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SENTRY NERF Turret Project

Welcome to the repository for the SENTRY NERF Turret, an advanced educational mechatronics platform developed for robotics experimentation, controls engineering, and hands-on learning.

This project demonstrates how mechanical, electrical, and software systems come together in a real, fully functional robotic turret. It provides students and researchers with an accessible platform to explore feedback control, sensor fusion, actuation, and system integration — using NERF balls as the medium for experimentation.

SENTRY Turret

Project Overview

The SENTRY turret is a fully automated NERF blaster mounted on a 2-axis pan/tilt head, controlled by a Raspberry Pi Pico running MicroPython.

The system integrates sensors, encoders, and current monitoring to deliver closed-loop control, ball detection, and real-time feedback. A custom breakout board centralizes the I²C architecture and provides additional ADC channels, making SENTRY a modular, expandable, and classroom-ready platform.

Isometric View
Cross Section

Key Features

  • Automated Targeting and Firing

    • Dual flywheels for ball launching
    • AS5600 encoder-driven feed belt for reliable ball delivery
    • INA260 current-spike detection for ball counting

  • Feedback & Control

    • BNO055 IMU for orientation and automatic tilt leveling
    • VL53L4CX Time-of-Flight (ToF) sensor for range measurement
    • QRD1114 optical sensors relocated to flywheels for rotational speed measurement
    • TCND5000 optical sensors to detect ball launch and calculate speed of ball
    • INA260 current monitoring for real-time performance feedback
    • ADS1115 I²C ADC breakout for expanded analog inputs

  • Interactive System

    • Push-button control for manual firing
    • WS2812B RGB LEDs for real-time visual status and diagnostics

  • Modular & Expandable

    • Unified I²C bus for all sensors, routed through a custom breakout board
    • UART communication ready for integration with a Raspberry Pi 4B and Oak-D Lite camera for advanced vision-based tracking
    • Designed for educational labs, adaptable for both controls and robotics research

Hardware Components

  • Mechanical Base

  • Controller

    • Raspberry Pi Pico running MicroPython
    • Custom breakout board consolidating I²C and ADC expansion

  • Motors

    • Flywheel motor (MOSFET-controlled)
    • Feed belt motor with quadrature encoder
    • Pan & tilt motors driven by BD62120AEFJ-E2 H-bridge drivers

  • Sensors

    • INA260 — current sensor for ball count & system load
    • BNO055 — IMU for orientation and tilt leveling
    • VL53L4CX — ToF distance sensor for range measurement & targeting feedback
    • QRD1114 — optical sensors for flywheel speed measurement
    • ADS1115 — I²C ADC for additional analog input channels

  • Indicators & Controls

    • Push button (manual firing trigger)
    • WS2812B RGB LEDs for status indication

Version 3 Highlights

Version 3 represents the most robust and educationally capable version of SENTRY to date, with major system-level improvements:

  • Unified I²C bus architecture with custom breakout board
  • Encoder integration on feed belt motor
  • VL53L4CX ToF rangefinding for advanced control and targeting
  • Relocated QRD1114s for direct flywheel speed measurement
  • More accurate ball detection via INA260 current spikes
  • ADS1115 I²C ADC for expanded analog sensor support
  • Integrated self-test routines for INA260, BNO055, and QRD1114 sensors
  • Improved documentation and BOM for replication and classroom use

Educational Applications

SENTRY is more than a turret — it is a teaching and research platform. It enables:

  • Demonstration of closed-loop feedback control
  • Practical labs on PID tuning and system stability
  • Exploration of sensor fusion and redundancy
  • Robotics coursework involving computer vision, control systems, and mechatronics integration
  • A fun, tangible way to connect engineering theory to practice

To-Do List

  • Generate and publish full Bill of Materials (BoM)
  • Finish development of handheld controller
  • Create system wiring/block diagram for documentation
  • Develop example student labs (PID tuning, tilt leveling, ball counting)
  • Expand Oak-D Lite + Raspberry Pi 4 integration for vision-based targeting
  • Continue refining firing logic and feed control with encoder feedback

Contribution

Contributions and improvements are welcome!
Feel free to fork, submit pull requests, or open issues for discussion.

⚡ This project is actively used in robotics and controls engineering education. Expect frequent updates as new features, documentation, and hardware iterations are released.