Journal
IEEE ROBOTICS AND AUTOMATION LETTERS
Volume 4, Issue 4, Pages 3363-3370Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2019.2926677
Keywords
Model learning for control; learning and adaptive systems; model predictive control; autonomous racing
Categories
Funding
- Swiss National Science Foundation [PP00P2_157601/1]
- Swiss National Science Foundation (SNF) [PP00P2_157601] Funding Source: Swiss National Science Foundation (SNF)
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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 10% while maintaining safety of the vehicle.
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