
Anyo developed advanced flight dynamics, control, and trajectory optimization systems for the MonopropUAV repository, focusing on realistic simulation and robust autonomy. He engineered modular frameworks in Rust and Python, integrating sensor fusion, model predictive control, and lossless convexification solvers to improve landing safety and fuel efficiency. His work included detailed physics modeling—such as fluid dynamics, wind, and aerodynamics—alongside telemetry pipelines for real-time monitoring. By structuring data flows and refining algorithms for attitude estimation and trajectory planning, Anyo enabled reproducible testing and maintainable code. The depth of his engineering ensured reliable, data-driven UAV development aligned with research and operational goals.
March 2026: Delivered major MPC and dynamics updates for Avionics-Propulsion-Landers-GT/MonopropUAV, establishing a modular simulation framework, enhancing landing safety, and improving fidelity of rocket dynamics and wind modeling. This work improves test realism, reduces risk in live testing, and accelerates validation.
March 2026: Delivered major MPC and dynamics updates for Avionics-Propulsion-Landers-GT/MonopropUAV, establishing a modular simulation framework, enhancing landing safety, and improving fidelity of rocket dynamics and wind modeling. This work improves test realism, reduces risk in live testing, and accelerates validation.
February 2026: Delivered end-to-end physics realism, control, and trajectory capabilities for the MonopropUAV project. Implemented test rigs, mass properties, MPC framework, ground-truth trajectory scenarios, and fluid dynamics, enabling more realistic simulations, robust control, and data-driven decision making.
February 2026: Delivered end-to-end physics realism, control, and trajectory capabilities for the MonopropUAV project. Implemented test rigs, mass properties, MPC framework, ground-truth trajectory scenarios, and fluid dynamics, enabling more realistic simulations, robust control, and data-driven decision making.
Monthly summary for 2026-01 highlighting key feature deliveries, major bug fixes, overall impact, and technical skills demonstrated for the MonopropUAV project. Focused on delivering a realistic flight dynamics framework and robust telemetry pipelines to enable real-time monitoring, control feedback, and data-driven propulsion development.
Monthly summary for 2026-01 highlighting key feature deliveries, major bug fixes, overall impact, and technical skills demonstrated for the MonopropUAV project. Focused on delivering a realistic flight dynamics framework and robust telemetry pipelines to enable real-time monitoring, control feedback, and data-driven propulsion development.
December 2025 monthly summary for Avionics-Propulsion-Landers-GT/MonopropUAV focused on delivering a robust trajectory planning workflow, improving the lossless convexification solver in Rust, and enhancing documentation for maintainability. The month emphasized cross-language tool integration (Rust/Python), performance diagnostics, and clear engineering communication to accelerate future iterations.
December 2025 monthly summary for Avionics-Propulsion-Landers-GT/MonopropUAV focused on delivering a robust trajectory planning workflow, improving the lossless convexification solver in Rust, and enhancing documentation for maintainability. The month emphasized cross-language tool integration (Rust/Python), performance diagnostics, and clear engineering communication to accelerate future iterations.
November 2025 focused on advancing trajectory optimization capabilities for the Monoprop UAV. Delivered a Rust-based lossless convexification solver prototype with iterative solution checking and initial error handling, and added fuel consumption estimation to support UAV performance analysis. While refining the codebase and constraints, we established the foundation for a robust optimization workflow, enabling more reliable mission planning and performance insights.
November 2025 focused on advancing trajectory optimization capabilities for the Monoprop UAV. Delivered a Rust-based lossless convexification solver prototype with iterative solution checking and initial error handling, and added fuel consumption estimation to support UAV performance analysis. While refining the codebase and constraints, we established the foundation for a robust optimization workflow, enabling more reliable mission planning and performance insights.
October 2025 monthly summary for Avionics-Propulsion-Landers-GT/MonopropUAV: Focused on delivering Rust-based lossless convexification solver with Clarabel integration, expanding vehicle kinematics conditioning, and establishing thrust profiling data logging for the 1D rocket fuel simulation. These efforts enable faster, safer trajectory optimization and data-driven analysis for the Monoprop UAV propulsion stack.
October 2025 monthly summary for Avionics-Propulsion-Landers-GT/MonopropUAV: Focused on delivering Rust-based lossless convexification solver with Clarabel integration, expanding vehicle kinematics conditioning, and establishing thrust profiling data logging for the 1D rocket fuel simulation. These efforts enable faster, safer trajectory optimization and data-driven analysis for the Monoprop UAV propulsion stack.
