Omnicopters for Spacecraft Simulation
AFRL-funded senior design project using omnicopters as hardware-in-the-loop spacecraft simulators. Developed optimal motor placement and a novel indoor disturbance-rejection …
AFRL-funded senior design project using omnicopters as hardware-in-the-loop spacecraft simulators. Developed optimal motor placement and a novel indoor disturbance-rejection …
Autonomy and Controls Tech Lead for SD Mines' IGVC entry. Won Rookie of the Year. Implemented MPPI control, A* path planning, and frontier-based autonomous exploration.
Simulation of a LEO satellite with autonomous debris avoidance, optical debris detection, attitude correction, and cyber-attack resilience — built in the Basilisk simulator.
Adaptive neural surrogate for 1D Hall thruster discharge simulation, used to optimize magnetic field profiles for improved thruster efficiency.
Suite of autonomous driving demonstrations combining nonlinear MPC with control barrier functions (CBF-nMPC) for provably safe lane changes with dynamic obstacles.
Physics-informed ARX system identification and PID optimization for HVAC control in environments with uncertain thermal parameters.
Blob-detection target acquisition with pixel-to-torque feedback control, tuned via data-driven transfer function identification.
Novel algorithm for detecting orbital debris from images taken by a moving space-based observer, using optical flow background removal that preserves faint objects.
Analytical modeling and optimization of Brayton cycle configurations for maximum thermal efficiency and net work output.
Introductory deep computer vision demo for general audiences — live object detection and neural style transfer via CNN.
Outreach demonstration covering RL control fundamentals, algorithm benchmarking, and live examples for a general audience.
Computational modeling and optimization of a novel supersonic ejector refrigeration cycle to improve thermal efficiency.
Model identification and regression for optimal trebuchet performance — part of Rocker Robotics competition team activities.
Computer Science Technical Lead for an autonomous vehicle challenge entry — computer vision, high-fidelity simulation, and RL control.
Differential drive robot with computer vision-based path detection and pixel-to-torque feedback control for autonomous course navigation.