 Conv2d and one Dense layer for training the model. This will save the model in keras_model.h5
python train.py 0.1 100
Here's a sample dataset and trained model to get you started.
Once we have the trained model, the RC car is run autonomously using autonomous.py.
python autonomous.py