Multi-UAVs end-to-end Distributed Trajectory Generation over Point Cloud Data
<|reference_start|>Multi-UAVs end-to-end Distributed Trajectory Generation over Point Cloud Data: 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.<|reference_end|>
@article{marino2024multi-uavs,
    title={Multi-UAVs end-to-end Distributed Trajectory Generation over Point Cloud
  Data},
    author={Antonio Marino (RAINBOW), Claudio Pacchierotti (RAINBOW), Paolo
  Robuffo Giordano (RAINBOW)},
    journal={IEEE Robotics and Automation Letters, 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19742},
    primaryClass={cs.MA}
}