R.A.M.B.O: Robotic Arm for Monitoring and Ballistic Operations

Status: Completed | Tech Stack: Raspberry Pi, Python, OpenCV, LiDAR

(A real-time demonstration of the autonomous tracking and firing system)

Abstract

This project implements an autonomous defense system capable of detecting, tracking, and neutralizing targets in real-time. Integrating a Raspberry Pi with a 2-DOF servo turret and Computer Vision, the system maps 2D camera coordinates to physical 3D space. A TF-Luna LiDAR sensor provides continuous Time-of-Flight (ToF) ranging, acting as a safety interlock that only triggers the ballistic mechanism (DC Motor via MOSFET) when the target breaches a 20cm proximity threshold.

📄 Read the full IEEE Technical Report

Key Features

• Computer Vision: Real-time color masking and centroid tracking using OpenCV.
• Sensor Fusion: Combines visual data (X/Y) with LiDAR depth data (Z) for 3D threat assessment.
• Hardware Interface: Custom Logic Level Shifting (TXS0108E) to bridge 3.3V Pi logic with 5V servos.
• Actuation: High-current DC motor control using an IRLZR44N N-Channel MOSFET.

Hardware Architecture

The system uses a mixed-voltage architecture handled by logic level shifters.

• Controller: Raspberry Pi 4 (GPIO & USB Interface)
• Vision: Raspberry Pi AI Camera Module
• Servos: 2x LX-16A Serial Bus Servos (UART Protocol)
• Sensor: TF-Luna LiDAR (I2C Protocol)

Software Logic

The system runs a closed-loop control cycle:

1. Acquire: Camera frame is processed for red color mask.
2. Calculate: Centroid (X,Y) is mapped to servo angle (0-1000).
3. Track: PID loop adjusts Pan/Tilt servos via UART.
4. Measure: LiDAR checks distance via I2C.
5. Engage: If distance < 20cm, GPIO High -> MOSFET Gate -> Fire.