Learning to Race in Extreme Turning Scene with Active Exploration and Gaussian Process Regression-based MPC
<|reference_start|>Learning to Race in Extreme Turning Scene with Active Exploration and Gaussian Process Regression-based MPC: Extreme cornering in racing often induces large side-slip angles, presenting a formidable challenge in vehicle control. To tackle this issue, this paper introduces an Active Exploration with Double GPR (AEDGPR) system. The system initiates by planning a minimum-time trajectory with a Gaussian Process Regression(GPR) compensated model. The planning results show that in the cornering section, the yaw angular velocity and side-slip angle are in opposite directions, indicating that the vehicle is drifting. In response, we develop a drift controller based on Model Predictive Control (MPC) and incorporate Gaussian Process Regression to correct discrepancies in the vehicle dynamics model. Moreover, the covariance from the GPR is employed to actively explore various cornering states, aiming to minimize trajectory tracking errors. The proposed algorithm is validated through simulations on the Simulink-Carsim platform and experiments using a 1/10 scale RC vehicle.<|reference_end|>
@article{wu2024learning,
    title={Learning to Race in Extreme Turning Scene with Active Exploration and
  Gaussian Process Regression-based MPC},
    author={Guoqiang Wu, Cheng Hu, Wangjia Weng, Zhouheng Li, Yonghao Fu, Lei Xie
  and Hongye Su},
    journal={arXiv preprint arXiv:2410.05740},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05740},
    primaryClass={cs.RO cs.AI cs.SY eess.SY}
}