Robotic Control System

Inspired by a Star Wars droid named Loadlifter

Python software to control the Hiwonder ArmPi Pro robot.

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Features

• Autonom driving in a warehouse using Mecanum wheels and a LiDAR
• Use robotic arm to pick up and place objects
• Camera for object detection with machine learning
• Demo videos available on my YouTube https://www.youtube.com/@danielrmv

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👨🏽‍💻 Created as a student project

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Installation on the Raspberry Pi

1. Prepare the system

   • Raspberry Pi OS or Ubuntu recommended, then install all updates:
     sudo apt update && sudo apt full-upgrade -y
   • Install basic tools:
     sudo apt install -y git python3 python3-pip python3-venv libgpiod2
   • Optional: Enable additional tools for camera, I2C, and serial interfaces (sudo raspi-config) and install libatlas-base-dev if you need faster NumPy operations.

2. Clone the repository

   cd ~
   git clone https://github.com/daniel-rmv/Loadlifter.git
   cd Loadlifter

3. Create the Python environment

   python3 -m venv .venv
   source .venv/bin/activate
   python -m pip install --upgrade pip

4. Install Python dependencies

   pip install -r requirements.txt

   • For camera functionality, make sure the Raspberry Pi camera is enabled.
   • For the gpiod path, run sudo adduser $USER gpio and log in again if needed.

5. Prepare the hardware

   • Enable I2C for the motor controller (sudo raspi-config → Interface Options → I2C).
   • Configure the serial interface for servos/LiDAR (enable UART, disable console login).
   • Check the wiring for the buzzer, LiDAR, motor controller, and camera.

6. Test the software

   • Example: start the control via CLI\

     python -m src --help

   • For live visualization, use a PC/laptop and start the camera/LiDAR.

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Start the overlay client on the Mac

1. Clone the repository

   cd ~
   git clone https://github.com/daniel-rmv/Loadlifter.git
   cd Loadlifter

2. Virtual environment and dependencies

   • Python 3 is already available on macOS (update via Homebrew if needed).
   • Create a virtual environment and install packages:

     python3 -m venv .venv
     source .venv/bin/activate
     python -m pip install --upgrade pip
     pip install -r requirements.txt

3. Run the overlay client

   • Make sure the MJPEG stream is running on the Pi (python -m src.low_level.camera_bridge).
   • Start the overlay client and enter the Pi IP:

     python -m src.visualization.overlay_client --pi <pi-ip>:5000

   • Optionally adjust the model or parameters such as --conf (confidence) or --fps. The default model is located at models/best.pt.