  This paper explores the application of Artificial Intelligence (AI)
techniques for generating the trajectories of fleets of Unmanned Aerial
Vehicles (UAVs). The two main challenges addressed include accurately
predicting the paths of UAVs and efficiently avoiding collisions between them.
Firstly, the paper systematically applies a diverse set of activation functions
to a Feedforward Neural Network (FFNN) with a single hidden layer, which
enhances the accuracy of the predicted path compared to previous work.
  Secondly, we introduce a novel activation function, AdaptoSwelliGauss, which
is a sophisticated fusion of Swish and Elliott activations, seamlessly
integrated with a scaled and shifted Gaussian component. Swish facilitates
smooth transitions, Elliott captures abrupt trajectory changes, and the scaled
and shifted Gaussian enhances robustness against noise. This dynamic
combination is specifically designed to excel in capturing the complexities of
UAV trajectory prediction. This new activation function gives substantially
better accuracy than all existing activation functions.
  Thirdly, we propose a novel Integrated Collision Detection, Avoidance, and
Batching (ICDAB) strategy that merges two complementary UAV collision avoidance
techniques: changing UAV trajectories and altering their starting times, also
referred to as batching. This integration helps overcome the disadvantages of
both - reduction in the number of trajectory manipulations, which avoids overly
convoluted paths in the first technique, and smaller batch sizes, which reduce
overall takeoff time in the second.
