
Worked on the LiU-SeeGoals/controller repository to deliver AI-driven simulation and real-time visualization features for robotics applications. Over two months, implemented enhancements such as SlowBrainAo and KickTheBall to improve AI behavior and robot control, while integrating GRSim/ER-force for more realistic simulation. Developed a real-time GUI visualizer using Fyne and introduced raymarching-based goal detection from the robot’s perspective, leveraging Go and JavaScript for backend and interface development. Focused on code cleanup, refactoring, and debugging to stabilize the system, reduce noisy output, and ensure reliable performance, resulting in faster iteration and improved decision support for robotics research and development.
April 2025 monthly summary for LiU-SeeGoals/controller. Focused on delivering real-time visualization, robot perception enhancements, and reliability improvements that collectively boost decision support and system stability. Key outcomes include a real-time GUI visualization using Fyne with a none-backend option and a refactor to use a named plot window for better integration with raycasting, raymarching-based goal detection from the robot’s perspective, and stabilization of the Fayne backend startup by fixing a segmentation fault and removing unnecessary debug prints.
April 2025 monthly summary for LiU-SeeGoals/controller. Focused on delivering real-time visualization, robot perception enhancements, and reliability improvements that collectively boost decision support and system stability. Key outcomes include a real-time GUI visualization using Fyne with a none-backend option and a refactor to use a named plot window for better integration with raycasting, raymarching-based goal detection from the robot’s perspective, and stabilization of the Fayne backend startup by fixing a segmentation fault and removing unnecessary debug prints.
March 2025 monthly summary for LiU-SeeGoals/controller. Delivered key AI and simulation enhancements, stabilized the controller, and reduced noisy output. The work focused on delivering business value through more capable AI behavior, realistic simulation, and cleaner code, enabling faster iteration, improved play quality, and more reliable performance in test and production environments.
March 2025 monthly summary for LiU-SeeGoals/controller. Delivered key AI and simulation enhancements, stabilized the controller, and reduced noisy output. The work focused on delivering business value through more capable AI behavior, realistic simulation, and cleaner code, enabling faster iteration, improved play quality, and more reliable performance in test and production environments.

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