LiU-SeeGoals/controller
Nov 2024 – Mar 2025
5 Months active
Languages Used
GoPython
Technical Skills
Backend DevelopmentControl SystemsEmbedded SystemsRoboticsSimulationAI
jakos322
PROFILE
Jakos322
Jakob Söderberg developed advanced robotics control and AI features for the LiU-SeeGoals/controller repository, focusing on modular, activity-based robot decision-making and robust simulation capabilities. He implemented asynchronous client-server communication, RRT-based path planning, and referee system integration to support real-world and competition scenarios. Using Go and Python, Jakob refined the AI architecture with slow and fast brain orchestration, improved goalie behavior modeling, and enhanced configuration management for seamless transitions between simulation and real operation. His work addressed concurrency, collision avoidance, and testing, resulting in a scalable, maintainable codebase that supports reliable multi-robot coordination and accelerates development of new robotic behaviors.
Overall Statistics
Feature vs Bugs
73%Features
Repository Contributions
36Total
Bugs
6
Commits
36
Features
16
Lines of code
6,506
Activity Months5
Your Network
24 people
Work History
March 2025
25 Commits • 11 Features
Mar 1, 2025March 2025 performance summary for LiU-SeeGoals/controller focused on delivering core competition-ready simulation capabilities, stabilizing operation under multi-robot scenarios, and strengthening testing real-world viability. Key features delivered include: integration of referee handling into the slow AI brain with configurable competition parameters (team size, action delays) and an improved slow-brain architecture; RRT-based path planning improvements for robust robot navigation; development of a Goaltender component to model goalie behavior in team play; and ongoing work to integrate Fetdator. Real-world scenario testing was set up and executed to validate behavior under realistic conditions, while scaffolding and code refactors (Importal scaffolding, Rasmus changes) prepared the project for sustained development. Kickoff rule enforcement and action visibility enhancements were implemented to align simulation with competition rules, and the referee system was enhanced with improved state machine, API accessors, and command handling. Notable bug fixes include resolving a deadlock in pathfinding, fixes for invisible robots, and miscellaneous stability improvements across the simulation. These fixes reduce edge-case failures and improve reliability during both development and testing. The combined efforts deliver measurable business value by enabling realistic, repeatable testing, reducing risk in deployment, and accelerating iteration cycles for future features. Technologies/skills demonstrated: RRT path planning, slow AI brain architecture, referee state machine and command handling, real-scenario testing, integration with Fetdator, and code scaffolding/refactoring.
25 Commits • 11 Features
Mar 1, 2025March 2025 performance summary for LiU-SeeGoals/controller focused on delivering core competition-ready simulation capabilities, stabilizing operation under multi-robot scenarios, and strengthening testing real-world viability. Key features delivered include: integration of referee handling into the slow AI brain with configurable competition parameters (team size, action delays) and an improved slow-brain architecture; RRT-based path planning improvements for robust robot navigation; development of a Goaltender component to model goalie behavior in team play; and ongoing work to integrate Fetdator. Real-world scenario testing was set up and executed to validate behavior under realistic conditions, while scaffolding and code refactors (Importal scaffolding, Rasmus changes) prepared the project for sustained development. Kickoff rule enforcement and action visibility enhancements were implemented to align simulation with competition rules, and the referee system was enhanced with improved state machine, API accessors, and command handling. Notable bug fixes include resolving a deadlock in pathfinding, fixes for invisible robots, and miscellaneous stability improvements across the simulation. These fixes reduce edge-case failures and improve reliability during both development and testing. The combined efforts deliver measurable business value by enabling realistic, repeatable testing, reducing risk in deployment, and accelerating iteration cycles for future features. Technologies/skills demonstrated: RRT path planning, slow AI brain architecture, referee state machine and command handling, real-scenario testing, integration with Fetdator, and code scaffolding/refactoring.
March 2025
February 2025
5 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for LiU-SeeGoals/controller: - Key features delivered: AI Activity Framework Refinement and Performance Optimization, and Referee System Integration with AI Fast Brain. - The AI work introduced new Goalie and MoveToBall structures, refined goalie state management, enhanced AI composition and distance calculations, and reduced the AI sleep interval to improve decision latency. A foundational referee handling component (HandleReferee) was added and integrated into FastBrainGO to process referee commands and generate actions. - Stability and progress: code compiles and supports movement to positions; ongoing goalie improvements with activities aligned to the new structure. - Overall impact: faster, more reliable AI decision loops, improved extensibility for future features, and groundwork for accommodating referee-driven match control. - Business value and technical accomplishments: reduced AI latency, better AI behavior modeling, and a scalable architecture supporting future enhancements.
