Cornell-University-Combat-Robotics/Autonomous-24-25
Nov 2024 – May 2025
6 Months active
Languages Used
CSVPythonNumpyArduino C++C++
Technical Skills
Algorithm DevelopmentAlgorithm ImplementationCode DocumentationComputer VisionData LoggingDebugging
Ananya-Jajodia
PROFILE
Ananya-jajodia
Nya Jaj developed and maintained autonomous robotics software for the Cornell-University-Combat-Robotics/Autonomous-24-25 repository, focusing on robust motor control, simulation, and navigation algorithms. Over six months, Nya designed a modular Ram class architecture, implemented Python-based motor control with dual-channel support, and integrated Pygame and Tkinter GUIs for simulation and user interaction. The work included refactoring for maintainability, expanding unit tests, and standardizing data formats to improve analytics and reliability. By leveraging Python, Arduino C++, and Numpy, Nya enabled safer hardware-in-the-loop validation, streamlined serial communication, and delivered features that improved both development iteration speed and autonomous system robustness.
Overall Statistics
Feature vs Bugs
86%Features
Repository Contributions
24Total
Bugs
2
Commits
24
Features
12
Lines of code
3,253
Activity Months6
Your Network
13 people
Work History
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 monthly highlights for the Cornell-University-Combat-Robotics/Autonomous-24-25 repo focused on enabling robust concurrent motor control and streamlined serial communication. Delivered dual-channel capability to update and control two channels simultaneously, reducing transmission overhead and enabling more sophisticated multi-motor choreography for autonomous tasks.
2 Commits • 1 Features
May 1, 2025May 2025 monthly highlights for the Cornell-University-Combat-Robotics/Autonomous-24-25 repo focused on enabling robust concurrent motor control and streamlined serial communication. Delivered dual-channel capability to update and control two channels simultaneously, reducing transmission overhead and enabling more sophisticated multi-motor choreography for autonomous tasks.
May 2025
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025, autonomous robotics project: Cornell-University-Combat-Robotics/Autonomous-24-25. Focused on codebase cleanliness and documentation to enable faster onboarding, safer maintenance, and more reliable experimentation.
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025, autonomous robotics project: Cornell-University-Combat-Robotics/Autonomous-24-25. Focused on codebase cleanliness and documentation to enable faster onboarding, safer maintenance, and more reliable experimentation.
February 2025
6 Commits • 3 Features
Feb 1, 2025February 2025 performance summary for Cornell-University-Combat-Robotics/Autonomous-24-25 focusing on delivering core control capabilities, expanding hardware interoperability, and improving data consistency to enable reliable operation and easier analytics.
6 Commits • 3 Features
Feb 1, 2025February 2025 performance summary for Cornell-University-Combat-Robotics/Autonomous-24-25 focusing on delivering core control capabilities, expanding hardware interoperability, and improving data consistency to enable reliable operation and easier analytics.
February 2025
January 2025
3 Commits • 1 Features
Jan 1, 2025January 2025 highlights for Cornell-University-Combat-Robotics/Autonomous-24-25 focused on reliability, safety, and maintainability of RamRam navigation. Key features and robustness improvements were delivered, alongside targeted bug fixes that strengthen autonomous decision-making and reduce regression risk. The work enhances planning accuracy, safer maneuvers, and faster iteration through improved tests and clearer diagnostics.
January 2025
3 Commits • 1 Features
Jan 1, 2025January 2025 highlights for Cornell-University-Combat-Robotics/Autonomous-24-25 focused on reliability, safety, and maintainability of RamRam navigation. Key features and robustness improvements were delivered, alongside targeted bug fixes that strengthen autonomous decision-making and reduce regression risk. The work enhances planning accuracy, safer maneuvers, and faster iteration through improved tests and clearer diagnostics.
December 2024
3 Commits • 2 Features
Dec 1, 2024December 2024 — Summary focused on key accomplishments, major fixes, impact, and skills demonstrated for Cornell-University-Combat-Robotics/Autonomous-24-25. Implemented a Ram integration overhaul with a Pygame-based testing harness, refactored Ram to output motor commands as a dictionary, and removed outdated transmission code to improve maintainability and testability. These changes enable safer hardware-in-the-loop validation, faster iteration, and a clearer separation of concerns.
3 Commits • 2 Features
Dec 1, 2024December 2024 — Summary focused on key accomplishments, major fixes, impact, and skills demonstrated for Cornell-University-Combat-Robotics/Autonomous-24-25. Implemented a Ram integration overhaul with a Pygame-based testing harness, refactored Ram to output motor commands as a dictionary, and removed outdated transmission code to improve maintainability and testability. These changes enable safer hardware-in-the-loop validation, faster iteration, and a clearer separation of concerns.
December 2024
November 2024
9 Commits • 4 Features
Nov 1, 2024November 2024: Delivered foundational Ram class architecture with test-mode CSV logging and a Pygame-based simulation GUI; introduced a Tkinter-based point-selection UI for Ram navigation; resolved critical orientation issues and improved shutdown handling; enhanced testing workflows and data logging; and applied code quality improvements to increase maintainability and clarity. Business value includes improved simulation fidelity for feature validation, reliable telemetry for debugging, faster development iteration, and readiness for battle-mode validation. Technologies demonstrated include Python, Pygame, Tkinter, CSV logging, and type hints.
November 2024
9 Commits • 4 Features
Nov 1, 2024November 2024: Delivered foundational Ram class architecture with test-mode CSV logging and a Pygame-based simulation GUI; introduced a Tkinter-based point-selection UI for Ram navigation; resolved critical orientation issues and improved shutdown handling; enhanced testing workflows and data logging; and applied code quality improvements to increase maintainability and clarity. Business value includes improved simulation fidelity for feature validation, reliable telemetry for debugging, faster development iteration, and readiness for battle-mode validation. Technologies demonstrated include Python, Pygame, Tkinter, CSV logging, and type hints.
Activity
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Quality Metrics
Correctness82.6%
Maintainability83.4%
Architecture80.0%
Performance71.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
Arduino C++C++CSVNumpyPython
Technical Skills
Algorithm DevelopmentAlgorithm ImplementationAlgorithm TestingArduinoC++Code CleanupCode DocumentationCode RefactoringComputer VisionData LoggingDebuggingDocumentation UpdateEmbedded SystemsGUI DevelopmentGame Development
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