/AutoRCCar

OpenCV Python Neural Network Autonomous RC Car

Primary LanguagePythonBSD 2-Clause "Simplified" LicenseBSD-2-Clause

AutoRCCar

中文文档 (感谢zhaoying9105)

See self-driving in action

A scaled down version of self-driving system using a RC car, Raspberry Pi, Arduino and open source software. The system uses a Raspberry Pi with a camera and an ultrasonic sensor as inputs, a processing computer that handles steering, object recognition (stop sign and traffic light) and distance measurement, and an Arduino board for RC car control.

Dependencies

  • Raspberry Pi:
    • Picamera
  • Computer:
    • Numpy
    • OpenCV 2.4.10.1
    • Pygame
    • PiSerial

About

  • raspberrt_pi/
    • 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: acts as a interface between rc controller and computer and allows user to send command via USB serial interface
  • computer/
    • cascade_xml/
      • trained cascade classifiers xml files
    • chess_board/
      • images for calibration, captured by pi camera
    • training_data/
      • training data for neural network in npz format
    • training_images/
      • saved video frames during image training data collection stage (optional)
    • mlp_xml/
      • trained neural network parameters in a xml file
    • picam_calibration.py: pi camera calibration, returns camera matrix
    • collect_training_data.py: receive streamed video frames and label frames for later training
    • mlp_training.py: neural network training
    • rc_driver.py: a multithread server program receives video frames and sensor data, and allows RC car drives by itself with stop sign, traffic light detection and front collision avoidance capabilities
  • test/
    • 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
  • Traffic_signal/
    • trafic signal sketch contributed by @geek111

How to drive

  1. Flash Arduino: Flash “rc_keyboard_control.ino” to Arduino and run “rc_control_test.py” to drive the rc car with keyboard (testing purpose)

  2. Pi Camera calibration: Take multiple chess board images using pi camera at various angles and put them into “chess_board” folder, run “picam_calibration.py” and it returns the camera matrix, those parameters will be used in “rc_driver.py”

  3. Collect training data and testing data: First run “collect_training_data.py” and then run “stream_client.py” on raspberry pi. User presses keyboard to drive the RC car, frames are saved only when there is a key press action. When finished driving, press “q” to exit, data is saved as a npz file.

  4. Neural network training: Run “mlp_training.py”, depend on the parameters chosen, it will take some time to train. After training, model will be saved in “mlp_xml” folder

  5. Cascade classifiers training (optional): trained stop sign and traffic light classifiers are included in the "cascade_xml" folder, if you are interested in training your own classifiers, please refer to OpenCV documentation and this great tutorial by Thorsten Ball

  6. Self-driving in action: First run “rc_driver.py” to start the server on the computer and then run “stream_client.py” and “ultrasonic_client.py” on raspberry pi.