DTR: Delaunay Triangulation-based Racing for Scaled Autonomous Racing
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866910975232311296 |
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| author | Tognoni, Luca Reichlin, Neil Ghignone, Edoardo Baumann, Nicolas Marty, Steven Boyle, Liam Magno, Michele |
| author_facet | Tognoni, Luca Reichlin, Neil Ghignone, Edoardo Baumann, Nicolas Marty, Steven Boyle, Liam Magno, Michele |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_24320 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DTR: Delaunay Triangulation-based Racing for Scaled Autonomous Racing Tognoni, Luca Reichlin, Neil Ghignone, Edoardo Baumann, Nicolas Marty, Steven Boyle, Liam Magno, Michele Robotics 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. |
| title | DTR: Delaunay Triangulation-based Racing for Scaled Autonomous Racing |
| topic | Robotics |
| url | https://arxiv.org/abs/2505.24320 |