Irene Grace Karot Polson

Autonomy Engineer @ AeroVect | MSE Robotics @ UPenn | IIT Kanpur'22

Philadelphia, Pennsylvania

Overview 

Irene Grace Karot Polson is currently an Autonomy Engineer at AeroVect, with a background in MSE Robotics from the University of Pennsylvania and a Bachelor of Technology from IIT Kanpur. She has experience in projects such as autonomous racing, obstacle avoidance, and spacecraft attitude control, showcasing expertise in optimization, reinforcement learning, computer vision, and control systems design. Highlights of Irene's career include her involvement in autonomous multi-drone path planning for large structure inspections at the Indian National Academy of Engineering, and her work as a Controls Development Engineer at RangeAero, where she honed her skills in motion control and decision trees.

Work Experience 

  • Autonomy Engineer

    2024 - Current

AeroVect develops autonomous driving platforms for GSE.

  • Autonomy Engineering Intern

    2023 - 2023

AeroVect develops autonomous driving platforms for GSE.

  • F1 Tenth Autonomous Racing

    2023 - 2023

    · Designed and deployed a high-speed ROS2 autonomous driving stack on NVIDIA Jetson Xavier NX. · Our stack featured Automatic Emergency Breaking(AEB), a pure-pursuit controller and precision navigation with RRT and occupancy grid-based obstacle avoidance.

  • Obstacle Avoidance Trajectory Planner And Controller for Quadrotor

    2023 - 2023

    · Engineered the autonomy stack for the CrazyFlie quadrotor, seamlessly integrating a nonlinear geometric controller, a 3D trajectory and motion planner employing A*/Dijkstra algorithms for obstacle configuration space, and a minimum jerk polynomial trajectory generator optimized through quadratic programming.

  • Pick and Place with Panda Arm Motion Planning

    2022 - 2022

    · Using inverse kinematics to determine joint angles needed for effective path planning. Both static and dynamic blocks were picked up and stacked. · ROS is used as framework and gazebo for simulation.

  • Flight Operations Analysis Intern

    2021 - 2021

    • Worked on automatic Flight classification into Multiple groups for more efficient flight operations analysis. • The multiple classes considered were test, delivery, training, and normal flights.

  • Derivative-free Adaptive Spacecraft Attitude Control

    2021 - 2021

    Advisor: Dr. Dipak Kumar Giri | Space Dynamics and Flight Control Laboratory · Developed a purely adaptive control system with derivative-free weight update laws on a spacecraft attitude system represented using MRPs. · Working on applications of machine learning and evolutionary optimization techniques on adaptive laws and its update rates as a part of my undergraduate research project.

  • Autonomous Multi-Drone Path Planning for Large Structure Inspections

    2021 - 2021

    Advisor: Dr. Debasish Ghose, Guidance | Control and Decision Systems Laboratory, IISc Bangalore · One amongst 60 students selected from across India as a candidate for the Indian National Academy of Engineering Mentorship Program 2021-22. · Developed an in-house controller and a 3D coverage path planning algorithm using Lissajous curves without explicit geometry. It can easily be implemented at nominal computation costs.

  • Control System Design using Bond Graph Representation

    2021 - 2021

    Advisor: Dr. N. Selvaganesan | Indian Control Conference 2021 · Developed a power-based graphical representation of quadcopter system and its controller using bond graph approach. Reduced model is derived and is used to obtain feedback. · The entire closed-loop MIMO system is represented using 20-sim software and simulations were performed to meet satisfactory stable responses under tracking and disturbance conditions.

  • Controls Development Engineer

    2020 - 2021

    • Automating the process of tuning PID controllers in a control system simultaneously. • Find the optimal gains using evolutionary optimization techniques such as the Genetic Algorithm. • Using PX4 simulator for the simulation environment with Quadcopter simulation using JMAVsim or connections to Pixhawk. • MAVLink connections through UDP ports were used in MATLAB for optimization and parameter setting.

  • Deep Learning Internship

    2020 - 2020

    • Classify satellite images into classes of military targets using PyTorch and Sci-Kit learn libraries. • Worked with a dataset of planes ( 25000+ images) to do binary classification and a dataset of different classes of ships ( 8000+) to do multiclass classification. • Implementation of Kalman Filter over Neural Networks to perform target tracking on passive Radar signal-bearing angles. Using the bearing angles and timestamps, we predict the target trajectory. • Made the Dataset for training and testing using Robotics System Toolkit, MATLAB.

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