hamuchiwa
57 linhas
3.6 KiB
Markdown
57 linhas
3.6 KiB
Markdown
## AutoRCCar
|
|
|
|
[See self-driving in action (Youtube)](https://youtu.be/BBwEF6WBUQs)
|
|
|
|
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
|
|
- 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 image data for neural network in npz format
|
|
- testing_data/
|
|
- testing image 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
|
|
- ***rc_control_test.py***: drive RC car with keyboard (testing purpose)
|
|
- ***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
|
|
- ***mlp_predict_test.py***: test trained neural network with testing data
|
|
- ***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
|
|
|
|
### 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, parameters are saved in “mlp_xml” folder
|
|
|
|
5. **Neural network testing:** Run *“mlp_predict_test.py”* to load testing data from “testing_data” folder and trained parameters from the xml file in “mlp_xml” folder
|
|
|
|
6. **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](http://docs.opencv.org/doc/user_guide/ug_traincascade.html) and [this great tutorial by Thorsten Ball](http://coding-robin.de/2013/07/22/train-your-own-opencv-haar-classifier.html)
|
|
|
|
7. **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.
|
|
|