Vision-Guided-Ball-Balancing-2DOF

2DOF Ball Balancing Plate using STM32, OpenCV, and PID Control

Table of Contents

• Project Description
• Hardware
• Technologies
• Control System
• How to Use It

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Project Description

Developed during MCTR601 Mechatronics Engineering course (BSc in Mechatronics Engineering) at the German University in Cairo (GUC), this project implements a real-time ball balancing system using computer vision and embedded control to stabilize and track a ball on a 2-degree-of-freedom (2DOF) tilting platform. The primary goal is to design a system that can dynamically stabilize a rolling ball by adjusting the tilt of a platform in both the x and y directions.

Key Features

| Feature | Description | |———|————-| | Multi-Control Strategies | PID, PV (Proportional-Velocity Controller), and LQR for dynamic stabilization | | Trajectory Tracking | Follows predefined paths (circle, figure-8) or user-drawn real-time trajectories via GUI | | Laser Tracking | Ball chases a moving laser dot projected on the platform | | Online Tuning | STM32F103C8T6 communicates online with PC via FTDI Serial Module for parameter tuning and monitoring system states |

Functional Diagram

Technical Stack

Vision System

• Python and OpenCV pipeline for image processing, including ball and laser detection.
• HSV color filtering for robust object detection under varying lighting conditions.
• Kalman filter and Exponential Moving Average (EMA) filter for enhancing position stability and trajectory prediction by minimizing noise.
• Velocity estimation by computing positional change over time.

Control System

• PID, PD (PV), and LQR controllers implemented and tuned for stable balancing.
• STM32F103C8T6 Bluepill microcontroller as the control backbone.
• PWM-driven servo motors (MG996R) actuate platform angles based on control input.
• Kalman filter provides reliable velocity estimates used in the control loop.
• Exponential Moving Average (EMA) filter applied to the controller’s output for smoothing.

GUI Interface

• Java-based Graphical User Interface (GUI) powered by JavaFX provides full user control.
• Enables interactive trajectory drawing, mode selection, control algorithm switching, real-time monitoring, data logging, and online parameter tuning.
• Communicates with the STM32 via UART through FTDI and opens a TCP/IP socket for communication with external software like MATLAB or Python.

Trajectory tracking

-Circle trajectory

Hardware Components

• 2x MG996R Servo Motors: High-torque servos for precise two-axis tilt control.
• USB Webcam: Mounted above the platform to capture real-time video of the ball’s position.
• Laser pointer: Used for detection and tracking.
• STM32F103C8T6 (Bluepill): Low-cost microcontroller serving as the system’s control backbone.
• USB to TTL Converter - FT232RL FTDI Serial Module: Used for communication between the PC (GUI) and the STM32.
• Power Supply
• Buck Converter XL4015
• Power Distribution Board

• Tiltable platform

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Project Highlights

• Multidisciplinary Integration: Effectively integrates embedded systems, computer vision, and control theory.
• Real-Time Performance: Designed for real-time dynamic ball balancing, with a vision pipeline maintaining tracking performance up to 60 FPS. System components work cohesively to achieve real-time stabilization.

System Architecture

The system follows a layered architecture structure to ensure modularity, scalability, and maintainability.

• PC Application Layer: Hosts the Java GUI, Python program (using OpenCV and Kalman filter for image processing and tracking), and can interface with MATLAB Control via TCP/IP.
• Communication Layer: Handles communication between the PC and the embedded system via UART (using an FTDI converter) and enables PC-to-PC communication (e.g., Java GUI to MATLAB) via TCP/IP.
• Embedded Layer (STM32 Bluepill):
  • Application Layer: Contains the main system logic (main.c, dataHandler.c, control.c, servo.c).
  • Middleware / OS: Utilizes FreeRTOS scheduler.
  • Hardware Abstraction Layer (HAL): Includes STM32 HAL Drivers and custom drivers (servo.c).
  • External Hardware: The STM32 Bluepill and its peripherals.

This architecture allows for seamless integration between software and hardware for responsive, accurate control.

Hardware

The physical system consists of a flat platform mounted on two servo motors arranged orthogonally, controlling tilt along the X and Y axes.

• Servo Motors (2x MG996R): Provide precise two-axis control for ball stabilization. Driven by STM32-generated 50Hz PWM signals (500-2500μs pulse width). Torque output of 11kg·cm at 6V and angular velocity of 0.15sec/60°. Metal gear transmission for durability. Connected to the platform via 47mm servo arms.
• Webcam: Captures live video frames at runtime.
• STM32F103C8T6 (Bluepill): Serves as the control backbone. Receives processed ball position data via Bluetooth from the PC (Note: Later sections mention UART/FTDI and TCP/IP for PC communication), applies the selected control algorithm, and generates PWM signals for servos.
• FTDI USB to TTL Converter: Facilitates serial communication between the PC and STM32.
• Power Supply and Buck Converter (XL4015): Provide necessary power to the components.

Technologies

The project integrates several key technologies:

• Microcontroller: STM32F103C8T6 (Bluepill) running FreeRTOS.
• Computer Vision: Python and OpenCV for real-time image acquisition, preprocessing, object detection (ball and laser) and position/velocity estimation.
• Control Theory: Implementation of PID, PD (PV), and LQR control algorithms. System modeling, linearization, and transfer function analysis. Discretization techniques for digital implementation.
• Actuation: Servo Motors (MG996R) driven by PWM signals from the STM32.
• User Interface: Java-based GUI (JavaFX) for comprehensive system control and monitoring.
• Communication: UART (via FTDI) and TCP/IP for data exchange and external control integration (MATLAB/Python).

How to Use It

The system is controlled primarily through a Java-based Graphical User Interface (GUI).

1. Hardware Setup: Ensure the ball balancing platform, servos, webcam, STM32 (Bluepill), FTDI converter, and power supply are correctly connected.
2. Start Vision System: Run the Python program which uses OpenCV to capture video from the webcam and perform ball/laser tracking. An interface allows selecting the correct camera.
3. Start GUI: Launch the Java GUI application.
4. Connect Hardware: Use the GUI to establish communication with the STM32 via the FTDI serial module.
5. Calibration (if needed): The GUI may offer a calibration mode. The Python vision system includes real-time trackbars for tuning HSV thresholds for ball and laser detection.
6. Mode Selection: Choose between operating modes like idle, automatic, manual, or calibration via the GUI.
7. Control Algorithm Selection: Dynamically select the desired control algorithm (PID, PD, LQR, or custom) through the GUI.
8. Parameter Tuning: Adjust control parameters (Kp, Ki, Kd, EMA alpha, etc.) online via the GUI for fine-tuning the system response.
9. Setpoints & Trajectories: Set desired ball positions or select/draw predefined trajectories (circle, figure-eight, custom paths) via the GUI. Input desired angular velocity for trajectory execution.
10. Monitoring: View real-time tracking of the ball’s position on a coordinate grid and visualize system input-output behavior through plotted graphs on the GUI.
11. Data Logging: Export recorded data as CSV files for offline analysis.
12. External Control (Optional): The GUI can connect to MATLAB or Python scripts via TCP/IP, allowing control signals to be generated externally, facilitating experimentation with advanced control techniques.

Contributors

• Ali Emad
• Marawan ElSayed

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