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.

翻译：本文探讨了人工智能（AI）技术在无人机群轨迹生成中的应用。研究主要解决两个挑战：准确预测无人机路径以及高效避免无人机间碰撞。首先，系统性地将多种激活函数应用于含单隐藏层的前馈神经网络（FFNN），相较于先前研究提高了路径预测精度。其次，我们提出新型激活函数AdaptoSwelliGauss，该函数融合Swish与Elliott激活函数的优势，并与经过缩放和平移的高斯分量无缝集成。Swish函数保证平滑过渡，Elliott函数捕捉轨迹突变，而缩放平移的高斯分量则增强抗噪能力。这种动态组合专为捕捉无人机轨迹预测的复杂性而设计，其预测精度显著优于所有现有激活函数。第三，我们提出一种新型集成碰撞检测、规避与分批（ICDAB）策略，该策略融合两种互补的无人机碰撞避免技术：改变无人机轨迹和调整起飞时间（亦称分批）。这种集成克服了两种方法的固有缺陷——既减少了轨迹调整次数（避免第一种方法中过度复杂的路径），又可采用更小的分批规模（缩短第二种方法中的总起飞时间）。