SFUnity/Training2025-SFUnity
Jan 2025 – Mar 2025
3 Months active
Languages Used
JSONJava
Technical Skills
Autonomous DrivingAutonomous NavigationAutonomous ProgrammingAutonomous RoutinesAutonomous SystemsBug Fix
Benson Liu
PROFILE
Benson Liu
Ben Liu developed and enhanced autonomous navigation features for the SFUnity/Training2025-SFUnity repository over a three-month period, focusing on reliability, configurability, and simulation fidelity. He implemented new autonomous routines and refined trajectory planning using Java, introducing algae-focused automation and improving path accuracy through trajectory regeneration. By restructuring control flow and state management, Ben increased the robustness of autonomous execution and reduced manual intervention. He also addressed simulation and elevator state bugs, ensuring accurate system behavior. His work demonstrated depth in robotics programming, command-based frameworks, and embedded systems, resulting in more repeatable autonomous missions and streamlined testing and configuration processes.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
31Total
Bugs
2
Commits
31
Features
8
Lines of code
10,724
Activity Months3
Your Network
7 people
Work History
March 2025
10 Commits • 3 Features
Mar 1, 2025March 2025 performance summary: Delivered a set of autonomous system enhancements for SFUnity/Training2025-SFUnity, including reliability-focused control-flow refactors, a new CenterCD algae handling routine, and trajectory improvements. Implemented state-check refactor (.done() to .active().negate()) to improve robustness, added CenterCDAlgaeCDEFL3 routine wired into Autos.java with supporting trajectories, and refined multiple trajectories including the new CToDealgify path to boost accuracy and repeatability. Fixed a critical elevator transition bug that could cause downward movement between L3 and dealgify, reducing risk during coral scoring. Technologies demonstrated include Java-based autos wiring, state-machine refactoring, and trajectory management with .traj files. Business impact: higher autonomy success rates, reduced manual intervention, and more reliable algae handling and scoring at CenterCD.
10 Commits • 3 Features
Mar 1, 2025March 2025 performance summary: Delivered a set of autonomous system enhancements for SFUnity/Training2025-SFUnity, including reliability-focused control-flow refactors, a new CenterCD algae handling routine, and trajectory improvements. Implemented state-check refactor (.done() to .active().negate()) to improve robustness, added CenterCDAlgaeCDEFL3 routine wired into Autos.java with supporting trajectories, and refined multiple trajectories including the new CToDealgify path to boost accuracy and repeatability. Fixed a critical elevator transition bug that could cause downward movement between L3 and dealgify, reducing risk during coral scoring. Technologies demonstrated include Java-based autos wiring, state-machine refactoring, and trajectory management with .traj files. Business impact: higher autonomy success rates, reduced manual intervention, and more reliable algae handling and scoring at CenterCD.
March 2025
February 2025
9 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for SFUnity/Training2025-SFUnity. Focused on delivering algae-focused automation improvements, trajectory integrity, and simulation accuracy to advance autonomous capabilities and operational reliability.
February 2025
9 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for SFUnity/Training2025-SFUnity. Focused on delivering algae-focused automation improvements, trajectory integrity, and simulation accuracy to advance autonomous capabilities and operational reliability.
January 2025
12 Commits • 3 Features
Jan 1, 2025January 2025 update for SFUnity/Training2025-SFUnity focused on increasing autonomous reliability and simplifying configuration by cleaning path definitions, expanding routines, and refining the auto-chooser. Delivered new auto paths and routines, stabilized execution by addressing broken commands, and streamlined the auto-selection process. These changes reduce manual tweaks, improve mission repeatability, and enable broader automated testing via algae-related automations.
12 Commits • 3 Features
Jan 1, 2025January 2025 update for SFUnity/Training2025-SFUnity focused on increasing autonomous reliability and simplifying configuration by cleaning path definitions, expanding routines, and refining the auto-chooser. Delivered new auto paths and routines, stabilized execution by addressing broken commands, and streamlined the auto-selection process. These changes reduce manual tweaks, improve mission repeatability, and enable broader automated testing via algae-related automations.
January 2025
Activity
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Quality Metrics
Correctness86.8%
Maintainability87.2%
Architecture84.6%
Performance86.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
JSONJava
Technical Skills
Autonomous DrivingAutonomous NavigationAutonomous ProgrammingAutonomous RoutinesAutonomous SystemsBug FixCode CleanupCode RefactoringCommand-Based FrameworkCommand-Based ProgrammingData ManagementEmbedded SystemsJavaJava DevelopmentPath Planning
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