Multi-UAVs end-to-end Distributed Trajectory Generation over Point Cloud Data
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909232975052800 |
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| author | Marino, Antonio Pacchierotti, Claudio Giordano, Paolo Robuffo |
| author_facet | Marino, Antonio Pacchierotti, Claudio Giordano, Paolo Robuffo |
| contents | This paper introduces an end-to-end trajectory planning algorithm tailored for multi-UAV systems that generates collision-free trajectories in environments populated with both static and dynamic obstacles, leveraging point cloud data. Our approach consists of a 2-fork neural network fed with sensing and localization data, able to communicate intermediate learned features among the agents. One network branch crafts an initial collision-free trajectory estimate, while the other devises a neural collision constraint for subsequent optimization, ensuring trajectory continuity and adherence to physicalactuation limits. Extensive simulations in challenging cluttered environments, involving up to 25 robots and 25% obstacle density, show a collision avoidance success rate in the range of 100 -- 85%. Finally, we introduce a saliency map computation method acting on the point cloud data, offering qualitative insights into our methodology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_19742 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-UAVs end-to-end Distributed Trajectory Generation over Point Cloud Data Marino, Antonio Pacchierotti, Claudio Giordano, Paolo Robuffo Multiagent Systems This paper introduces an end-to-end trajectory planning algorithm tailored for multi-UAV systems that generates collision-free trajectories in environments populated with both static and dynamic obstacles, leveraging point cloud data. Our approach consists of a 2-fork neural network fed with sensing and localization data, able to communicate intermediate learned features among the agents. One network branch crafts an initial collision-free trajectory estimate, while the other devises a neural collision constraint for subsequent optimization, ensuring trajectory continuity and adherence to physicalactuation limits. Extensive simulations in challenging cluttered environments, involving up to 25 robots and 25% obstacle density, show a collision avoidance success rate in the range of 100 -- 85%. Finally, we introduce a saliency map computation method acting on the point cloud data, offering qualitative insights into our methodology. |
| title | Multi-UAVs end-to-end Distributed Trajectory Generation over Point Cloud Data |
| topic | Multiagent Systems |
| url | https://arxiv.org/abs/2406.19742 |