Month: 2025-09 — Focused on delivering efficient UAV trajectory optimization and refining the lossless convexification workflow in MonopropUAV. Implemented a two-step trajectory optimization approach combining a rough minimum-time search with a high-fidelity refinement to improve accuracy and fuel efficiency, aligning with project goals of safer navigation and reduced mission costs. This work streamlined trajectory planning for UAVs with improved computational performance and robustness, enabling more reliable mission planning in constrained environments.
Month: 2025-09 — Focused on delivering efficient UAV trajectory optimization and refining the lossless convexification workflow in MonopropUAV. Implemented a two-step trajectory optimization approach combining a rough minimum-time search with a high-fidelity refinement to improve accuracy and fuel efficiency, aligning with project goals of safer navigation and reduced mission costs. This work streamlined trajectory planning for UAVs with improved computational performance and robustness, enabling more reliable mission planning in constrained environments.
August 2025 monthly summary for Avionics-Propulsion-Landers-GT/MonopropUAV focusing on delivering a robust, testable lossless convex landing workflow with improved visualization and solver accuracy, complemented by proactive testing groundwork.
August 2025 monthly summary for Avionics-Propulsion-Landers-GT/MonopropUAV focusing on delivering a robust, testable lossless convex landing workflow with improved visualization and solver accuracy, complemented by proactive testing groundwork.
July 2025: Focused on expanding UAV autonomy and data-driven validation for MonopropUAV. Delivered trajectory optimization enhancements with a lossless convex solver, introduced IMU-at-rest and Lidar data simulations with visualization, and improved project tooling for maintainability. Stabilized 1D fuel simulation in parallel with solver experiments to reduce risk during future optimizations. These efforts deliver measurable business value through improved fuel efficiency planning, safer landings, richer sensor data for validation, and more maintainable tooling.
July 2025: Focused on expanding UAV autonomy and data-driven validation for MonopropUAV. Delivered trajectory optimization enhancements with a lossless convex solver, introduced IMU-at-rest and Lidar data simulations with visualization, and improved project tooling for maintainability. Stabilized 1D fuel simulation in parallel with solver experiments to reduce risk during future optimizations. These efforts deliver measurable business value through improved fuel efficiency planning, safer landings, richer sensor data for validation, and more maintainable tooling.
June 2025 monthly summary for Avionics-Propulsion-Landers-GT/MonopropUAV: Delivered two core features advancing UAV landing optimization and fuel dynamics, established environmental setup for reproducible experiments, and prepared groundwork for further optimization research. No major bugs fixed this month; activity focused on feature delivery, baseline instrumentation, and alignment with research objectives. Impact includes improved trajectory optimization capability, better fuel-estimation workflows for mission planning, and increased visibility into system dynamics. Technologies demonstrated include Python-based optimization concepts, PID control, data visualization, and shell scripting for environment provisioning.
June 2025 monthly summary for Avionics-Propulsion-Landers-GT/MonopropUAV: Delivered two core features advancing UAV landing optimization and fuel dynamics, established environmental setup for reproducible experiments, and prepared groundwork for further optimization research. No major bugs fixed this month; activity focused on feature delivery, baseline instrumentation, and alignment with research objectives. Impact includes improved trajectory optimization capability, better fuel-estimation workflows for mission planning, and increased visibility into system dynamics. Technologies demonstrated include Python-based optimization concepts, PID control, data visualization, and shell scripting for environment provisioning.
Month: 2025-05 — The team delivered key sensor fusion improvements, timing reliability fixes, and propulsion/control enhancements for the MonopropUAV platform, together with foundational math support and improved data visualization. These changes increase reliability, responsiveness, and maintainability, enabling safer autopilot operation and accelerated development cycles.
Month: 2025-05 — The team delivered key sensor fusion improvements, timing reliability fixes, and propulsion/control enhancements for the MonopropUAV platform, together with foundational math support and improved data visualization. These changes increase reliability, responsiveness, and maintainability, enabling safer autopilot operation and accelerated development cycles.
April 2025 monthly summary for Avionics-Propulsion-Landers-GT/MonopropUAV focused on enhancing monocopter dynamics fidelity and improving code maintainability. Implemented a dynamic state update change to incorporate gimbal angular velocity, boosting physical accuracy of the monocopter simulation and aligning with hardware test expectations. Performed targeted cleanup in the dynamics module by removing legacy gyro force code to streamline the simulation pipeline.
