Adaptive Lane Following with Auto-Trim Tuning
Project Resources
- Objective: Develop an Adaptive Controller for Duckiebots to autonomously calibrate wheel trim, ensuring stable lane following without manual tuning.
- Approach: Implement a Model-Reference Adaptive Control (MRAC) system that continuously adjusts trim based on real-time lane position feedback.
- Authors: Pietro Griffa, Simone Arreghini, Rohit Suri, Aleksandar Petrov, Jacopo Tani
Before and after:
Training:
Project highlights
Calibration of sensor and actuators is always important in setting up robot systems, especially in the context of autonomous operations. Manual tweaking of calibration parameters though is a nuisance, albeit necessary when every physical instance of the robots is slightly different from each other.
In this project, the authors developed a process to automatically calibrate the trim parameter in the Duckiebot, i.e., allowing it to go straight when an equal command to both wheel motors is provided.
Adaptive lane following in Duckietown: beyond manual odometry calibration
The objective of this project is to develop a process to autonomously calibrate the wheel trim parameter of Duckiebots, eliminating the need for manual tuning or improving upon it. Manual tuning of this parameter, as part of the odometry calibration procedure, is needed to account for the invevitable slight differences existing across different Duckiebots, due to manufacturing, assembly, handling difference, etc.
Creating an automatic trim calibration procedure enhances the Duckiebot’s lane following behavior, by continuously adjusting the wheel alignment based on real-time lane pose feedback. Duckiebots typically require manual calibration for the odometry, which introduces variability and reduces scalability in autonomous mobility experiments.
By implementing a Model-Reference Adaptive Control (MRAC) based approach, the project ensures consistent performance despite mechanical variations or external disturbances. This is desireable for large-scale Duckietown deployments where the robots need to maintain uniform behavior across different assemblies.
Adaptive control reduces dependence on predefined parameters, allowing Duckiebots to self-correct without external intervention. This enables more reproducible fleet-level performance, useful for research in autonomous navigation. This project supports experimentation in self-calibrating robotic systems through application of adaptive control research.
Model Reference Adaptive Control (MRAC) for adaptive lane following in Duckietown
The method employs a Model-Reference Adaptive Control (MRAC) framework that iteratively estimates the optimal trim value during lane following by processing lane pose feedback from the vision pipeline, and comparing expected and actual motion to compute a correction factor. An adaptation law updates the trim dynamically based on real-time error minimization.
Pose estimation relies on a vision-based lane filter, which introduces latency and noise, affecting convergence stability. The adaptive controller must maintain stability while ensuring convergence to an optimal trim value within a finite time window.
The performance of this approach is constrained by sensor inaccuracies, requiring threshold-based filtering to exclude unreliable pose data. The algorithm operates in real-world conditions where road surface variations, lighting changes, and mechanical wear affect performance. Synchronizing lane pose data with controller updates while minimizing computation delays is a key challenge, and ensuring that the adaptive controller does not introduce oscillations or instability in the control loop requires parameter tuning.
Adaptive lane following: full report
Check out the full report here.
Adaptive lane following in Duckietown: Authors
Pietro Griffa is currently working as a Systems and Estimation Engineer at Verity, Switzerland.
Simone Arreghini is currently pursuing his Ph. D. at IDSIA USI-SUPSI, Switzerland.
Rohit Suri was a mentor on this project and is currently working as a Senior Research Scientist at Venti Technologies, Singapore.
Aleksandar Petrov was a mentor on this project and is currently pursuing his Ph. D. at the University of Oxford, United Kingdom.
Jacopo Tani was a supervisor on this project and is currently the CEO at Duckietown.
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Duckietown is a modular, customizable, and state-of-the-art platform for creating and disseminating robotics and AI learning experiences.
Duckietown is designed to teach, learn, and do research: from exploring the fundamentals of computer science and automation to pushing the boundaries of knowledge.
These spotlight projects are shared to exemplify Duckietown’s value for hands-on learning in robotics and AI, enabling students to apply theoretical concepts to practical challenges in autonomous robotics, boosting competence and job prospects.