Cornell-University-Combat-Robotics/Autonomous-24-25
Jan 2025 – May 2025
3 Months active
Languages Used
C++GitPythonJavaScriptMarkdownText
Technical Skills
Code RefactoringComputer VisionData CollectionDebuggingFile ManagementGPU Computing
Aaron Harnish
PROFILE
Aaron Harnish
Over three months, contributed to the Cornell-University-Combat-Robotics/Autonomous-24-25 repository by developing a generalized, multi-backend YOLO model framework with GPU-accelerated inference and robust performance profiling. Integrated YOLO v12 for object detection and tracking, optimizing runtime by removing costly image-saving steps and introducing configurable visualization controls. Enhanced autonomous robotics capabilities by implementing a PID-controlled ramming algorithm and refactoring prediction logic for improved handling of bot positions and velocities. Maintained high code quality through systematic refactoring, documentation consolidation, and repository organization. Leveraged Python, PyTorch, and TensorRT to deliver scalable, maintainable solutions that improved model portability, runtime efficiency, and developer productivity.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
30Total
Bugs
0
Commits
30
Features
9
Lines of code
1,538
Activity Months3
Your Network
13 peopleWork History
May 2025
8 Commits • 2 Features
May 1, 20252025-05 monthly summary for Cornell-University-Combat-Robotics/Autonomous-24-25. Focused on delivering a high-value feature and strong maintenance work to improve autonomous performance, reliability, and developer productivity. No major bugs fixed this month; improvements were achieved through feature delivery and documentation/repo hygiene that enable safer operation and faster iteration.
8 Commits • 2 Features
May 1, 20252025-05 monthly summary for Cornell-University-Combat-Robotics/Autonomous-24-25. Focused on delivering a high-value feature and strong maintenance work to improve autonomous performance, reliability, and developer productivity. No major bugs fixed this month; improvements were achieved through feature delivery and documentation/repo hygiene that enable safer operation and faster iteration.
May 2025
March 2025
8 Commits • 3 Features
Mar 1, 2025March 2025 monthly summary for Cornell-University-Combat-Robotics/Autonomous-24-25 focusing on YOLO v12 integration, new ML assets, and performance/observability improvements. Highlights include delivering end-to-end YOLO v12 detection with tracking and visualization controls, adding v12 model binaries and updated demo paths to support testing, and optimizing runtime and observability by removing costly image saves and making angle display optional. These workstreams contributed to faster iteration, improved testing fidelity, and better runtime visibility, enabling more reliable, field-ready object tracking.
March 2025
8 Commits • 3 Features
Mar 1, 2025March 2025 monthly summary for Cornell-University-Combat-Robotics/Autonomous-24-25 focusing on YOLO v12 integration, new ML assets, and performance/observability improvements. Highlights include delivering end-to-end YOLO v12 detection with tracking and visualization controls, adding v12 model binaries and updated demo paths to support testing, and optimizing runtime and observability by removing costly image saves and making angle display optional. These workstreams contributed to faster iteration, improved testing fidelity, and better runtime visibility, enabling more reliable, field-ready object tracking.
January 2025
14 Commits • 4 Features
Jan 1, 2025January 2025 monthly summary for Cornell-University-Combat-Robotics/Autonomous-24-25. Delivered a generalized, multi-backend YOLO model framework with visualization, GPU-accelerated inference, and performance instrumentation, along with substantial codebase improvements to support scalable backend deployment and maintainability. This quarter’s work enhances model portability across TensorRT, ONNX, PyTorch, and OpenVINO, reduces prediction latency, and strengthens the foundation for future hardware-accelerated deployments.
14 Commits • 4 Features
Jan 1, 2025January 2025 monthly summary for Cornell-University-Combat-Robotics/Autonomous-24-25. Delivered a generalized, multi-backend YOLO model framework with visualization, GPU-accelerated inference, and performance instrumentation, along with substantial codebase improvements to support scalable backend deployment and maintainability. This quarter’s work enhances model portability across TensorRT, ONNX, PyTorch, and OpenVINO, reduces prediction latency, and strengthens the foundation for future hardware-accelerated deployments.
January 2025
Activity
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Quality Metrics
Correctness87.0%
Maintainability87.4%
Architecture84.0%
Performance83.4%
AI Usage22.0%
Skills & Technologies
Programming Languages
C++GitJavaScriptMarkdownPythonText
Technical Skills
Algorithm DevelopmentCode OrganizationCode RefactoringComputer VisionData CollectionDebuggingDocumentationEmbedded SystemsFile ManagementGPU ComputingImage ProcessingMachine LearningModel ConversionModel ManagementObject Detection
Repositories Contributed To
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