Delaunay三角分割に基づく自律レース向けリアクティブ制御
DTR: Delaunay Triangulation-based Racing for Scaled Autonomous Racing
LiDARからDelaunay三角分割と走路境界セグメンテーションで中心線を抽出し、FTGの罠を回避するリアクティブ制御器DTRを提案。ラップタイムを70%短縮し、地図依存手法に迫る性能を実現した。
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2. 先行研究と比べてどこがすごい？
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6. 次に読むべき論文は？
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著者: Luca Tognoni, Neil Reichlin, Edoardo Ghignone, Nicolas Baumann, Steven Marty, Liam Boyle, Michele Magno
分類: cs.RO
原文アブストラクト
Reactive controllers for autonomous racing avoid the computational overhead of full ee-Think-Act autonomy stacks by directly mapping sensor input to control actions, eliminating the need for localization and planning. A widely used reactive strategy is FTG, which identifies gaps in LiDAR range measurements and steers toward a chosen one. While effective on fully bounded circuits, FTG fails in scenarios with incomplete boundaries and is prone to driving into dead-ends, known as FTG-traps. This work presents DTR, a reactive controller that combines Delaunay triangulation, from raw LiDAR readings, with track boundary segmentation to extract a centerline while systematically avoiding FTG-traps. Compared to FTG, the proposed method achieves lap times that are 70\% faster and approaches the performance of map-dependent methods. With a latency of 8.95 ms and CPU usage of only 38.85\% on the robot's OBC, DTR is real-time capable and has been successfully deployed and evaluated in field experiments.
