Air Lab | Carnegie Mellon University

Estimating Urban Wind Fields Using UAV-Based Measurements | 2020

Through real-world experiments we proved our method accurately estimates wind inlet conditions using the wind measurements from a flying UAV.

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Goods Delivery Energy Productivity | 2019 - Present

  • Creating a neural network to select motion primitives for a UAV to fly in windy urban environments
  • Building and validating an energy model for autonomous unmanned ground vehicles
  • Developing a path-planning algorithm which factors in risk, energy consumption, and wind

MAGICC Lab | Brigham Young University

UAV Gesture Commands | 2018-19

  • Designed and trained a model to classify ten gestures with an accuracy of 95% using accelerometer and gyroscope measurements.
  • Presented the research at the ICUAS 2019 conference
  • Designed and tested intuitive gestures and behaviors for natural directing of a fleet of UAVs
  • Submitted article to the Journal of Intelligent & Robotics Systems

Multi-Mission Project | 2017-19

  • Developed a search algorithm for cooperating UAVs which maximizes area knowledge and the number of tracked targets using Gaussian process regressions
  • Presented the research at the ICUAS 2018 conference
  • Implemented a Gaussian Mixture Model Kalman filter for more accurate target tracking with heterogeneous sensors

Personal Projects

AUVSI Student Unmanned Aerial Systems Competition

  • Developed a robust RRT path planner for the AUVSI SUAS competition. This planner avoided obstacles while minimizing the waypoint capture error through ensuring long straight paths through waypoints.
  • Also fabricated and repaired fixed-wing UAVs, created an image distortion correction program for letter and shape recognition, and many other tasks over the three years on the team.

Autopilot Implementation

  • Implemented the autopilot from Small Unmanned Aircraft: Theory and Practice in Python. This includes controllers, estimators, a path planner, and a path manager.