Avionics-Propulsion-Landers-GT/MonopropUAV
Mar 2025 – Apr 2025
2 Months active
Languages Used
CC++CMakeCSVGitHTMLPythonFortran
Technical Skills
Aerospace EngineeringAlgorithm DevelopmentC++C++ DevelopmentCode DocumentationControl Systems
sboebel2024
PROFILE
Sboebel2024
During two months on the Avionics-Propulsion-Landers-GT/MonopropUAV repository, Sam Boebel developed and integrated advanced state estimation and control features for UAV autonomy. He implemented sensor fusion using the Madgwick filter and extended Kalman filters, improving attitude and position accuracy. Sam engineered a linear quadratic regulator (LQR) pipeline, including matrix linearization, gain computation, and latency analysis, leveraging C++ and MATLAB for robust numerical methods. He introduced triangulation algorithms, enhanced code documentation, and refactored matrix libraries to support quaternions. His work addressed critical bugs, improved memory management, and established a foundation for scalable, maintainable control and estimation systems in embedded aerospace applications.
Overall Statistics
Feature vs Bugs
90%Features
Repository Contributions
69Total
Bugs
3
Commits
69
Features
27
Lines of code
8,012,370
Activity Months2
Your Network
75 people
Work History
April 2025
56 Commits • 23 Features
Apr 1, 2025April 2025 performance summary for Avionics-Propulsion-Landers-GT/MonopropUAV focused on delivering robust control and estimation capabilities with strong emphasis on numerical reliability and collaboration readiness. Key control-loop work progressed toward a near-Riccati LQR pipeline with validated gain generation and integration of K and U via LQR. Foundational numerical methods were solidified (pseudoinverse implemented; SVD groundwork; extensive LAPACK enhancements) to support more accurate and scalable computations. Kalman-based DC estimation was introduced and integrated (Kalman Dc modules), improving sensor fusion and DC state estimation. Critical bug fixes and reliability improvements were completed (large A issue resolved; memory leak fixed in ControlLoop/LQR), and CARE-related techniques were advanced through Fixed-Point Iteration with subspace elevation for Kd. Documentation and project scaffolding were enhanced to improve onboarding and remote collaboration.
56 Commits • 23 Features
Apr 1, 2025April 2025 performance summary for Avionics-Propulsion-Landers-GT/MonopropUAV focused on delivering robust control and estimation capabilities with strong emphasis on numerical reliability and collaboration readiness. Key control-loop work progressed toward a near-Riccati LQR pipeline with validated gain generation and integration of K and U via LQR. Foundational numerical methods were solidified (pseudoinverse implemented; SVD groundwork; extensive LAPACK enhancements) to support more accurate and scalable computations. Kalman-based DC estimation was introduced and integrated (Kalman Dc modules), improving sensor fusion and DC state estimation. Critical bug fixes and reliability improvements were completed (large A issue resolved; memory leak fixed in ControlLoop/LQR), and CARE-related techniques were advanced through Fixed-Point Iteration with subspace elevation for Kd. Documentation and project scaffolding were enhanced to improve onboarding and remote collaboration.
April 2025
March 2025
13 Commits • 4 Features
Mar 1, 2025March 2025 performance summary for the MonopropUAV project (Avionics-Propulsion-Landers-GT). Focused on delivering robust state estimation, real-time control readiness, and codebase health improvements to enable safer autonomous flight and faster feature delivery. Key outcomes include integrated attitude estimation via sensor fusion, readiness for LQR-based control, enhanced state representation with triangulation, and substantial codebase/documentation improvements that reduce maintenance burden and support future enhancements.
March 2025
13 Commits • 4 Features
Mar 1, 2025March 2025 performance summary for the MonopropUAV project (Avionics-Propulsion-Landers-GT). Focused on delivering robust state estimation, real-time control readiness, and codebase health improvements to enable safer autonomous flight and faster feature delivery. Key outcomes include integrated attitude estimation via sensor fusion, readiness for LQR-based control, enhanced state representation with triangulation, and substantial codebase/documentation improvements that reduce maintenance burden and support future enhancements.
Activity
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Quality Metrics
Correctness76.8%
Maintainability76.0%
Architecture75.0%
Performance73.0%
AI Usage24.8%
Skills & Technologies
Programming Languages
CC++CMakeCSVFortranGLSLGitHTMLJavaScriptMATLAB
Technical Skills
Aerospace EngineeringAlgorithm DevelopmentAlgorithm ImplementationBuild SystemsC++C++ DevelopmentCode DocumentationCode GenerationComputer GraphicsControl SystemsData AnalysisData EngineeringData ManagementData ProcessingData Visualization
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