Cornell-University-Combat-Robotics/Autonomous-24-25
Nov 2024 – May 2025
6 Months active
Languages Used
C++MarkdownPNGPythonBash
Technical Skills
Algorithm DevelopmentCamera CalibrationCamera IntegrationComputer VisionDocumentationImage Processing
grace
PROFILE
Grace
Grace Liu developed and enhanced autonomous perception and navigation systems for the Cornell-University-Combat-Robotics/Autonomous-24-25 repository, focusing on robust robot localization, orientation prediction, and real-time object detection. She implemented color-based corner detection and orientation estimation using OpenCV and Python, integrating machine learning models such as YOLO for live detection and match-phase decision-making. Her work included refactoring core algorithms for maintainability, improving UI workflows, and deploying models across diverse hardware environments. By introducing test-driven validation with arena-specific data and refining control systems, Grace improved reliability and scalability, enabling faster iteration and more consistent autonomous operation in competition-style robotics scenarios.
Overall Statistics
Feature vs Bugs
94%Features
Repository Contributions
34Total
Bugs
1
Commits
34
Features
15
Lines of code
6,043
Activity Months6
Your Network
134 people
Work History
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for Cornell-University-Combat-Robotics/Autonomous-24-25 focused on advancing robot perception and reliability in arena conditions. Delivered the Robot Corner Detection Enhancement, a refactor of the corner-detection logic that estimates orientation by comparing the pixel counts of the top and bottom colors, plus added warp and color picking to improve detection robustness. A new test video was introduced to validate performance in arena scenarios under realistic conditions. No major bugs fixed this month. This work directly improves autonomous navigation reliability and consistency in competition-style arenas, reducing misdetections and enabling more stable reactivity to arena geometry. Technologies demonstrated include color-based feature detection, pixel-count analysis, code refactoring for maintainability, and test-driven validation with scenario-specific video data. Commit reference: bf43a67cd71e6da0884f113fd0ac7ede0517e475.
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for Cornell-University-Combat-Robotics/Autonomous-24-25 focused on advancing robot perception and reliability in arena conditions. Delivered the Robot Corner Detection Enhancement, a refactor of the corner-detection logic that estimates orientation by comparing the pixel counts of the top and bottom colors, plus added warp and color picking to improve detection robustness. A new test video was introduced to validate performance in arena scenarios under realistic conditions. No major bugs fixed this month. This work directly improves autonomous navigation reliability and consistency in competition-style arenas, reducing misdetections and enabling more stable reactivity to arena geometry. Technologies demonstrated include color-based feature detection, pixel-count analysis, code refactoring for maintainability, and test-driven validation with scenario-specific video data. Commit reference: bf43a67cd71e6da0884f113fd0ac7ede0517e475.
May 2025
April 2025
9 Commits • 4 Features
Apr 1, 2025April 2025 performance summary for Cornell-University-Combat-Robotics/Autonomous-24-25. Delivered a suite of features to streamline operator workflow, enhance autonomy robustness, and enable broader model deployment across development and field hardware. Highlights include UI/warp improvements that reduce interaction friction, robustness fixes and corner-detection enhancements that stabilize navigation, deployment-ready ML assets, and environment-aware configurations to support multi-device operation. These changes reduce manual steps, improve reliability, and accelerate field-ready deployment while clarifying ownership of hardware-specific paths.
April 2025
9 Commits • 4 Features
Apr 1, 2025April 2025 performance summary for Cornell-University-Combat-Robotics/Autonomous-24-25. Delivered a suite of features to streamline operator workflow, enhance autonomy robustness, and enable broader model deployment across development and field hardware. Highlights include UI/warp improvements that reduce interaction friction, robustness fixes and corner-detection enhancements that stabilize navigation, deployment-ready ML assets, and environment-aware configurations to support multi-device operation. These changes reduce manual steps, improve reliability, and accelerate field-ready deployment while clarifying ownership of hardware-specific paths.
