YOLO-Based Water Cannon Auto-Aiming System

Introduction

This is a college innovation project (Project No. 20251983), relying on the resources of Sun Yat-sen University’s School of Systems Science and Engineering and Robotics Association, supervised by Associate Professor Hou Yanqing https://ssse.sysu.edu.cn/teacher/252, belonging to project initiation, initially understanding the content related to vision and robot control.

Hardware Platform

Dual-Hull Unmanned Boat Design

A high-strength catamaran with dual propulsion systems for enhanced stability and power. For details, see zephyr

Hardware Configuration

• Mainboard: NVIDIA Jetson Xavier NX Super for GPU inference.
• Servo Gimbal: Fashionrobo intelligent servos with serial communication and PID control.
• Camera: Nuwa HP60C depth cameras for 3D perception and distance measurement.

Algorithm Design

Modular ROS2 Architecture

• Framework: Node-based architecture separating perception, decision-making, and execution for asynchronous communication and concurrent processing.
• Benefits: Improves performance, scalability, and maintainability.

Adaptive Control Algorithms

• PID Integration: PID control with auto-tuning for servo motors, including anti-windup, low-pass filtering, and dead-zone compensation to reduce power consumption and eliminate oscillations.

System Architecture

Built on ROS2, divided into three layers:

Perception Layer

• Camera Monitor Node: Handles USB/depth camera inputs, publishes image streams to /camera/image.
• Object Detection Node: Uses YOLOv8 for real-time detection, publishes bounding boxes and confidence scores to /detection/result.

Decision Layer

• Tracking Calculation Node: Transforms pixel coordinates to angular errors using camera FOV parameters.

Execution Layer

• Servo Control Node: Implements adaptive PID controllers with auto-tuners for precise actuation.
• Servo Monitor Node: Monitors voltage, temperature, and position for safety.

Integration

• Launch Package: One-click startup with configurable parameters for depth camera integration.

Key Achievements

Python Monolithic System (v1.1.8 / AIM V1.0)

• Software: “AIM Intelligent Target Tracking System” with multi-threaded architecture, SimplePID controller, SimpleTargetTracker, and CoordinateConverter.
• Features: Intelligent degradation protection, auto-tuning, and monitoring.

Project Team

• Project Leader: Cu Disheng (Information Engineering, 2023)
• Team Members: Cen Dai, Liu Chaoyin, Mao Lishan (Information Engineering, 2023)
• Supervisor: Hou Yanqing (School of Systems Science and Engineering)