Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2023
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2309.07262 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866917724044656640 |
|---|---|
| author | Fork, Thomas Borrelli, Francesco |
| author_facet | Fork, Thomas Borrelli, Francesco |
| contents | We present two quadrotor raceline optimization approaches which differ in using Euclidean or non-Euclidean geometry to describe vehicle position. Both approaches use high-fidelity quadrotor dynamics and avoid the need to approximate gates using waypoints. We demonstrate both approaches on simulated racetracks with realistic vehicle parameters where we demonstrate 100x faster compute time than comparable published methods and improved solver convergence. We then extend the non-Euclidean approach to compute racelines in the presence of numerous static obstacles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_07262 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Euclidean and non-Euclidean Trajectory Optimization Approaches for Quadrotor Racing Fork, Thomas Borrelli, Francesco Robotics Systems and Control We present two quadrotor raceline optimization approaches which differ in using Euclidean or non-Euclidean geometry to describe vehicle position. Both approaches use high-fidelity quadrotor dynamics and avoid the need to approximate gates using waypoints. We demonstrate both approaches on simulated racetracks with realistic vehicle parameters where we demonstrate 100x faster compute time than comparable published methods and improved solver convergence. We then extend the non-Euclidean approach to compute racelines in the presence of numerous static obstacles. |
| title | Euclidean and non-Euclidean Trajectory Optimization Approaches for Quadrotor Racing |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2309.07262 |