This project is a metal detection vehicle built using the Raspberry Pi Pico W (the version with the CYW43439 chip). I mainly wrote the code in C and did most of the development myself, without relying on any pre-existing libraries.
- Mine detection: Non-invasive and human-safe detection of mines.
- Archaeological scanning: Automated scanning of large areas for archaeological findings.
- Raspberry Pi Pico 2
- u-blox NEO-6M(GPS module)
- 49e sensor (Hall effect sensors)
- MPU6050 (Accelerometer and Gyroscope)
- Battery Management System (BMS)
- Power supply: Lithium-ion batteries in a 2s2p configuration
- Custom analog circuit: A metal detection system based on a Beat Frequency Oscillator (BFO)
The project is split into two main parts:
-
Computer Application: A multithreaded C application that runs on the computer. You can find it in my other repository called - "metal_detection_app".
-
Metal Detection Vehicle: The metal detection system consists of two main parts: the hardware (including the vehicle’s drive system, the metal detection circuit, etc.) and the software (algorithms for mapping, control, regulation, data acquisition, metal detection, communication via Wi-Fi and UDP, etc.).
The vehicle system includes the following functionalities:
- Wireless communication system (UDP protocol over Wi-Fi), implemented on a separate core to minimize latency and prevent overloading the main core.
- Trajectory control and stabilization system – a PID algorithm that ensures the vehicle moves straight, even on uneven surfaces.
- Vehicle control system – a Hall sensor-based system that enables the vehicle to turn in place, thanks to 4 independent servos.
- Metal detection system – a custom solution based on a Beat Frequency Oscillator (BFO) circuit for metal detection.
- Mapping systems – an inertial mapping system based on an MPU6050 module and a u-blox NEO-6M v2 GPS module for positioning.
- Other smaller systems – additional systems supporting the overall functionality.
You can find it in my other repository called - "metal_detection_app".
