Mechatronics Master's Graduate specializing in Robotics, Deep Learning, Embodied AI and AI Engineering.
- 🎓 Education: M.Sc. in Mechatronics from the University of Siegen & B.Sc. in Electrical Engineering from the University of Malaya .
- 🔬 Research: Focus on differential inverse kinematics, robot simulation, and autonomous control systems.
- 📝 Publication: Co-author of "Constraint-Aware Null-Space Inverse Kinematics for a 9-DOF Mobile Manipulator" accepted at CoDIT 2026.
| Category | Tools & Technologies |
|---|---|
| Languages & Core Math | C++, Python, MATLAB |
| Robotics Frameworks | ROS 2, Gazebo, Nav2, MoveIt, URDF, Isaac Sim |
| Libraries & CV | PyTorch, OpenCV, NumPy, Pandas, Matplotlib |
- 9-DOF Mobile Manipulator Simulation
- Modeled and simulated differential inverse kinematics using Isaac Sim and ROS 2 frameworks for a master's thesis.
- Ackermann Steering Robot Simulation
- Developed a URDF model with
ros2_controlintegration, tuned steering/velocity controllers, and validated motion in Gazebo.
- Developed a URDF model with
- A Pathfinding Visualization*
- Built an interactive grid visualization for the A* pathfinding algorithm using C++, SFML, and CMake.
- Robot Sensor Data Analysis
- Processed ROS 2 Gazebo odometry and sensor data using NumPy and Pandas to compute motion error and localization drift.
- Real-Time Perception & Autonomous Goal-Seeking
- Deployed YOLO and OpenCV with a 3D depth camera to map goal coordinates and enable autonomous navigation.
- CIFAR-10 Image Classification
- Implemented a 3-layer CNN from scratch in PyTorch, achieving 75.76% test accuracy using Adam optimization.
- Sequence-Based Sentiment Analysis
- Designed an RNN architecture (128 hidden units) in PyTorch with a custom NLP preprocessing pipeline (TF-IDF, NLTK).
- 📧 Email: abubakarmughal92@gmail.com
- 💼 LinkedIn: www.linkedin.com/in/abubakar-mughal
- 📍 Location: Germany