February 2025
5 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for LiU-SeeGoals/controller: - Key features delivered: AI Activity Framework Refinement and Performance Optimization, and Referee System Integration with AI Fast Brain. - The AI work introduced new Goalie and MoveToBall structures, refined goalie state management, enhanced AI composition and distance calculations, and reduced the AI sleep interval to improve decision latency. A foundational referee handling component (HandleReferee) was added and integrated into FastBrainGO to process referee commands and generate actions. - Stability and progress: code compiles and supports movement to positions; ongoing goalie improvements with activities aligned to the new structure. - Overall impact: faster, more reliable AI decision loops, improved extensibility for future features, and groundwork for accommodating referee-driven match control. - Business value and technical accomplishments: reduced AI latency, better AI behavior modeling, and a scalable architecture supporting future enhancements.
January 2025
2 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for LiU-SeeGoals/controller: Delivered the Robot AI Activity-Based Control Framework, introducing an activity-based decision-making system with interfaces and concrete implementations for activities (e.g., MoveTo), and integrating updates to slow/fast brains to manage and execute activities, enabling modular and extensible robot control. Key refactor of the proposed solution and debugging efforts improved reliability (commits include b17f0be10dd25174043849a6afdd09322c2cb099). Moreover, debugging stabilized the simulation control path for MoveTo, addressing issues in translateSim to ensure the simulator responds correctly (commit a909c4ab8f549581d6fb6846bfd3e0bce005e32b). Major bugs fixed include the MoveTo simulation control path, leading to reliable, testable behavior in simulated environments. Overall impact: a scalable foundation for adding new activities, reduced maintenance burden, and faster iteration cycles for robot behaviors, with clear business value in more adaptable automation and safer, more predictable testing. Technologies/skills demonstrated: activity-based architecture, interface-driven design, modular control, brain state orchestration (slow/fast brains), debugging and refactoring, and simulation control.
2 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for LiU-SeeGoals/controller: Delivered the Robot AI Activity-Based Control Framework, introducing an activity-based decision-making system with interfaces and concrete implementations for activities (e.g., MoveTo), and integrating updates to slow/fast brains to manage and execute activities, enabling modular and extensible robot control. Key refactor of the proposed solution and debugging efforts improved reliability (commits include b17f0be10dd25174043849a6afdd09322c2cb099). Moreover, debugging stabilized the simulation control path for MoveTo, addressing issues in translateSim to ensure the simulator responds correctly (commit a909c4ab8f549581d6fb6846bfd3e0bce005e32b). Major bugs fixed include the MoveTo simulation control path, leading to reliable, testable behavior in simulated environments. Overall impact: a scalable foundation for adding new activities, reduced maintenance burden, and faster iteration cycles for robot behaviors, with clear business value in more adaptable automation and safer, more predictable testing. Technologies/skills demonstrated: activity-based architecture, interface-driven design, modular control, brain state orchestration (slow/fast brains), debugging and refactoring, and simulation control.
January 2025
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for LiU-SeeGoals/controller: Implemented real-world robot operation support with separate network configuration for simulation and real operation (ConfigReal, Port_real) and introduced RealTest scenario to validate real-world behavior. Port naming was refined to align with real-operation configuration, enhancing clarity and maintainability. No major bugs reported this month. Impact includes accelerated field readiness through improved testing coverage and streamlined configuration management. Technologies demonstrated include robotics networking, test automation, and configuration management in a production-grade controller.
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for LiU-SeeGoals/controller: Implemented real-world robot operation support with separate network configuration for simulation and real operation (ConfigReal, Port_real) and introduced RealTest scenario to validate real-world behavior. Port naming was refined to align with real-operation configuration, enhancing clarity and maintainability. No major bugs reported this month. Impact includes accelerated field readiness through improved testing coverage and streamlined configuration management. Technologies demonstrated include robotics networking, test automation, and configuration management in a production-grade controller.
November 2024
2 Commits • 1 Features
Nov 1, 2024Month: 2024-11 summary for LiU-SeeGoals/controller. Focused on delivering MoveTo action enhancements and asynchronous SimClient updates to enable team-aware movement and continuous updates. No major bugs fixed this period. Overall impact: improved multi-robot coordination, more reliable real-time position updates, and a solid foundation for scalable robotic workflows. Technologies/skills demonstrated include controller refactoring, asynchronous messaging, simulation translation layer, and basic proportional control for MoveTo commands.
2 Commits • 1 Features
Nov 1, 2024Month: 2024-11 summary for LiU-SeeGoals/controller. Focused on delivering MoveTo action enhancements and asynchronous SimClient updates to enable team-aware movement and continuous updates. No major bugs fixed this period. Overall impact: improved multi-robot coordination, more reliable real-time position updates, and a solid foundation for scalable robotic workflows. Technologies/skills demonstrated include controller refactoring, asynchronous messaging, simulation translation layer, and basic proportional control for MoveTo commands.
November 2024
Activity
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Quality Metrics
Correctness79.2%
Maintainability78.8%
Architecture76.2%
Performance68.4%
AI Usage27.8%
Skills & Technologies
Programming Languages
GoPython
Technical Skills
AIAI DevelopmentAI IntegrationAlgorithm ImplementationBackend DevelopmentClient-Server CommunicationCollision AvoidanceConcurrencyConfiguration ManagementControl SystemsEmbedded SystemsGame AIGame DevelopmentGame LogicGame State Management
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