  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.
