モータースポーツ車両のための外乱を考慮した確率的安全保証付き最小時間計画戦略
Disturbance-aware minimum-time planning strategies for motorsport vehicles with probabilistic safety certificates
外乱による不確かさの成長とフィードバック制御を考慮し、確率的な安全保証を満たしつつラップタイムを最小化する軌道最適化フレームワークを提案した。
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著者: Martino Gulisano, Matteo Masoni, Marco Gabiccini, Massimo Guiggiani
分類: cs.RO
原文アブストラクト
This paper presents a disturbance-aware framework that embeds robustness into minimum-lap-time trajectory optimization for motorsport. Two formulations are introduced. (i) Open-loop, horizon-based covariance propagation uses worst-case uncertainty growth over a finite window to tighten tire-friction and track-limit constraints. (ii) Closed-loop, covariance-aware planning incorporates a time-varying LQR feedback law in the optimizer, providing a feedback-consistent estimate of disturbance attenuation and enabling sharper yet reliable constraint tightening. Both methods yield reference trajectories for human or artificial drivers: in autonomous applications the modelled controller can replicate the on-board implementation, while for human driving accuracy increases with the extent to which the driver can be approximated by the assumed time-varying LQR policy. Computational tests on a representative Barcelona-Catalunya sector show that both schemes meet the prescribed safety probability, yet the closed-loop variant incurs smaller lap-time penalties than the more conservative open-loop solution, while the nominal (non-robust) trajectory remains infeasible under the same uncertainties. By accounting for uncertainty growth and feedback action during planning, the proposed framework delivers trajectories that are both performance-optimal and probabilistically safe, advancing minimum-time optimization toward real-world deployment in high-performance motorsport and autonomous racing.
