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
Aaron Harnish developed a generalized, multi-backend YOLO model framework for the Cornell-University-Combat-Robotics/Autonomous-24-25 repository, focusing on scalable deployment and maintainability. He integrated GPU-accelerated inference using PyTorch and TensorRT, introduced performance profiling to reduce latency, and enhanced code organization through main-flow refactoring. Aaron delivered end-to-end YOLO v12 detection with tracking and visualization controls, optimized runtime by removing costly image-saving steps, and improved observability for debugging and tuning. He also enhanced autonomous robot performance by implementing a PID-controlled ramming algorithm and consolidated project documentation, leveraging Python, C++, and version control to support reliable, field-ready robotics development and faster iteration.
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 people
Work 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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