purdue-arc/sphero-swarm
Oct 2025 – Apr 2026
4 Months active
Languages Used
PythonCSSJSONJavaScriptTypeScript
Technical Skills
Argument ParsingComputer VisionDeep LearningDepthAINetwork CommunicationObject Detection
Jack-Crowley
PROFILE
Jack-crowley
Worked on the purdue-arc/sphero-swarm repository, delivering real-time camera integration and object tracking using YOLOv8 and DepthAI to support robotics perception pipelines. Enhanced the system’s reliability by fixing thread synchronization issues and reorganizing code for maintainability. Developed flexible input handling with argument parsing and JSON-based communication, enabling seamless data ingestion from webcams and video files. Improved Sphero tracking accuracy and swarm coordination through grid-based arena visualization, latency instrumentation, and simulation controls. Leveraged Python, TypeScript, and React to expand the project’s scalability, user interface, and backend logic, supporting larger swarm experiments and accelerating onboarding with comprehensive documentation and testing workflows.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
21Total
Bugs
2
Commits
21
Features
10
Lines of code
68,834
Activity Months4
Your Network
19 peopleShared Repositories
19
Work History
April 2026
8 Commits • 4 Features
Apr 1, 2026April 2026 (2026-04) monthly summary for purdue-arc/sphero-swarm. Delivered notable improvements across rotation stability, user interface, simulation controls, and scaling of swarm experiments, while strengthening dependencies and control logic to support larger deployments and faster iteration cycles. The work emphasizes business value through more reliable operations, greater configurability, and enhanced performance.
8 Commits • 4 Features
Apr 1, 2026April 2026 (2026-04) monthly summary for purdue-arc/sphero-swarm. Delivered notable improvements across rotation stability, user interface, simulation controls, and scaling of swarm experiments, while strengthening dependencies and control logic to support larger deployments and faster iteration cycles. The work emphasizes business value through more reliable operations, greater configurability, and enhanced performance.
April 2026
March 2026
1 Commits • 1 Features
Mar 1, 2026Monthly summary for 2026-03 focusing on the Sphero Swarm project integration and enhancements. The primary activity this month was merging upstream updates into the feature branch to improve simulation and control capabilities for Sphero robots. No major bug fixes were recorded; the emphasis was on integration, alignment with main, and preparing the codebase for rapid continued development in the next sprint.
March 2026
1 Commits • 1 Features
Mar 1, 2026Monthly summary for 2026-03 focusing on the Sphero Swarm project integration and enhancements. The primary activity this month was merging upstream updates into the feature branch to improve simulation and control capabilities for Sphero robots. No major bug fixes were recorded; the emphasis was on integration, alignment with main, and preparing the codebase for rapid continued development in the next sprint.
November 2025
9 Commits • 3 Features
Nov 1, 2025November 2025 (purdue-arc/sphero-swarm) delivered core reliability improvements for Sphero tracking and substantial enhancements to arena visualization, performance instrumentation, and documentation. The changes enable safer testing, faster debugging, and clearer onboarding for new contributors. Key features delivered: - Sphero Arena Grid and Mapping Enhancements: Added grid visualization for the arena, grid overlay in Spotter, and coordinate mapping between pixel space and the arena grid; included code cleanup. (Commits: 8b6900cf7550aa4b7b001d78a4dcc24c15b60214; 2f8c4ac955a9788ebd3db352f0d2f9fcca6befd5; d038e25ce84d724efd27a8cb519bab2bfe6b4f1f; 6c7ecbc9292d872490b5a0e22c3d03401cc677e8) - Sphero Spotter Performance