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 gener-
ates 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 optimiza-
tion, 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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