
Samantha Hajdukiewicz developed autonomy and perception features for the MissouriMRDT/Autonomy_Software repository, focusing on robust navigation, object detection, and developer experience. She enhanced the state machine for marker-based navigation, integrated YOLO-based object detection models, and streamlined the pipeline by standardizing object taxonomy and removing obsolete models. Using C++ and Python, Samantha improved simulation fidelity, optimized model performance, and refactored code for maintainability. She also expanded user interface elements with creative ASCII art and improved data validation. Her work reduced navigation complexity, increased detection reliability, and enabled safer autonomous operation, reflecting a deep understanding of robotics, machine learning, and software architecture.

January 2026 monthly summary for MissouriMRDT/Autonomy_Software: Delivered navigation simplification, perception robustness, and UI/detection enhancements that reduce complexity, improve reliability, and boost operator morale. Key outcomes include removing AvoidanceState to streamline the autonomy/navigation state machine; stabilizing perception by reverting camera offset changes and updating LiDAR/database paths, adding the BMP model and removing unused models; enhancing operator engagement with cowsay characters and raising object detection confidence to 70%. These changes reduce risk of navigation errors, improve recognition accuracy, and enable safer, more predictable autonomous operation in production environments.
January 2026 monthly summary for MissouriMRDT/Autonomy_Software: Delivered navigation simplification, perception robustness, and UI/detection enhancements that reduce complexity, improve reliability, and boost operator morale. Key outcomes include removing AvoidanceState to streamline the autonomy/navigation state machine; stabilizing perception by reverting camera offset changes and updating LiDAR/database paths, adding the BMP model and removing unused models; enhancing operator engagement with cowsay characters and raising object detection confidence to 70%. These changes reduce risk of navigation errors, improve recognition accuracy, and enable safer, more predictable autonomous operation in production environments.
December 2025 Monthly Summary for MissouriMRDT/Autonomy_Software. The team delivered key enhancements to perception and developer experience, focusing on delivering business value through accurate object detection, streamlined pipelines, and maintainable code. Features and improvements include Rock Pick support with standardized terminology and a new object detection model, along with code quality and dev-environment refinements that reduce maintenance effort and enable GPU-accelerated development.
December 2025 Monthly Summary for MissouriMRDT/Autonomy_Software. The team delivered key enhancements to perception and developer experience, focusing on delivering business value through accurate object detection, streamlined pipelines, and maintainable code. Features and improvements include Rock Pick support with standardized terminology and a new object detection model, along with code quality and dev-environment refinements that reduce maintenance effort and enable GPU-accelerated development.
For May 2025, delivered significant autonomy enhancements and data-validation fixes that improve reliability, safety, and developer readiness in MissouriMRDT/Autonomy_Software.
For May 2025, delivered significant autonomy enhancements and data-validation fixes that improve reliability, safety, and developer readiness in MissouriMRDT/Autonomy_Software.
April 2025 monthly summary highlighting autonomy software delivery and simulation improvements for MissouriMRDT. Focused on delivering robust marker-based navigation and improving depth perception fidelity in simulation to support reliable mission execution and planning.
April 2025 monthly summary highlighting autonomy software delivery and simulation improvements for MissouriMRDT. Focused on delivering robust marker-based navigation and improving depth perception fidelity in simulation to support reliable mission execution and planning.
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