April 2025 monthly summary for Avionics-Propulsion-Landers-GT/MonopropUAV focused on enhancing monocopter dynamics fidelity and improving code maintainability. Implemented a dynamic state update change to incorporate gimbal angular velocity, boosting physical accuracy of the monocopter simulation and aligning with hardware test expectations. Performed targeted cleanup in the dynamics module by removing legacy gyro force code to streamline the simulation pipeline.
March 2025 performance summary for Avionics-Propulsion-Landers-GT/MonopropUAV. Delivered foundational TVC integration into monocopter dynamics and implemented a Madgwick attitude estimation filter in C++, establishing a robust base for accurate attitude estimation and flight-control simulations. These efforts improved simulation fidelity, enabling earlier validation of control algorithms and reducing integration risk for flight hardware. Key commits demonstrate concrete progress across both work streams: TVC integration (983c1be4..., f8d83824..., 209651b9...) and Madgwick filter (db706d58..., 7bd7b59b..., 0a4028a0...). Minor fixes were applied to enhance stability and correctness.
March 2025 performance summary for Avionics-Propulsion-Landers-GT/MonopropUAV. Delivered foundational TVC integration into monocopter dynamics and implemented a Madgwick attitude estimation filter in C++, establishing a robust base for accurate attitude estimation and flight-control simulations. These efforts improved simulation fidelity, enabling earlier validation of control algorithms and reducing integration risk for flight hardware. Key commits demonstrate concrete progress across both work streams: TVC integration (983c1be4..., f8d83824..., 209651b9...) and Madgwick filter (db706d58..., 7bd7b59b..., 0a4028a0...). Minor fixes were applied to enhance stability and correctness.
February 2025 monthly summary for Avionics-Propulsion-Landers-GT/MonopropUAV focusing on delivering a robust attitude estimation enhancement. The month delivered a new dynamics model and EKF refinement, with updates to the EKF output data to improve accuracy and performance under real-world flight conditions. The changes were implemented in a single feature with a clear commit path, enabling reliable deployment in the MonopropUAV stack.
February 2025 monthly summary for Avionics-Propulsion-Landers-GT/MonopropUAV focusing on delivering a robust attitude estimation enhancement. The month delivered a new dynamics model and EKF refinement, with updates to the EKF output data to improve accuracy and performance under real-world flight conditions. The changes were implemented in a single feature with a clear commit path, enabling reliable deployment in the MonopropUAV stack.
January 2025 monthly summary for Avionics-Propulsion-Landers-GT/MonopropUAV. Delivered end-to-end synthetic data generation for attitude estimation testing, hardened GPS data conversion in the synthetic data pipeline, and a functional Extended Kalman Filter (EKF) for attitude estimation with testing and visualization. Implemented realistic sensor data with noise/offset, corrected geospatial conversions to account for Earth's curvature, and provided a reusable EKF framework with state transition, measurement prediction, quaternion utilities, rotation matrices, and a data-driven visualization runner. Established a reproducible testing workflow with synthetic fixtures and validation against ground-truth data.
January 2025 monthly summary for Avionics-Propulsion-Landers-GT/MonopropUAV. Delivered end-to-end synthetic data generation for attitude estimation testing, hardened GPS data conversion in the synthetic data pipeline, and a functional Extended Kalman Filter (EKF) for attitude estimation with testing and visualization. Implemented realistic sensor data with noise/offset, corrected geospatial conversions to account for Earth's curvature, and provided a reusable EKF framework with state transition, measurement prediction, quaternion utilities, rotation matrices, and a data-driven visualization runner. Established a reproducible testing workflow with synthetic fixtures and validation against ground-truth data.
December 2024 monthly recap for Avionics-Propulsion-Landers-GT/MonopropUAV. Focused on delivering debugging tooling for the Fouratis filter, expanding data-driven evaluation capabilities, and establishing reproducible validation assets that drive reliability and business value.
December 2024 monthly recap for Avionics-Propulsion-Landers-GT/MonopropUAV. Focused on delivering debugging tooling for the Fouratis filter, expanding data-driven evaluation capabilities, and establishing reproducible validation assets that drive reliability and business value.
2024-11 monthly summary for MonopropUAV: Delivered enhancements to the Attitude Estimation pipeline, improved data handling and filter initialization, and expanded test data + code organization to support robust validation and maintainability. These changes increased attitude accuracy, safety during flight tests, and reduced validation time, setting the stage for upcoming autonomy features.
2024-11 monthly summary for MonopropUAV: Delivered enhancements to the Attitude Estimation pipeline, improved data handling and filter initialization, and expanded test data + code organization to support robust validation and maintainability. These changes increased attitude accuracy, safety during flight tests, and reduced validation time, setting the stage for upcoming autonomy features.

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