Improvements and Simulation: Introduced latency measurement in Spotter and added a grid arena simulation for Sphero control. (Commits: 51e82621e6616cccdf0e22e3dceb4d9cc0f91e5f; 51d24ee0219d9f144fc62832a92c085f30d29f03) - Documentation Enhancements for Perceptions: Enhanced documentation detailing setup, usage, and file overviews for the Sphero detection/tracking system. (Commit: ffd7085f8365dc537ef16a324b627f9a9c7cc8fd) Major bugs fixed: - Sphero Tracking and Swarm Communication Reliability (Bug Fixes): Fixed tracking ID mapping in the YOLOv8 tracker and improved reliability of server-client communication for the Sphero swarm. (Commits: e7e4dcfa42efdefc79e5f7d87d5f38c9f17d46e8; a6f63a284749a2baea6ec25389a70b2e33175605) Overall impact and accomplishments: - Increased operational reliability of Sphero tracking and swarm coordination, reducing downtime and error-prone handoffs. - Improved observability and debuggability through latency metrics and grid-based arena visualization, enabling faster issue isolation and performance tuning. - Safer, more efficient development and testing cycles via a grid-based simulation environment and up-to-date documentation, accelerating onboarding and knowledge transfer. Technologies/skills demonstrated: - Computer vision integration (YOLOv8), real-time tracking, and robust server-client communication. - 2D grid visualization, coordinate mapping, and visualization overlays. - Latency instrumentation, grid-based simulation, and testing scaffolding. - Documentation best practices and contributor-ready READMEs.
9 Commits • 3 Features
Nov 1, 2025November 2025 (purdue-arc/sphero-swarm) delivered core reliability improvements for Sphero tracking and substantial enhancements to arena visualization, performance instrumentation, and documentation. The changes enable safer testing, faster debugging, and clearer onboarding for new contributors. Key features delivered: - Sphero Arena Grid and Mapping Enhancements: Added grid visualization for the arena, grid overlay in Spotter, and coordinate mapping between pixel space and the arena grid; included code cleanup. (Commits: 8b6900cf7550aa4b7b001d78a4dcc24c15b60214; 2f8c4ac955a9788ebd3db352f0d2f9fcca6befd5; d038e25ce84d724efd27a8cb519bab2bfe6b4f1f; 6c7ecbc9292d872490b5a0e22c3d03401cc677e8) - Sphero Spotter Performance Improvements and Simulation: Introduced latency measurement in Spotter and added a grid arena simulation for Sphero control. (Commits: 51e82621e6616cccdf0e22e3dceb4d9cc0f91e5f; 51d24ee0219d9f144fc62832a92c085f30d29f03) - Documentation Enhancements for Perceptions: Enhanced documentation detailing setup, usage, and file overviews for the Sphero detection/tracking system. (Commit: ffd7085f8365dc537ef16a324b627f9a9c7cc8fd) Major bugs fixed: - Sphero Tracking and Swarm Communication Reliability (Bug Fixes): Fixed tracking ID mapping in the YOLOv8 tracker and improved reliability of server-client communication for the Sphero swarm. (Commits: e7e4dcfa42efdefc79e5f7d87d5f38c9f17d46e8; a6f63a284749a2baea6ec25389a70b2e33175605) Overall impact and accomplishments: - Increased operational reliability of Sphero tracking and swarm coordination, reducing downtime and error-prone handoffs. - Improved observability and debuggability through latency metrics and grid-based arena visualization, enabling faster issue isolation and performance tuning. - Safer, more efficient development and testing cycles via a grid-based simulation environment and up-to-date documentation, accelerating onboarding and knowledge transfer. Technologies/skills demonstrated: - Computer vision integration (YOLOv8), real-time tracking, and robust server-client communication. - 2D grid visualization, coordinate mapping, and visualization overlays. - Latency instrumentation, grid-based simulation, and testing scaffolding. - Documentation best practices and contributor-ready READMEs.
