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Article Dans Une Revue IEEE Robotics and Automation Letters Année : 2024

Multi-UAVs end-to-end Distributed Trajectory Generation over Point Cloud Data

Résumé

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 physical actuation 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.
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Dates et versions

hal-04552877 , version 1 (19-04-2024)
hal-04552877 , version 2 (28-06-2024)

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  • HAL Id : hal-04552877 , version 2

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Antonio Marino, Claudio Pacchierotti, Paolo Robuffo Giordano. Multi-UAVs end-to-end Distributed Trajectory Generation over Point Cloud Data. IEEE Robotics and Automation Letters, 2024. ⟨hal-04552877v2⟩
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