Reduce Lap Time for Autonomous Racing with Curvature-Integrated MPCC Local Trajectory Planning Method

要約

The widespread application of autonomous driving technology has significantly advanced the field of autonomous racing.
Model Predictive Contouring Control (MPCC) is a highly effective local trajectory planning method for autonomous racing.
ただし、従来のMPCCメソッドは、重大な曲率の変化がある競馬場と格闘しており、自律レース中の車両の性能を制限しています。
To address this issue, we propose a curvature-integrated MPCC (CiMPCC) local trajectory planning method for autonomous racing.
This method optimizes the velocity of the local trajectory based on the curvature of the racetrack centerline.
特定の実装には、RaceTrack中心線の曲率を参照速度プロファイルにマッピングすることが含まれます。これは、ローカル軌道の速度を最適化するためにコスト関数に組み込まれます。
この参照速度プロファイルは、Racetrack Centerlineの曲率を正規化およびマッピングすることにより作成され、それにより、有意な曲率を持つRaceTrackでの効率的かつ性能指向のローカル軌道計画を確保します。
The proposed CiMPCC method has been experimented on a self-built 1:10 scale F1TENTH racing vehicle deployed with ROS platform.
実験結果は、提案された方法が、鋭い曲率を備えた挑戦的な競馬場で優れた結果を達成し、他の自律的なレース軌道計画方法と比較して、全体のラップ時間を11.4％-12.5％改善することを示しています。
Our code is available at https://github.com/zhouhengli/CiMPCC.

要約(オリジナル)

The widespread application of autonomous driving technology has significantly advanced the field of autonomous racing. Model Predictive Contouring Control (MPCC) is a highly effective local trajectory planning method for autonomous racing. However, the traditional MPCC method struggles with racetracks that have significant curvature changes, limiting the performance of the vehicle during autonomous racing. To address this issue, we propose a curvature-integrated MPCC (CiMPCC) local trajectory planning method for autonomous racing. This method optimizes the velocity of the local trajectory based on the curvature of the racetrack centerline. The specific implementation involves mapping the curvature of the racetrack centerline to a reference velocity profile, which is then incorporated into the cost function for optimizing the velocity of the local trajectory. This reference velocity profile is created by normalizing and mapping the curvature of the racetrack centerline, thereby ensuring efficient and performance-oriented local trajectory planning in racetracks with significant curvature. The proposed CiMPCC method has been experimented on a self-built 1:10 scale F1TENTH racing vehicle deployed with ROS platform. The experimental results demonstrate that the proposed method achieves outstanding results on a challenging racetrack with sharp curvature, improving the overall lap time by 11.4%-12.5% compared to other autonomous racing trajectory planning methods. Our code is available at https://github.com/zhouhengli/CiMPCC.

arxiv情報

提供元, 利用サービス

arxiv.jp, Google