FIR filters

[szeyoong-low]

Finite Impulse Response (FIR) filters for noise filtering

• Implemented a FIR filter (utama_core/run/refiners/filters.py)
• Wrote unit tests to verify that mean squared error is reduced across the board (utama_core/tests/refiners/noise_filter_test.py)
• Integrated filters into the position refiners (utama_core/run/refiners/position.py)
• Added utilities for exporting vision data and real time tracking of robot coordinates during live testing (utama_core/run/refiners/position.py) -> Moved to testing/data_processing
• Modified other unit tests affected by PositionRefiner taking more arguments necessary for the filters (utama_core/tests/refiners/position_unit_test.py and position_refiner_integration_test.py)
• Added a folder (vision_data) with various datasets generated from simulations for use in unit testing and running analytics
• Analysed the filters to optimise parameters and verify effectiveness in a Jupyter notebook (vision_data/filter_results_summary.ipynb) -> Moved to testing/data_processing
• Added multiple dependencies to Pixi: scipy, seaborn, pyqtgraph, pyside6 (pixi.toml)

Results achieved

When testing with grSim using the PID controller under random noise with standard deviation of 10 cm, we achieved a 45% improvement in robot positioning accuracy.

However, under the DWA controller, it creates random oscillations. We suspect it is due to the phase delay that FIR filters inherently caused. As such, we have since focused on developing a Kalman filter.

Note:

Kindly keep the additional unused parameters to the Position Refiner. It is required for Kalman filters, and removing it will require double work.

Completed by @szeyoong-low and @JiamingLiu5

[Copilot]

Pull request overview

This PR implements FIR (Finite Impulse Response) filtering for noise reduction in robot position tracking from vision data. The implementation includes a new filter module, comprehensive unit tests validating MSE reduction, and integration into the position refinement pipeline with support for both friendly and enemy robot tracking.

Key Changes:

• New FIR filter implementation with configurable parameters for position smoothing
• Unit tests demonstrating filter effectiveness across 6 robots using real simulation data
• Updated PositionRefiner API to accept team color and robot count parameters for filter initialization
• Added visualization and analysis dependencies (scipy, seaborn, pyqtgraph, pyside6)

Reviewed changes

Copilot reviewed 17 out of 24 changed files in this pull request and generated 11 comments.

Show a summary per file

File	Description
utama_core/run/refiners/filters.py	New FIR filter implementation with position and orientation filtering support
utama_core/tests/refiners/noise_filter_test.py	Comprehensive unit tests validating MSE reduction for x, y, and vector positions
utama_core/run/refiners/position.py	Integration of filters into position refiners (not shown in diff)
utama_core/tests/refiners/position_unit_test.py	Updated test signatures to match new PositionRefiner constructor
utama_core/tests/refiners/position_refiner_integration_test.py	Updated integration test with new PositionRefiner parameters
utama_core/run/strategy_runner.py	Updated PositionRefiner instantiation with team and robot count parameters
utama_core/entities/data/vision.py	Added Gaussian noise injection method for testing
pixi.toml	Added scipy, seaborn, pyqtgraph, and pyside6 dependencies
vision_data/*.csv	Test data files for filter validation
vision_data/.jupyter/*	Jupyter workspace configuration (should not be committed)
main.py	Changed mode from "rsim" to "grsim"

---

💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.

[Copilot]

Copilot encountered an error and was unable to review this pull request. You can try again by re-requesting a review.