Mechatronics Projects

This project focused on enhancing the realism of an electric wheelchair simulator by developing and validating a digital twin. The primary objective was to create a highly accurate virtual model, calibrated with real-world data, that faithfully reproduces the physical behavior of a real wheelchair. This precision is essential for demanding applications such as powerchair football, where maneuver accuracy is critical.

• Analytical Modeling with Matlab/Simulink: I developed a fundamental mass-spring-damper analytical model in Matlab/Simulink to represent the wheelchair as a dynamic vertical system. This model was used to simulate and analyze the chair's response to perturbations (e.g., crossing an obstacle), forming the theoretical basis for defining essential dynamic parameters like position, rotation, and pitch.
• Digital Twin Development in Unreal Engine 5: I created a virtual wheelchair within Unreal Engine 5. This involved optimizing a 3D model in 3ds Max, importing it as a Skeletal Mesh, and configuring realistic physical properties (mass, inertia, constraints) for its chassis, drive wheels, and caster wheels. The wheelchair's control logic was programmed using Blueprints and reinforced with C++ in Visual Studio to enable real-time parameter extraction (position, rotation), resulting in an interactive simulator responsive to user inputs and physical constraints.
• Experimental Validation with Qualisys: To validate the model against reality, I conducted experiments using a Qualisys motion capture system (12 infrared cameras). A real wheelchair, fitted with reflective markers, had its dynamics precisely recorded during obstacle crossing (e.g., height variations, pitch, rotations). Simultaneously, data from the Unreal simulator (position, rotation) was extracted in real-time via a custom C++ module and saved, providing synchronized real-world and virtual data sources.
• Data Superposition and Analysis in Matlab: I developed a dedicated Matlab application to compare the real and simulated data. This tool allowed for the simultaneous import and display of curves (e.g., Z-height evolution, pitch angle) from both Unreal and Qualisys, highlighting discrepancies such as high-frequency vibrations present in the real chair but absent in the initial model. An optimization function was integrated to identify physical parameters (stiffness, damping, inertia) that better matched reality, making Matlab central to validating and refining the simulator.
• Optimization Loop (Core of the Project): The core of this work was an iterative optimization loop. Discrepancies between real and simulated data were analyzed in Matlab, an algorithm automatically adjusted the model's physical parameters (e.g., stiffness, damping, inertia, mass), and these corrected parameters were then reinjected into Unreal Engine for a new simulation. This cyclical process, repeating the simulation and comparison, progressively enabled the digital twin to converge towards the actual behavior of the wheelchair.

This project successfully delivered an experimentally validated digital twin simulator that is more faithful in its dynamic reactions, capable of reproducing real wheelchair behaviors, and ready for concrete applications like training powerchair football athletes in an immersive environment. It provided invaluable experience across multiple engineering disciplines, including dynamic system modeling, real-time simulation development in game engines, experimental validation with motion capture, and data analysis and optimization.

This project involved the design, fabrication, and programming of an automated sorting system. At its heart is a custom-built Delta robot, chosen for its speed and precision, which is commanded using computer vision for object identification and manipulation. The system demonstrates a comprehensive approach from mechanical conception to integrated electronic control.

• Mechanical Design and Fabrication: I designed the robot's unique parallel architecture using SolidWorks. Following the digital design, I fabricated the physical structure and components in a workshop.
• Hardware Platform: The robot is controlled by a Raspberry Pi 3. I used Dynamixel motors, known for their accuracy and feedback capabilities, to drive the robot's arms.
• Kinematic Modeling and Control: I derived and implemented the inverse geometric model of the Delta robot. This model allows the Raspberry Pi to calculate the necessary joint angles for the Dynamixel motors based on the desired Cartesian coordinates of the object the robot needs to pick or place.
• Computer Vision Integration: I integrated computer vision into the system. This enables the robot to process camera input, detect specific objects, and determine their positions in the workspace.
• Software Development and Remote Control: I developed the control software on the Raspberry Pi to link the computer vision data with the kinematic calculations and command the Dynamixel motors. I also implemented functionality for remote control, allowing the robot to be operated or monitored by connecting to the same network as the Raspberry Pi.

This project provided invaluable experience across multiple engineering disciplines: mechanical design (CAD and fabrication), embedded systems programming (Raspberry Pi), robotics kinematics and control, advanced motor control (Dynamixel), computer vision, and network communication. It showcases my ability to build and program a complex robotic system from the ground up to perform an autonomous task like sorting.

This project, completed as part of a semester project for the Master’s program in Mechatronics, focuses on the design, simulation, and implementation of an autonomous mobile robot. The robot is equipped with advanced features for line-following, obstacle detection, and remote control capabilities.

• Mechanical Design: Chassis and component modeling using SolidWorks.
• Electronic Integration: Circuit design simulated on Proteus, integrating an Arduino board, ultrasonic sensors for obstacle detection, and infrared sensors for line-following.
• Software Development: Implementation of control logic using C++ and Arduino IDE to achieve autonomous and remote-controlled operations.
• Simulation: Flowchart-based system simulation to validate control algorithms and logic.

This project demonstrates expertise in robotics, from mechanical design to electronic integration and control, showcasing real-world applications in automation and navigation.

This project implements a complete proximity detection and alarm system: an Arduino reads distance data from an HC-SR04 ultrasonic sensor, computes whether an obstacle is within a danger zone, and streams formatted messages over serial. A C# WPF application receives these messages, displays real-time distance and system status, and plays a looping alarm sound when an object is too close.

• Arduino Sensor Module: • HC-SR04 ultrasonic sensor on pins 9 (TRIG) and 10 (ECHO). • Measure pulse width with pulseIn() to compute distance in cm. • Send messages “D:<distance>” and “ALARM:ON/OFF” via Serial @ 9600 baud.
• Serial Communication: • C# uses System.IO.Ports.SerialPort to open COM port and read lines asynchronously. • DataReceived handler parses lines, updates UI labels and indicators accordingly.
• WPF GUI (C#): • MVVM-style XAML layout with ComboBox for ports, Connect/Refresh buttons, and panels for radar & alarm status. • Dispatcher.Invoke to marshal UI updates from the background thread.
• Audible Alarm: • MediaPlayer plays a looping MP3 when alarm is active. • Graceful handling of missing or load errors, with user notifications.
• State Management: • Threshold constant (50 cm) defines danger zone. • Visual cues: colored Ellipses and TextBlocks for “Zone libre” vs. “Objet détecté” vs. “Alarme active”. • Clean connect/disconnect logic, proper cleanup on window closing.

This project demonstrates end-to-end integration of embedded sensor hardware with a rich desktop interface, covering real-time serial data parsing, thread-safe UI updates, multimedia playback, and robust error handling.