Hi!! I am Prakrit
🎓 Hello! I am Prakrit Tyagi, a recent graduate with a Master’s degree in Mechanical Engineering, driven by a passion for robotics and innovation. My academic journey has equipped me with a solid foundation in the design of robotic software stacks, bug analysis, and problem-solving, complemented by hands-on experience in hardware robotics projects.
🕵️♂️ With a keen interest in advancing technology, I have focused my studies and projects on controls, path planning, SLAM, and machine learning. I thrive in environments where creativity meets technical expertise, and I am eager to apply my skills to real-world challenges.
💼 I am currently seeking opportunities in robotics companies and startups where I can contribute to groundbreaking projects and collaborate with like-minded professionals.
Education
- Carnegie Mellon University 2022-2024
- M.S. in Mechanical Engineering
- GPA: 4.00/4.0
- Delhi Technological University 2017-2021
- B.Tech in Mechanical Engineering
- GPA: 8.75/10.0
Projects
Warebots: Multi Agent Path Finding
The project explores the Multi-Agent Pathfinding (MAPF) problem, focusing on one-shot MAPF and its dynamic counterpart, Lifelong MAPF (LMAPF). It delves into the Conflict-Based Search Algorithm (CBS), a two-level approach for resolving conflicts between agents, showcasing its effectiveness in small-scale environments inspired by the League of Robot Runners competition.
LQR based Thrust control of Quadcopter
Implemented an LQR controller, which is linearized depending on the quadrotor’s state, unifies the control of rotational and translational states, handles time-varying system dynamics, control parameters and does direct motor control.
Human Facial Emotion Recognition & Classification
As part of a semester-long machine learning team project, built a CNN model that could recognize and classify human emotions from facial images. The objective was to train the model to identify the emotion of a person from their faces.
Super Visual Lidar Odometry and Mapping
Implemented a robust real-time ROS-based framework for accurate trajectory estimation, 3D Mapping, and Localization by augmenting the feature extraction and matching algorithm with Super-Point descriptor and SuperGlue matching algorithm.