March 2025
5 Commits • 2 Features
Mar 1, 2025March 2025 highlights for Cornell-University-Combat-Robotics/Autonomous-24-25: Delivered core autonomous navigation and perception improvements, stabilized the perception pipeline with YOLO integration, and enhanced usability through vision UX enhancements and comprehensive documentation. The changes boosted navigation robustness, reduced integration friction, and accelerated field readiness for deployments.
5 Commits • 2 Features
Mar 1, 2025March 2025 highlights for Cornell-University-Combat-Robotics/Autonomous-24-25: Delivered core autonomous navigation and perception improvements, stabilized the perception pipeline with YOLO integration, and enhanced usability through vision UX enhancements and comprehensive documentation. The changes boosted navigation robustness, reduced integration friction, and accelerated field readiness for deployments.
March 2025
February 2025
5 Commits • 3 Features
Feb 1, 2025February 2025 monthly summary for Cornell-University-Combat-Robotics/Autonomous-24-25. Focused on delivering robust robot orientation prediction and visualization, integrating pre-saved homography for warping and motor control, and refining robotic agent detection and visualization. Debug cleanup improved reliability and readability.
February 2025
5 Commits • 3 Features
Feb 1, 2025February 2025 monthly summary for Cornell-University-Combat-Robotics/Autonomous-24-25. Focused on delivering robust robot orientation prediction and visualization, integrating pre-saved homography for warping and motor control, and refining robotic agent detection and visualization. Debug cleanup improved reliability and readability.
January 2025
7 Commits • 3 Features
Jan 1, 2025January 2025: Focused on elevating perception, navigation, and real-time decision-making to drive business value in autonomous robotics competition workflows. Delivered multi-robot corner detection with robust data handling, integrated live object detection into the match loop, and merged Ram Ram navigation for improved localization and enemy tracking. Strengthened data format compatibility and maintainability, enabling scalable improvements and faster iteration.
7 Commits • 3 Features
Jan 1, 2025January 2025: Focused on elevating perception, navigation, and real-time decision-making to drive business value in autonomous robotics competition workflows. Delivered multi-robot corner detection with robust data handling, integrated live object detection into the match loop, and merged Ram Ram navigation for improved localization and enemy tracking. Strengthened data format compatibility and maintainability, enabling scalable improvements and faster iteration.
January 2025
November 2024
7 Commits • 2 Features
Nov 1, 2024November 2024 monthly summary for Cornell-University-Combat-Robotics/Autonomous-24-25: Implemented core perception and calibration enhancements to advance autonomous localization and testing readiness. Delivered Robot Corner Detection and Orientation System with color-based front-corner detection, orientation calculation, and perspective warp; refactored into a class-based design, added manual color selection, test images, and documentation. Launched Camera Testing and Color Selection Tooling to support image-based feature detection and calibration, including ColorPicker utilities. These efforts increased robustness, reduced calibration time, and expanded the team's testing toolbox.
November 2024
7 Commits • 2 Features
Nov 1, 2024November 2024 monthly summary for Cornell-University-Combat-Robotics/Autonomous-24-25: Implemented core perception and calibration enhancements to advance autonomous localization and testing readiness. Delivered Robot Corner Detection and Orientation System with color-based front-corner detection, orientation calculation, and perspective warp; refactored into a class-based design, added manual color selection, test images, and documentation. Launched Camera Testing and Color Selection Tooling to support image-based feature detection and calibration, including ColorPicker utilities. These efforts increased robustness, reduced calibration time, and expanded the team's testing toolbox.
Activity
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Quality Metrics
Correctness83.2%
Maintainability83.2%
Architecture78.6%
Performance70.8%
AI Usage23.0%
Skills & Technologies
Programming Languages
BashC++MarkdownPNGPython
Technical Skills
Algorithm DevelopmentAlgorithm ImplementationBug FixingCamera CalibrationCamera IntegrationCode RefactoringColor DetectionComputer VisionControl SystemsData ProcessingDebuggingDocumentationEmbedded SystemsError HandlingFile I/O
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