This repository is the implementation of the paper "Indoor navigation of unmanned grounded vehicle using CNN", International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-6, March 2020.
Contains both Hardware tested on toy car and simulations. Generate training data- records training images & respective steering angles. Using convolutional neural network(CNN)model can autonomously drive the car.
test/
Real_Vehicle.py: Big RC car control with keyboard
rc_control_test.py: RC car control with keyboard
stream_server_test.py: video streaming from Pi to computer
ultrasonic_server_test.py: sensor data streaming from Pi to computer neural network model in OpenCV3
raspberryPi/
stream_client.py: stream video frames in jpeg format to the host computer
ultrasonic_client.py: send distance data measured by sensor to the host computer
arduino/
rc_keyboard_control.ino: control RC car controller
computer/
picam_calibration.py: pi camera calibration
collect_training_data.py: collect images in grayscale, data saved as *.npz
model.py: neural network model
model_training.py: model training and validation
rc_driver_helper.py: helper classes/functions for rc_driver.py
rc_driver.py: receive data from raspberry pi and drive the RC car based on model prediction
rc_driver_nn_only.py: simplified rc_driver.py without object detection
About 3000 Images of our college corridor were taken
I have used the black pages as boundary
This video is the prototype which was sucessfully implemented on a medium size toy car in college premises.
https://www.ijrte.org/wp-content/uploads/papers/v8i6/F7972038620.pdf
Please cite the above paper if you use the model in your work.