用于预测和优化无人机集群轨迹的AI算法
AI Algorithm for Predicting and Optimizing Trajectory of UAV Swarm
AI总结：
本文研究无人机集群轨迹预测与防撞问题，通过系统比较单隐藏层FFNN激活函数、提出AdaptoSwelliGauss及融合轨迹调整与分批起飞的ICDAB策略，提升预测精度并降低碰撞规避代价。
摘要：
本文探讨了人工智能（AI）技术在生成无人机（UAV）编队轨迹方面的应用。所解决的两个主要挑战包括准确预测无人机路径以及高效避免无人机之间的碰撞。首先，本文将多种激活函数系统地应用于具有单个隐藏层的前馈神经网络（FFNN），与以往工作相比提高了预测路径的准确性。其次，我们提出了一种新颖的激活函数 AdaptoSwelliGauss，它是 Swish 与 Elliott 激活函数的精细融合，并与经过缩放和平移的高斯分量无缝结合。Swish 有助于实现平滑过渡，Elliott 捕捉突发的轨迹变化，而经过缩放和平移的高斯分量增强了对噪声的鲁棒性。这种动态组合专门用于出色地捕捉无人机轨迹预测中的复杂性。这种新的激活函数在准确性方面显著优于所有现有激活函数。第三，我们提出了一种新颖的集成碰撞检测、规避与分批（ICDAB）策略，该策略融合了两种互补的无人机防撞技术：改变无人机轨迹和调整其起飞时间，后者也称为分批。这种集成有助于克服两者的缺点：减少轨迹操控次数，从而避免第一种技术中过于复杂的路径；以及减小分批规模，从而降低第二种技术中的总起飞时间。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
