MissouriMRR/SUAS-2025
Oct 2024 – Nov 2024
2 Months active
Languages Used
Python
Technical Skills
Autonomous SystemsDrone ControlFlight Control SoftwareLoggingPythonAPI Integration
Connor Hallemann
PROFILE
Connor Hallemann
Worked on the MissouriMRR/SUAS-2025 repository to enhance autonomous drone flight reliability and maintainability by delivering features focused on return-to-launch logic, altitude validation, and observability. Refactored flight control software in Python to improve RTL distance checks and added user-facing feedback, ensuring drones navigate home before initiating return procedures. Improved safety by refining takeoff and landing altitude verification, introducing margin-of-error checks, and strengthening descent validation. Replaced print statements with structured logging for better diagnostics and centralized monitoring. Addressed code quality by removing unused imports, aligning with linting standards. Demonstrated skills in Python, embedded systems, and code refactoring for robotics applications.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
5Total
Bugs
1
Commits
5
Features
3
Lines of code
79
Activity Months2
Your Network
7 peopleShared Repositories
7
Work History
November 2024
3 Commits • 1 Features
Nov 1, 2024November 2024 monthly performance summary for MissouriMRR/SUAS-2025 focused on safety-critical altitude validation improvements, observability, and code quality. Delivered features enhancing takeoff/RTL altitude verification, improved logging for diagnostics, and code cleanup to remove a unused import, driving reliability and maintainability.
3 Commits • 1 Features
Nov 1, 2024November 2024 monthly performance summary for MissouriMRR/SUAS-2025 focused on safety-critical altitude validation improvements, observability, and code quality. Delivered features enhancing takeoff/RTL altitude verification, improved logging for diagnostics, and code cleanup to remove a unused import, driving reliability and maintainability.
November 2024
October 2024
2 Commits • 2 Features
Oct 1, 2024October 2024 monthly summary for MissouriMRR/SUAS-2025 focused on delivering reliability, observability, and maintainability improvements with clear business value. Key features delivered this month: 1) Return-to-Launch Reliability Enhancement: Refactored RTL distance check threshold and added user-facing feedback to ensure the drone navigates to its home location before initiating RTL, improving autonomous return reliability and mission success probability. Commit: 54c70196a7a95976a88affc8bd4434d8efe29ff0. 2) Observability Enhancement: Print-to-Logging in Drone State Machine: Replaced all print statements with logging calls to enable configurable, centralized output and easier troubleshooting. Commit: 73a7254247dd958a82fa54b7312b18e5b86b488d. Major bugs fixed: None documented for this month. Focus was on reliability improvements and improved telemetry/observability rather than defect resolution. Overall impact and accomplishments: These changes reduce operational risk during autonomous flights, shorten debugging cycles, and establish a maintainable foundation for monitoring. The RTL enhancement directly improves safety by ensuring home navigation precedes RTL, while the logging refactor improves post-deployment visibility and incident response. Technologies/skills demonstrated: Python-based flight control logic, threshold/refactor techniques, standard logging frameworks, observability improvements, and code quality practices (replacing print statements with structured logging).
October 2024
2 Commits • 2 Features
Oct 1, 2024October 2024 monthly summary for MissouriMRR/SUAS-2025 focused on delivering reliability, observability, and maintainability improvements with clear business value. Key features delivered this month: 1) Return-to-Launch Reliability Enhancement: Refactored RTL distance check threshold and added user-facing feedback to ensure the drone navigates to its home location before initiating RTL, improving autonomous return reliability and mission success probability. Commit: 54c70196a7a95976a88affc8bd4434d8efe29ff0. 2) Observability Enhancement: Print-to-Logging in Drone State Machine: Replaced all print statements with logging calls to enable configurable, centralized output and easier troubleshooting. Commit: 73a7254247dd958a82fa54b7312b18e5b86b488d. Major bugs fixed: None documented for this month. Focus was on reliability improvements and improved telemetry/observability rather than defect resolution. Overall impact and accomplishments: These changes reduce operational risk during autonomous flights, shorten debugging cycles, and establish a maintainable foundation for monitoring. The RTL enhancement directly improves safety by ensuring home navigation precedes RTL, while the logging refactor improves post-deployment visibility and incident response. Technologies/skills demonstrated: Python-based flight control logic, threshold/refactor techniques, standard logging frameworks, observability improvements, and code quality practices (replacing print statements with structured logging).
Activity
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Quality Metrics
Correctness88.0%
Maintainability88.0%
Architecture76.0%
Performance76.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
API IntegrationAutonomous SystemsCode RefactoringDrone ControlEmbedded SystemsFlight Control SoftwareFlight SystemsLoggingPythonRobotics
Repositories Contributed To
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