FTC Roadrunner 1.0 Gain Tuning Tutorial
Road Runner Version 1.0 Tuning: Part Two
Overview of Previous Tuning
- The video is a follow-up on the tuning process for Road Runner version 1.0, focusing on manual feedback tuning for robot navigation.
- Key gains discussed include axial gain (forward/backward), heading gain (rotation), and lateral gain (side-to-side correction). These determine how well the robot corrects its path when misaligned.
Identifying Issues with Robot Navigation
- The robot performs well in straight lines but struggles with turns and curves, indicating a need for further tuning of the three gains.
- Observations show that the robot drifts to the right due to imperfect sensors and gyro readings, which can affect performance during longer autonomous runs. This drift should not be overly concerning for short tasks.
Adjusting Gains Through Disturbances
- The current settings are an axial gain of 5, heading gain of 1, and lateral gain of 1; adjustments will be made by introducing disturbances while the robot is in motion.
- Disturbances can be applied axially (forward/back), laterally (left/right), or rotationally to test how well the robot responds to corrections while driving. Safety precautions are emphasized during this testing phase.
Testing Lateral Gain Adjustments
- Initial tests involve pushing the robot laterally as it drives; results indicate slow correction responses from the robot under current settings. A proposed adjustment increases lateral gain from 1 to 3 to improve responsiveness.
- After increasing lateral gain, the robot shows improved correction speed when nudged off course, demonstrating more effective self-correction capabilities as seen in tracking graphs where blue and green lines align closely.
Further Tuning Insights
- Incremental adjustments are recommended; going beyond desired values helps identify upper and lower bounds for optimal performance settings during tuning sessions. The goal is achieving quick corrections without excessive overshooting or erratic behavior while following paths accurately over time.
Robot Navigation and Tuning Techniques
Understanding Robot Off-Center Adjustments
- The robot can detect when it is off-center and adjust its drive motors to maintain a straight line, but it may not keep the correct heading.
- Changing the heading gain to three allows the robot to attempt better self-correction when rotated, indicating its ability to adjust orientation.
Simulating Disturbances and Trajectories
- Introducing obstacles (e.g., blocks) in the field helps observe how the robot corrects itself under various conditions, showcasing its adaptive behavior.
- Adjusting parameters like axial gain, heading gain, and lateral gain is crucial for ensuring that the robot maintains direction while navigating different trajectories.
Importance of Physical Changes on Performance
- Any physical modifications to the robot (weight changes, gear ratio adjustments, or component replacements) necessitate retuning of gains to ensure optimal performance.
Final Considerations for Tuning Values