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.
@article{arxiv.2406.19742,
title = {Multi-UAVs end-to-end Distributed Trajectory Generation over Point Cloud Data},
author = {Antonio Marino and Claudio Pacchierotti and Paolo Robuffo Giordano},
journal= {arXiv preprint arXiv:2406.19742},
year = {2024}
}