Learning-Based Model Predictive Control for Autonomous Racing
By Juraj Kabzan, Lukas Hewing, Alexander Liniger, Melanie Nicole Zeilinger. IEEE Robotics and Automation Letters, 2019
448 citations recorded by the source corpus.
In this letter, we present a learning-based control approach for autonomous racing with an application to the AMZ Driverless race car gotthard. One major issue in autonomous racing is that accurate vehicle models that cover the entire performance envelope of a race car are highly nonlinear, complex, and complicated to identify, rendering them impractical for control. To address this issue, we employ a relatively simple nominal vehicle model, which is improved based on measurement data and tools from machine learning.The resulting formulation is an online learning data-driven model predictive controller, which uses Gaussian processes regression to take residual model uncertainty into account and achieve safe driving behavior. To improve the vehicle model online, we select from a constant in-flow of data points according to a criterion reflecting the information gain, and maintain a small dictionary of 300 data points. The framework is tested on the full-size AMZ Driverless race car, where it is able to improve the vehicle model and reduce lap times by $ {\mathbf{10}{\%}}$ while maintaining safety of the vehicle.
Subfields: Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/77b3f96aeb3a74e7bc864695904d5f35c8dea1b9
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
- Elia Kaufmann
- Alexander Liniger
- Davide Scaramuzza
- Philip Föhn
- Guillem Torrente
- Lukas Hewing
- Melanie Nicole Zeilinger
- Kim Peter Wabersich
Citations in the corpus
- Data-Driven MPC for Quadrotors (2021, methodology): “For the propagation of the covariance, we refer to the formulation in [11], [12].”
- Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing (2022, methodology): “The research published with the Formula Student cars is spread completely over all topics in object detection [45]– [47], [151], [179], localization [10], [67], [116], [172], planning [55], [173], [193] and control [57], [93], [96], [123], [131], [132] with a focus on holistic software pipelines [39”
- Learning-Based Model Predictive Control: Toward Safe Learning in Control (2020, background): “…of GP-based MPC to robotic systems have been presented, e.g., for trajectory tracking with robotic manipulators (95), path following of off-road mobile robots (87, 96), autonomous racing (86, 90, 97), process control (98), and telescope systems in the context of periodic additive disturbances (99).”