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🚓 TurtleBot4 Multi-Robot Park Patrol

Autonomous illegal parking detection system using TurtleBot4 robots, developed as part of the Doosan Robotics Bootcamp 2025 – 3rd Cohort**.


📌 Overview

This project demonstrates a multi-robot surveillance system using two TurtleBot4 robots. The robots autonomously patrol an area, detect parked vehicles, recognize license plates, and verify them against a database. If a vehicle is departing and there is a risk of collision, or if illegal parking is detected, the robot activates a warning signal (🔊 beep-boop alert).


🎯 Use Case Scenario

  1. A vehicle enters a blind zone and is detected using YOLO-based vision.
  2. The nearest TurtleBot4 robot executes Nav2 navigation to approach the vehicle.
  3. The robot performs local patrol and identifies any parked vehicle.
  4. The license plate number and current position are sent to a central system controller.
  5. The controller queries a local database to verify the license plate.
  6. If the vehicle is unauthorized → 🚨 illegal parking alert is triggered.
  7. The alert is visualized on a GUI dashboard and stored in a monitoring database, displaying the vehicle number and zone information.
  8. The system operates in two zones, each covered by a dedicated TurtleBot4 robot (multi-robot architecture).

🎥 Demo Video

Watch the video


🔁 System Architecture

image

  • robot0: Patrols Zone 0, checks vehicle presence, detects license plate, and raises alert.

  • robot1: Operates in Zone 1, performs same tasks independently.

  • Shared Services:

    • License plate OCR module
    • Vehicle number database service

🔧 Features

🚘 AI Computer Vision (YOLOv8 + Bytetrack + OCR + Depth)

  • Detects vehicles using YOLOv8 (object detection) + Bytetrack (object tracking)
  • Recognizes license plates using Easy OCR with depth-guided localization.
  • Measures distance to license plate using a depth camera to enhance recognition accuracy.

obt1 obt2

🗺️ Custom Navigation Graph with BFS

  • Implements a custom waypoint-based navigation graph.
  • Employs a Breadth-First Search (BFS) algorithm for optimal path planning across patrol zones.
  • Supports automatic saving and loading of waypoint configurations in YAML format, ensuring persistence and reusability.

normalvsBFS

📊 Real-Time Dashboard

  • Monitors robot patrol status and OCR results in real time.
  • Displays unauthorized vehicle detections by zone.
  • Visual feedback for system alerts and mission states.

dashboard

🗃️ Local Database Integration

  • Maintains a whitelist of authorized license plates.
  • Matches OCR results against the database to determine illegal parking.
  • Sends alert messages if an unauthorized vehicle is detected.

image1


📄 Documentation

For a detailed explanation of this project, please refer to the following document:

👉 docs/F-1&2_지능1_이재호_배재성_전유진_정은영_김태영_김도엽_한준모.pdf


👥 Contributors

Thanks to these wonderful people who have contributed to this project:

weedmo
weedmo
yujin114
yujin114
ethica-alt
ethica-alt
jsbae-RL
jsbae-RL
SmiteLims
SmiteLims
taeyoung730
taeyoung730
kimdoyub
kimdoyub

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