Rawan Abdellatif

Knowledgeable mechatronics engineer with strong background in electrical, mechanical and software engineering. Skilled in developing and testing microcontroller-based systems, troubleshooting hardware and software, and creating successful maintenance and repair protocols. Detail-oriented team player with strong organizational skills. Ability to handle multiple projects simultaneously with a high degree of accuracy.

• Python, JavaScript, MATLAB, C, C++, Robot Operating System (ROS)
• Microcontroller programming
• Sensor Technology
• Robotics Programming
• Agile Methodologies
• DOORs and Enterprise Architect

• Reinforcement Learning and Optimal Control, German University in Cairo, 2021
• Nonlinear and Adaptive Control, German University in Cairo, 2021
• Optimization Techniques for Multi-Cooperative Systems, German University in Cairo, 2021
• Image Processing for Mechatronics, German University in Cairo, 2021
• Autonomous Systems, German University in Cairo, 2021
• Cooling of Electronic System, German University in Cairo, 2021
• Fundamentals of Reinforcement Learning, University of Alberta, 2022
• Machine Learning, Stanford University, In Progress

• Multi Robotic System Lab: Transforming a CNC machine into a 3D printer by making hardware modifications and integrating Arduino IDE algorithms with Repetier Software.
• ARAtronics Lab: Implementing controller for a PUMA robot employing 3 distinct algorithms on Arduino, incorporating joystick manipulation, forward and inverse kinematics, and interaction with a MYO band.
• Aerial Robotics Lab: Simulating and implementing distributed and formation control for multiple quadrotors through the application of the Artificial Potential Field algorithm using MatLab.

• Super Hero Chess Game: Developing a Super-Hero Chess game board with an interactive GUI using JavaScript.
• Inverted Pendulum: Controlling hardware for an inverted pendulum system using the PID controller algorithm implemented on an Arduino.
• Autonomous RC Car: Programming an RC car using Raspberry Pi as the high-level controller to autonomously navigate predefined paths, achieving an average accuracy of 75%.
• Image Processing-based Controlled Goalkeeper Robot: Developing and implementing an innovative image recognition algorithm for a mini goalkeeper, resulting in an unprecedented 80% reduction in goal-scoring attempts during testing.