Autonomous STM32H743ZI Drone System
- STM32H743ZI microcontroller board
- GPS module (e.g., NEO-6M)
- LIDAR sensor (e.g., TFMini Plus)
- OV7670 camera module
- Telemetry module (e.g., XBee)
- IMU (Inertial Measurement Unit)
- Drone frame and motors
- STM32CubeIDE (version 1.8.0 or later)
- ARM GCC Toolchain
- OpenOCD for flashing
- Flight Control: Implements PID-based stabilization for roll, pitch, and yaw.
- GPS Navigation: Utilizes GPS data for waypoint navigation.
- Obstacle Avoidance: Uses LIDAR data to detect and avoid obstacles.
- Path Planning: Generates and follows optimal paths between waypoints.
- Telemetry: Enables real-time data transmission and remote command reception.
- Computer Vision: Incorporates image processing for scene analysis and decision-making.
- Clone the repository:
git clone https://github.com/yezzfusl/AeroBrainSTM.git
cd AeroBrainSTM
- Build the project:
make
- Flash the binary to the STM32H743ZI:
openocd -f interface/stlink.cfg -f target/stm32h7x.cfg -c "program build/autonomous_drone.elf verify reset exit"
- Power on the drone system.
- Use the telemetry interface to:
- Add waypoints
- Start the mission
- Monitor drone status
- Abort the mission if necessary
- Microcontroller: STM32H743ZI (Arm Cortex-M7, 400 MHz, 2 MB Flash, 1 MB RAM)
- GPS: UART communication, NMEA sentence parsing
- LIDAR: I2C or UART communication, distance measurement up to 12m
- Camera: OV7670 module, DCMI interface, 640x480 resolution
- Telemetry: UART communication, custom protocol for data exchange
- Image Processing: Basic edge detection using Sobel operator
- Path Planning: A* algorithm for optimal path generation
- Obstacle Avoidance: Reactive avoidance based on LIDAR readings
- The main control loop runs at 100 Hz (10ms cycle time).
- Image processing is computationally intensive and may impact overall system performance.
- The system uses DMA for efficient data transfer from peripherals.
- Geofencing to restrict flight area
- Failsafe routines for loss of GPS or telemetry signal
- Low battery voltage detection and auto-return
- Implement advanced computer vision algorithms for object detection and tracking
- Enhance path planning with dynamic obstacle avoidance
- Add support for multi-drone coordination
This project is licensed under the MIT License - see the LICENSE file for details.
- [@YEZZFUSL]