November 2025
October 2025
3 Commits • 2 Features
Oct 1, 2025Month: 2025-10 | Repository: purdue-arc/sphero-swarm Concise monthly summary focusing on business value and technical achievements: - Key features delivered: - Real-time Camera Integration with YOLOv8 Object Detection and DepthAI-based Tracking, enabling live perception with optional freezing of initial object IDs for consistent tracking. This supports monitoring and labeling of objects in perception pipelines. - (Reference commit: 59a56600be14aa4ade7c6796708f47f0848ee8e7) - Major bugs fixed: - Sphero Spotter stability improvements: fixed thread synchronization by ensuring the main thread waits for the spawned thread to complete, and reorganized the SpheroCoordinate module path for clarity. (Reference commit: 922c5b3c68d7a5450bbc58541b9256099d08b571) - Input handling and usage enhancements: - Added argument parsing, established connections for Sphero Spotter, introduced new input stream classes for webcams and video files, refactored main logic to use streams and arguments, and updated the test client to communicate via JSON. This enables flexible data ingestion and easier testing. (Reference commit: b8cf2f2ee21d52e48b8576d812035ff7838d3f53) - Overall impact and accomplishments: - Improved real-time perception capabilities and reliability for perception stacks used in robotics demos and deployments. - Enhanced stability and maintainability through clearer code organization and synchronization fixes. - Expanded data ingestion options and testing workflow, accelerating integration with new data sources. - Technologies/skills demonstrated: - YOLOv8, DepthAI, real-time object detection and tracking - Thread synchronization and concurrency handling - Command-line argument parsing, input streams for camera/video, JSON-based communication - Code organization and test/client tooling improvements Business value: - Faster time-to-perception demonstrations with robust tracking. - Fewer runtime threading issues and clearer module structure reduce maintenance costs. - Flexible data ingestion enables broader testing and easier integration with external perception pipelines.
October 2025
3 Commits • 2 Features
Oct 1, 2025Month: 2025-10 | Repository: purdue-arc/sphero-swarm Concise monthly summary focusing on business value and technical achievements: - Key features delivered: - Real-time Camera Integration with YOLOv8 Object Detection and DepthAI-based Tracking, enabling live perception with optional freezing of initial object IDs for consistent tracking. This supports monitoring and labeling of objects in perception pipelines. - (Reference commit: 59a56600be14aa4ade7c6796708f47f0848ee8e7) - Major bugs fixed: - Sphero Spotter stability improvements: fixed thread synchronization by ensuring the main thread waits for the spawned thread to complete, and reorganized the SpheroCoordinate module path for clarity. (Reference commit: 922c5b3c68d7a5450bbc58541b9256099d08b571) - Input handling and usage enhancements: - Added argument parsing, established connections for Sphero Spotter, introduced new input stream classes for webcams and video files, refactored main logic to use streams and arguments, and updated the test client to communicate via JSON. This enables flexible data ingestion and easier testing. (Reference commit: b8cf2f2ee21d52e48b8576d812035ff7838d3f53) - Overall impact and accomplishments: - Improved real-time perception capabilities and reliability for perception stacks used in robotics demos and deployments. - Enhanced stability and maintainability through clearer code organization and synchronization fixes. - Expanded data ingestion options and testing workflow, accelerating integration with new data sources. - Technologies/skills demonstrated: - YOLOv8, DepthAI, real-time object detection and tracking - Thread synchronization and concurrency handling - Command-line argument parsing, input streams for camera/video, JSON-based communication - Code organization and test/client tooling improvements Business value: - Faster time-to-perception demonstrations with robust tracking. - Fewer runtime threading issues and clearer module structure reduce maintenance costs. - Flexible data ingestion enables broader testing and easier integration with external perception pipelines.
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Quality Metrics
Correctness83.8%
Maintainability81.0%
Architecture80.0%
Performance81.0%
AI Usage28.6%
Skills & Technologies
Programming Languages
CSSJSONJavaScriptPythonTypeScript
Technical Skills
Argument ParsingComputer VisionDeep LearningDepthAIFull Stack DevelopmentNetwork CommunicationObject DetectionObject TrackingPythonPython DevelopmentPython programmingPython scriptingReactReal-time SystemsTypeScript
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