Python Rover Robot

This is a Python Rover project which currently uses 3 ultrasonic sensors and a magnetometer to navigate. Its a fairly specific thing I used as a learning tool perhaps someone may find as a useful example.

It is capable of streaming basic OpenCV image processing done onboard at roughly 700ms frequency (very slow), helper scripts are included to calibrate a lens for image processing.

It does not yet navigate by the houghline detection it performs due to the capability being limited by the visual frequency.

The navigation loop seems to be limited by the multithreading performance of Python. I will be attempting to re-create the whole framework in Rust.

Supported OS.

Raspbian Stable/Raspberry Pi OS stable

Instructions

Install everything in Linuxfile with apt

for package in `cat Linuxfile`; do
apt -y install $package
done

Create a venv for the files, The drivers used require root access.

cd ~
git clone https://github.com/russmac/python_rover robot
cd robot && python3 -m venv venv
. venv/bin/activate
pip3 install -r requirements.txt

Edit instance/config.py

camera_selection = "picamera_v2_vga"

camera_configurations = {
    "picamera_v2_vga": {
        "id": "picamera_v2_vga",
        "fisheye": True,
        "canny_auto_levels": True,
        "stream_canny": False,
        "camera_x": 640,
        "camera_y": 480,
        "framerate": 90,
        "canny_params": {"upper": 160,
                         "lower": 40},
        "hough_params": {"minLineLength": 100,
                         "maxLineGap": 5,
                         "probabilistic": True},
    },
}

motor_configuration = {
    "enable_pin": 17,
    "slew_pin": 27,
    "left": {
        "pwm_pin": 13,
        "forward_pin": 6,
        "reverse_pin": 5,
        "state_flag_pin": 11,
    },
    "right": {
        "pwm_pin": 12,
        "forward_pin": 20,
        "reverse_pin": 16,
        "state_flag_pin": 26,
    },
}

ultrasonic_configuration = {
    "ussl_pin": 7,
    "ussc_pin": 8,
    "ussr_pin": 25,
    # How many cm is considered clear from center sensor
    "clear_distance_c": 18,
    # How many cm is considered clear from side sensors
    "clear_distance_side": 30,
    # What cm is a clear path.
    "path_distance": 38,
}

And to start up

python3 threaded.py

The app looks for a camera calibration in ./config matching the camera id in instance/config.py , The same config is used in the calibration helper scripts so the files should be correctly named.

The robot will concurrently look for a locally saved Bosch BNO055 calibration, Whichever parts are present will be used, You will be prompted to calibrate the rest.

Using the calibration tools

https://docs.opencv.org/2.4/doc/tutorials/calib3d/camera_calibration/camera_calibration.html#

1. Run ./calibration_capture.py on your Rpi and take your captures
2. You need to rsync or scp ~/robot/calibration to ~/samples/calibration on your local machine when your done.
3. You then run calibrate.py and it will show you progress as it attempts to find chessboard corners.
4. Succesfully chessboarded calibration images will be saved (and drawn on) under samples/results , Failures will be deleted from samples/calibration.
5. Numpy binary arrays will be saved under samples/config , You need to SCP or Rsync these to ~/robot/config (including sample_image.jpg).

Viewing the camera feed

Navigate to http://robot:8000 where robot is your robots IP address.

Hardware list

Microcontroller

• Raspberry Pi 4B (used in my project)

Other options;

• Raspberry Pi 3B
• Nvidia Jetson Developer kit
• Nvidia Xavier NX

Platform

• DFRobot "Devastator" platform (any two motor rover will do)
• Polulu Dual MC33926 Motor Driver

Navigation

• 3x DFRobot URM37 v5.0 Ultrasonic rangefinders.
• Adafruit ADS 1015 12 bit ADC
• DFRobot 10DOF AHRS (Bosch BNO055 & BMP280)

Vision

• PiCamera V2

Power

• Adafruit PowerBoost 1000 Charger 5v 1A
• Polymer Lithium Ion Battery (LiPo) 3.7V 6000mAh