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
Worked on the Avionics-Propulsion-Landers-GT/MonopropUAV repository, developing robust state estimation and control features for UAV autonomous flight. Integrated sensor fusion using the Madgwick filter and EKF with UWB, GPS, and Lidar data to improve attitude and position accuracy. Advanced the control pipeline by implementing linearized LQR matrices, gain computation, and latency analysis, leveraging C++ and MATLAB for numerical reliability. Enhanced the codebase with custom matrix and quaternion math classes, refactored documentation, and resolved critical bugs including memory leaks. Introduced Kalman-based DC estimation and expanded LAPACK integration, supporting scalable, maintainable control and estimation workflows for 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
89 peopleSame Organization
@gatech.edu67
Tomi AkejuMember
alexdcdcMember
Adam McLaughlinMember
Akshat-ShenoiMember
Swami, AdityaMember
bihanMember
bmendonca3Member
bpatel347Member
bzhang625Member
Shared Repositories
22
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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