Automatic Nerf Remote Sentry

Overview

Designed and built a fully autonomous Nerf turret as part of Trine University’s Robotics Senior Design capstone. The system uses OpenCV for real-time target tracking on a Raspberry Pi 4 (4 GB) with a custom power supply, gyroscopic aiming, and motion-prediction logic for moving targets.

Key Features

• Real-Time Target Tracking: OpenCV-driven detection with calibration of target shape, size, and color performed live on the Pi.
• Motion Prediction: Predicts target trajectories for dynamic aiming rather than chasing the last frame.
• Gyroscopic Aiming System: Custom gyroscope-stabilized servo gimbal for precise yaw/pitch control.
• Multithreaded Architecture: Splits video processing, prediction, remote control, and servo motion across threads to keep latency low on a low-end device.
• Remote / Cloud Control: Operator can override or monitor remotely; the same threading model handles network I/O without blocking the CV loop.
• Custom Power Supply: In-house power-delivery design to drive the servos and Pi reliably from a single battery.

Tech Stack

• CV: OpenCV, Matplotlib, NumPy
• Hardware: Raspberry Pi 4 (4 GB), servos, gyroscope, custom PSU
• Language: Python (multithreaded)
• Course: Trine University, Robotics Senior Design (Capstone)