purdue-arc/sphero-swarm
Sep 2025 – Feb 2026
5 Months active
Languages Used
PythonMarkdownunknown
Technical Skills
Computer VisionObject DetectionPython ScriptingScriptingVideo ProcessingAprilTag Detection
Quasi_Mot0
PROFILE
Quasi_mot0
Andrew Mollen developed a suite of real-time computer vision tools for the purdue-arc/sphero-swarm repository, focusing on perception, detection, and tracking for robotics applications. He implemented AprilTag and YOLO-based object detection pipelines using Python and OpenCV, enabling live webcam analysis, frame extraction, and dynamic visualization overlays. His work included asynchronous processing, algorithm optimization, and robust diagnostics to improve detection accuracy, reduce latency, and streamline data generation. Andrew also enhanced observability and UI stability, introduced real-time HSV color filtering with live tuning, and fixed visualization bugs, resulting in a more reliable, maintainable, and operator-friendly perception stack for swarm robotics experiments.
Overall Statistics
Feature vs Bugs
90%Features
Repository Contributions
19Total
Bugs
1
Commits
19
Features
9
Lines of code
707
Activity Months5
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Work History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for development efforts focused on real-time color-based tracking enhancements in the Purdue ARC Sphero swarm project. The work centers on enabling dynamic tuning of color filters with immediate visual feedback to accelerate calibration, validation, and deployment in robotic color-tracking tasks.
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for development efforts focused on real-time color-based tracking enhancements in the Purdue ARC Sphero swarm project. The work centers on enabling dynamic tuning of color filters with immediate visual feedback to accelerate calibration, validation, and deployment in robotic color-tracking tasks.
February 2026
December 2025
3 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for purdue-arc/sphero-swarm. Focused on enhancing observability, diagnostics, and UI stability to accelerate troubleshooting and reduce downtime. Delivered key capabilities in Sphero Spotter with improved visibility into detection/tracking and robust handling of idle frames. Achieved traceable commits for changes, enabling faster triage and maintainability.
December 2025
3 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for purdue-arc/sphero-swarm. Focused on enhancing observability, diagnostics, and UI stability to accelerate troubleshooting and reduce downtime. Delivered key capabilities in Sphero Spotter with improved visibility into detection/tracking and robust handling of idle frames. Achieved traceable commits for changes, enabling faster triage and maintainability.
November 2025
11 Commits • 5 Features
Nov 1, 2025November 2025 highlights for purdue-arc/sphero-swarm: Implemented major perception stack upgrades and visualization improvements enabling more accurate, faster detection and better operator telemetry. Delivered end-to-end improvements across April Tag detection, perception models, latency measurement, Top Down integration, and visualization to drive reliability and actionable insights for field deployments. Major bug fix to stabilize Spotter visualization, reducing noisy or misaligned outputs and improving operator confidence. Business value delivered includes higher detection accuracy, lower processing latency, and clearer runtime telemetry for informed decision-making.
11 Commits • 5 Features
Nov 1, 2025November 2025 highlights for purdue-arc/sphero-swarm: Implemented major perception stack upgrades and visualization improvements enabling more accurate, faster detection and better operator telemetry. Delivered end-to-end improvements across April Tag detection, perception models, latency measurement, Top Down integration, and visualization to drive reliability and actionable insights for field deployments. Major bug fix to stabilize Spotter visualization, reducing noisy or misaligned outputs and improving operator confidence. Business value delivered includes higher detection accuracy, lower processing latency, and clearer runtime telemetry for informed decision-making.
November 2025
October 2025
2 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for purdue-arc/sphero-swarm: Delivered real-time AprilTag detection via webcam with visualization overlays, enabling immediate tag localization and debugging. Enhanced visualization with multi-tag connection, precise outlines, and perspective-warped views for improved analytics. Commits 033cd5748fe155bcf4d5f9d5caf59af2027acaac (April Tag Stuff) and 6b99b58618e1ae9f297539e3df78a0a9f9b0e8ae (april stuff) documented. No major bugs fixed this month; the focus was on feature development, stabilization, and data capture readiness. Business value includes accelerated vision-assisted swarm control workflows, improved calibration/analysis data, and reduced debugging time. Technologies demonstrated include Python, OpenCV, real-time video processing, computer vision techniques (tag detection, drawing overlays, perspective transforms), and persistence of tag corner data.
October 2025
2 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for purdue-arc/sphero-swarm: Delivered real-time AprilTag detection via webcam with visualization overlays, enabling immediate tag localization and debugging. Enhanced visualization with multi-tag connection, precise outlines, and perspective-warped views for improved analytics. Commits 033cd5748fe155bcf4d5f9d5caf59af2027acaac (April Tag Stuff) and 6b99b58618e1ae9f297539e3df78a0a9f9b0e8ae (april stuff) documented. No major bugs fixed this month; the focus was on feature development, stabilization, and data capture readiness. Business value includes accelerated vision-assisted swarm control workflows, improved calibration/analysis data, and reduced debugging time. Technologies demonstrated include Python, OpenCV, real-time video processing, computer vision techniques (tag detection, drawing overlays, perspective transforms), and persistence of tag corner data.
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 — purdue-arc/sphero-swarm: Delivered a Video Splitting Utility and YOLO-based Object Detection Test Script to accelerate perception development and data generation. Enhancements include auto-creation of an annotated_test_frames folder, frame-skipping to save every Nth frame, and a final summary print of frames saved. These changes improve data quality, reduce storage and processing costs, and provide a repeatable testing workflow for live webcam scenarios. Key commits include 58cff9df0f227d7a5071ce4cc7c697e75e63951d (Adding the new yolo test and video splitter) and 14f5bba1c5a031d438712cda04b013c826f12b6a (vixed video splitter, automatically creates files called annotated_test_frames).
2 Commits • 1 Features
Sep 1, 2025September 2025 — purdue-arc/sphero-swarm: Delivered a Video Splitting Utility and YOLO-based Object Detection Test Script to accelerate perception development and data generation. Enhancements include auto-creation of an annotated_test_frames folder, frame-skipping to save every Nth frame, and a final summary print of frames saved. These changes improve data quality, reduce storage and processing costs, and provide a repeatable testing workflow for live webcam scenarios. Key commits include 58cff9df0f227d7a5071ce4cc7c697e75e63951d (Adding the new yolo test and video splitter) and 14f5bba1c5a031d438712cda04b013c826f12b6a (vixed video splitter, automatically creates files called annotated_test_frames).
September 2025
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Quality Metrics
Correctness88.4%
Maintainability86.2%
Architecture82.0%
Performance86.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPythonunknown
Technical Skills
AprilTag DetectionAprilTagsComputer VisionDebuggingObject DetectionOpenCVPythonPython ScriptingPython programmingPython scriptingScriptingSoftware DevelopmentVideo Processingalgorithm developmentalgorithm optimization
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