Syed Mazhar

Research Focus(Master's Thesis): Dedicated to pioneering advancements in motion planning algorithms, specifically tailored for real-time path finding and execution. Specializing in applications for non-holonomic vehicles, integrating complex vehicle dynamics and kinematic constraints into the motion planning process. Metrics: Aim to achieve a 20% improvement in path efficiency and a 25% reduction in computation time compared to existing motion planning algorithms.

Robot Modeling and Simulation. Task Priority Algorithm for Control. Deep Reinforcement Learning for Navigation and Obstacle Avoidance.

Research on Weakly Supervised Semantic Segmentation (WSSS) of sonar data.

Working on diverse defense applications was a key highlight of my apprenticeship, where I designed and developed mechanical systems, equipment, and products. I also had the responsibility of testing and evaluating missile equipment, while simultaneously conducting simulation and analysis of missile parts. My experience also involved the integration of missile components, and conducting research to identify new technologies that could improve the efficiency of mechanical systems.

During my internship, I gained hands-on experience in Robot Manipulation using a Manual Teach Pendant and Python Programming to detect Aruco markers. I was responsible for executing Pick and Place tasks and programming Robot end effector movement using software. This opportunity allowed me to enhance my skills in Robotics and gain practical experience in the field.

Throughout my internship, I was tasked with programming robotic planning algorithms and conducting research to identify areas for improvement in robots. I was also responsible for staying up-to-date with new research in Robotics and communicating my findings through a scientific report, which allowed me to further develop my technical writing and presentation skills.

9.5 out of 10.

94 marks out of 100.

Gold Medalist (Rank 1st out of 120). Grade: 9.53/10